From 8c9562c584672996dc5d01914305c3f2c48e96e6 Mon Sep 17 00:00:00 2001 From: kjsato Date: Tue, 8 Aug 2023 16:12:11 -0400 Subject: [PATCH 01/50] deleted unnecessary lines in tests/seir/test_seir.py::test_check_values() --- flepimop/gempyor_pkg/tests/seir/test_seir.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/flepimop/gempyor_pkg/tests/seir/test_seir.py b/flepimop/gempyor_pkg/tests/seir/test_seir.py index d173c785f..2127034ed 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_seir.py +++ b/flepimop/gempyor_pkg/tests/seir/test_seir.py @@ -51,8 +51,8 @@ def test_check_values(): seeding[0, 0] = 1 - if np.all(seeding == 0): - warnings.warn("provided seeding has only value 0", UserWarning) + #if np.all(seeding == 0): + # warnings.warn("provided seeding has only value 0", UserWarning) if np.all(s.mobility.data < 1): warnings.warn("highest mobility value is less than 1", UserWarning) From 83edd0a1a78520828c67cbd8f8da509b97ac3f05 Mon Sep 17 00:00:00 2001 From: kjsato Date: Tue, 8 Aug 2023 16:32:37 -0400 Subject: [PATCH 02/50] modified type(s.mobility) to scipy.sparse.csr_matrix in assert line because deprecated --- flepimop/gempyor_pkg/src/gempyor/seir.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flepimop/gempyor_pkg/src/gempyor/seir.py b/flepimop/gempyor_pkg/src/gempyor/seir.py index d01d360a9..44e1e6bf2 100644 --- a/flepimop/gempyor_pkg/src/gempyor/seir.py +++ b/flepimop/gempyor_pkg/src/gempyor/seir.py @@ -23,7 +23,7 @@ def steps_SEIR( seeding_data, seeding_amounts, ): - assert type(s.mobility) == scipy.sparse.csr.csr_matrix + assert type(s.mobility) == scipy.sparse.csr_matrix mobility_data = s.mobility.data mobility_data = mobility_data.astype("float64") assert type(s.compartments.compartments.shape[0]) == int From 0ea5a1a838d481abc3b35391ea089b92528383cb Mon Sep 17 00:00:00 2001 From: kjsato Date: Tue, 15 Aug 2023 16:38:24 -0400 Subject: [PATCH 03/50] create a testcode for gempyor/file_paths.py, and added comments on the target file --- .../gempyor_pkg/src/gempyor/file_paths.py | 2 + .../tests/utils/test_file_paths.py | 75 +++++++++++++++++++ 2 files changed, 77 insertions(+) create mode 100644 flepimop/gempyor_pkg/tests/utils/test_file_paths.py diff --git a/flepimop/gempyor_pkg/src/gempyor/file_paths.py b/flepimop/gempyor_pkg/src/gempyor/file_paths.py index f20f7048c..0760508e1 100644 --- a/flepimop/gempyor_pkg/src/gempyor/file_paths.py +++ b/flepimop/gempyor_pkg/src/gempyor/file_paths.py @@ -13,10 +13,12 @@ def create_file_name(run_id, prefix, index, ftype, extension, create_directory=T def create_file_name_without_extension(run_id, prefix, index, ftype, create_directory=True): if create_directory: os.makedirs(create_dir_name(run_id, prefix, ftype), exist_ok=True) +# hardcoded, target dir to be modified later return "model_output/%s/%s%09d.%s.%s" % (ftype, prefix, index, run_id, ftype) def run_id(): +# if multiplatforms, to be modified esp on Windows return datetime.datetime.strftime(datetime.datetime.now(), "%Y.%m.%d.%H:%M:%S.%Z") diff --git a/flepimop/gempyor_pkg/tests/utils/test_file_paths.py b/flepimop/gempyor_pkg/tests/utils/test_file_paths.py new file mode 100644 index 000000000..a460f14b1 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/utils/test_file_paths.py @@ -0,0 +1,75 @@ +import pytest +import datetime +import os +from mock import MagicMock + +from gempyor import file_paths + +FAKE_TIME = datetime.datetime(2023,8,9,16,00,0) + +@pytest.fixture(scope="module") +def mock_datetime_now(monkeypatch): + datetime_mock = MagicMock(wraps=datetime.datetime) + datetime_mock.now.return_value = FAKE_TIME + monkeypatch.setattr(datetime, "datetime", datetime_mock) + +@pytest.fixture(scope="module") +def test_datetime(mock_datetime_now): + assert datetime.datetime.now() == FAKE_TIME + +def test_run_id(): + run_id = file_paths.run_id() + assert run_id == datetime.datetime.strftime(datetime.datetime.now(), "%Y.%m.%d.%H:%M:%S.%Z") + +@pytest.fixture(scope="module") +def set_run_id(): + return lambda: file_path.run_id() + + +tmp_path = "/tmp" + +@pytest.mark.parametrize(('prefix','ftype'),[ + ('test0001','seed'), + ('test0002','seed'), + ('test0003','seed'), + ('test0004','seed'), + ('test0001','seed'), + ('test0002','seed'), + ('test0003','seed'), + ('test0004','seed'), +]) +def test_create_dir_name(set_run_id, prefix, ftype): + #run_id = set_run_id() + os.chdir(tmp_path) + os.path.exists(file_paths.create_dir_name(set_run_id, prefix, ftype)) + + +@pytest.mark.parametrize(('prefix','index','ftype','extension','create_directory'),[ + ('test0001','0','seed','csv', True), + ('test0002','0','seed','parquet', True), + ('test0003','0','seed','csv', False), + ('test0004','0','seed','parquet', False), + ('test0001','1','seed','csv', True), + ('test0002','1','seed','parquet', True), + ('test0003','1','seed','csv', False), + ('test0004','1','seed','parquet', False), +]) +def test_create_file_name(set_run_id, prefix, index, ftype, extension, create_directory): + os.chdir(tmp_path) + os.path.isfile(file_paths.create_file_name(set_run_id, prefix, int(index), ftype, extension, create_directory)) + + +@pytest.mark.parametrize(('prefix','index','ftype','create_directory'),[ + ('test0001','0','seed', True), + ('test0002','0','seed', True), + ('test0003','0','seed', False), + ('test0004','0','seed', False), + ('test0001','1','seed', True), + ('test0002','1','seed', True), + ('test0003','1','seed', False), + ('test0004','1','seed', False), +]) +def test_create_file_name_without_extension(set_run_id, prefix, index, ftype, create_directory): + os.chdir(tmp_path) + os.path.isfile(file_paths.create_file_name_without_extension(set_run_id, prefix, int(index), ftype, create_directory)) + From 49ebec36498dc9f3442cd5df8ad18995a8708377 Mon Sep 17 00:00:00 2001 From: kjsato Date: Wed, 16 Aug 2023 12:22:54 -0400 Subject: [PATCH 04/50] mofified test_file_paths.py to activate mock when datetime.datetime.now was called at run_id() --- .../tests/utils/test_file_paths.py | 21 +++++++++++-------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/flepimop/gempyor_pkg/tests/utils/test_file_paths.py b/flepimop/gempyor_pkg/tests/utils/test_file_paths.py index a460f14b1..da7bf282e 100644 --- a/flepimop/gempyor_pkg/tests/utils/test_file_paths.py +++ b/flepimop/gempyor_pkg/tests/utils/test_file_paths.py @@ -7,19 +7,24 @@ FAKE_TIME = datetime.datetime(2023,8,9,16,00,0) +''' @pytest.fixture(scope="module") def mock_datetime_now(monkeypatch): datetime_mock = MagicMock(wraps=datetime.datetime) datetime_mock.now.return_value = FAKE_TIME monkeypatch.setattr(datetime, "datetime", datetime_mock) - @pytest.fixture(scope="module") def test_datetime(mock_datetime_now): assert datetime.datetime.now() == FAKE_TIME +''' + +def test_run_id(monkeypatch): + datetime_mock = MagicMock(wraps=datetime.datetime) + datetime_mock.now.return_value = FAKE_TIME + monkeypatch.setattr(datetime, "datetime", datetime_mock) -def test_run_id(): run_id = file_paths.run_id() - assert run_id == datetime.datetime.strftime(datetime.datetime.now(), "%Y.%m.%d.%H:%M:%S.%Z") + assert run_id == datetime.datetime.strftime(FAKE_TIME, "%Y.%m.%d.%H:%M:%S.%Z") @pytest.fixture(scope="module") def set_run_id(): @@ -33,13 +38,12 @@ def set_run_id(): ('test0002','seed'), ('test0003','seed'), ('test0004','seed'), - ('test0001','seed'), - ('test0002','seed'), - ('test0003','seed'), - ('test0004','seed'), + ('test0005','hosp'), + ('test0006','hosp'), + ('test0007','hosp'), + ('test0008','hosp'), ]) def test_create_dir_name(set_run_id, prefix, ftype): - #run_id = set_run_id() os.chdir(tmp_path) os.path.exists(file_paths.create_dir_name(set_run_id, prefix, ftype)) @@ -72,4 +76,3 @@ def test_create_file_name(set_run_id, prefix, index, ftype, extension, create_di def test_create_file_name_without_extension(set_run_id, prefix, index, ftype, create_directory): os.chdir(tmp_path) os.path.isfile(file_paths.create_file_name_without_extension(set_run_id, prefix, int(index), ftype, create_directory)) - From 442106f467d40b354b209990ad20cec01a43eb77 Mon Sep 17 00:00:00 2001 From: Shaun Truelove Date: Mon, 28 Aug 2023 23:01:17 -0400 Subject: [PATCH 05/50] fix missing rates in config SEIR section --- flepimop/R_packages/config.writer/R/yaml_utils.R | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/flepimop/R_packages/config.writer/R/yaml_utils.R b/flepimop/R_packages/config.writer/R/yaml_utils.R index 41445d07d..fa95714ef 100644 --- a/flepimop/R_packages/config.writer/R/yaml_utils.R +++ b/flepimop/R_packages/config.writer/R/yaml_utils.R @@ -1976,15 +1976,19 @@ seir_chunk <- function(resume_modifier = NULL, " proportion_exponent: [\n", " [",rate_propexp_parts, ",\"1\",\"1\",\"1\"],\n", " [",rate_alpha_parts, ",\"1\",\"1\",\"1\"]]\n", - " rate: [\n", - paste0(sapply(X = na.omit(c(rate_seir_parts, rate_vacc_parts, rate_var_parts, rate_age_parts)), - function(x = X){ paste0(" ",x,",\n")}) ), - " ]\n"), + " rate: [", + ifelse(nchar(rate_seir_parts)<100, + paste(na.omit(c(rate_seir_parts, rate_vacc_parts, rate_var_parts, rate_age_parts)), collapse = ", "), + paste0("\n ", paste(na.omit(c(rate_seir_parts, rate_vacc_parts, rate_var_parts, rate_age_parts)), collapse = ",\n "))), + "]\n"), paste0( " proportional_to: [\"source\"]\n", " proportion_exponent: [[\"1\",\"1\",\"1\",\"1\"]]\n", - " rate: [", paste(na.omit(c(rate_seir_parts, rate_vacc_parts, rate_var_parts, rate_age_parts)), collapse = ", "), "]\n")), - # " rate: [", glue::glue_collapse(na.omit(c(rate_seir_parts, rate_vacc_parts, rate_var_parts, rate_age_parts)), collapse = ", "), "]\n")), + " rate: [", + ifelse(nchar(rate_seir_parts)<100, + paste(na.omit(c(rate_seir_parts, rate_vacc_parts, rate_var_parts, rate_age_parts)), collapse = ", "), + paste0("\n ", paste(na.omit(c(rate_seir_parts, rate_vacc_parts, rate_var_parts, rate_age_parts)), collapse = ",\n "))), + "]\n")), "\n") return(tmp) From d144544ea6603135ce8698f2d3a323954ce6fe27 Mon Sep 17 00:00:00 2001 From: Shaun Truelove Date: Mon, 28 Aug 2023 23:07:51 -0400 Subject: [PATCH 06/50] rearrange the API pull for delphi --- datasetup/build_covid_data.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/datasetup/build_covid_data.R b/datasetup/build_covid_data.R index 2aa339315..5cb71c415 100644 --- a/datasetup/build_covid_data.R +++ b/datasetup/build_covid_data.R @@ -49,12 +49,12 @@ source(file.path(opt$path, "datasetup/data_setup_source.R")) # SET DELPHI API KEY ------------------------------------------------------ if (any(grepl("nchs|hhs", opt$gt_data_source))){ - if (!is.null(opt$delphi_api_key)){ - cat(paste0("Using Environment variable for Delphi API key: ", opt$delphi_api_key)) - options(covidcast.auth = opt$delphi_api_key) - } else if (!is.null(config$inference$gt_api_key)){ + if (!is.null(config$inference$gt_api_key)){ cat(paste0("Using Config variable for Delphi API key: ", config$inference$gt_api_key)) options(covidcast.auth = config$inference$gt_api_key) + } else if (!is.null(opt$delphi_api_key)){ + cat(paste0("Using Environment variable for Delphi API key: ", opt$delphi_api_key)) + options(covidcast.auth = opt$delphi_api_key) } else { newkey <- readline(prompt = "Please enter your Delphi API key before proceeding:") #check From 7826ad03e98872ec083853190e363902cff4796b Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 31 Aug 2023 11:36:40 -0400 Subject: [PATCH 07/50] added test functions in test_setup.py, divided SpatialSetup part to test_SpatialSetup.py, modified src/setup.py to suit to the correspoinding tests, added NOTE comments on TBM --- flepimop/gempyor_pkg/src/gempyor/setup.py | 25 +- flepimop/gempyor_pkg/tests/seir/test_setup.py | 600 +++++++++++++++++- 2 files changed, 617 insertions(+), 8 deletions(-) diff --git a/flepimop/gempyor_pkg/src/gempyor/setup.py b/flepimop/gempyor_pkg/src/gempyor/setup.py index fbd8e2114..664c576a6 100644 --- a/flepimop/gempyor_pkg/src/gempyor/setup.py +++ b/flepimop/gempyor_pkg/src/gempyor/setup.py @@ -84,11 +84,17 @@ def __init__( # I'm not really sure if we should impose defaut or make setup really explicit and # have users pass - if seir_config is None and config["seir"].exists(): + #if seir_config is None and config["seir"].exists(): + if not seir_config and config["seir"].exists(): self.seir_config = config["seir"] + # added below to cope with the imcompleteness of config["seir"] + if not parameters_config and config["seir"]["parameters"].exists(): + self.parameters_config = config["seir"]["parameters"] # Set-up the integration method and the time step - if config["seir"].exists() and (seir_config or parameters_config): + #if config["seir"].exists() and (seir_config or parameters_config): + if (self.seir_config and self.parameters_config): + #if ((seir_config or self.seir_config) and parameters_config): # modified to handle the case of "T and (F or F)) -> F" if "integration" in self.seir_config.keys(): if "method" in self.seir_config["integration"].keys(): self.integration_method = self.seir_config["integration"]["method"].get() @@ -97,7 +103,7 @@ def __init__( if self.integration_method == "rk4": self.integration_method = "rk4.jit" if self.integration_method not in ["rk4.jit", "legacy"]: - raise ValueError(f"Unknow integration method {self.integration_method}.") + raise ValueError(f"Unknown integration method {self.integration_method}.") if "dt" in self.seir_config["integration"].keys() and self.dt is None: self.dt = float( eval(str(self.seir_config["integration"]["dt"].get())) @@ -122,6 +128,7 @@ def __init__( f"Should be either non-specified (default: 'v3'), or set to 'old' or 'v2'." ) elif config_version == "old" or config_version == "v2": + # NOTE: even behaved as old, "v2" seems by default in parameter.py raise ValueError( f"Configuration version 'old' and 'v2' are no longer supported by flepiMoP\n" f"Please use a 'v3' instead, or use the COVIDScenarioPipeline package. " @@ -148,7 +155,8 @@ def __init__( # 3. Outcomes self.npi_config_outcomes = None if self.outcomes_config: - if self.outcomes_config["interventions"]["settings"][self.outcome_scenario].exists(): + # if self.outcomes_config["interventions"]["settings"][self.outcome_scenario].exists(): + if self.outcomes_config["interventions"]["settings"][self.outcome_scenario].keys(): # type dict self.npi_config_outcomes = self.outcomes_config["interventions"]["settings"][self.outcome_scenario] # 4. Inputs and outputs @@ -161,9 +169,12 @@ def __init__( self.out_run_id = out_run_id if in_prefix is None: +# NOTE: hard-coded "model_output" +# NOTE: asymmetric with out_prefix in_prefix = f"model_output/{setup_name}/{in_run_id}/" self.in_prefix = in_prefix if out_prefix is None: +# NOTE: hard-coded "model_output" out_prefix = f"model_output/{setup_name}/{npi_scenario}/{out_run_id}/" self.out_prefix = out_prefix @@ -176,6 +187,8 @@ def __init__( ftypes.extend(["hosp", "hpar", "hnpi"]) for ftype in ftypes: datadir = file_paths.create_dir_name(self.out_run_id, self.out_prefix, ftype) +# NOTE: owing to file_paths.py dirname will be used as hard-coded one +# NOTE: owing to run_id form, %Y.%m.%d. is not good to be appled on Windows os.makedirs(datadir, exist_ok=True) if self.write_parquet and self.write_csv: @@ -184,6 +197,9 @@ def __init__( self.extension = "parquet" elif self.write_csv: self.extension = "csv" + else: + # there were cases in which self.extension was not set then: + self.extension = "parquet" # to avoid no self.extension in anytime def get_input_filename(self, ftype: str, sim_id: int, extension_override: str = ""): return self.get_filename( @@ -314,6 +330,7 @@ def __init__(self, *, setup_name, geodata_file, mobility_file, popnodes_key, nod elif mobility_file.suffix == ".npz": self.mobility = scipy.sparse.load_npz(mobility_file).astype(int) # Validate mobility data + # data valication/arrangement is needed if self.mobility.shape != (self.nnodes, self.nnodes): raise ValueError( f"mobility data must have dimensions of length of geodata ({self.nnodes}, {self.nnodes}). Actual: {self.mobility.shape}" diff --git a/flepimop/gempyor_pkg/tests/seir/test_setup.py b/flepimop/gempyor_pkg/tests/seir/test_setup.py index 48582dfff..c38941e6f 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_setup.py +++ b/flepimop/gempyor_pkg/tests/seir/test_setup.py @@ -5,7 +5,7 @@ import pytest import confuse -from gempyor import setup +from gempyor import setup, parameters from gempyor.utils import config @@ -15,12 +15,544 @@ os.chdir(os.path.dirname(__file__)) -class TestSpatialSetup: - def test_SpatialSetup_success(self): +class TestSetup: + def test_Setup_success(self): ss = setup.SpatialSetup( setup_name=TEST_SETUP_NAME, geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.txt", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={}, + outcome_scenario=None, + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, + ) + + def test_tf_is_ahead_of_ti_fail(self): + # time to finish (tf) is ahead of time to start(ti) error + with pytest.raises(ValueError, match=r".*tf.*less.*"): + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-03-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + npi_scenario=None, + config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={}, + outcome_scenario=None, + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, + ) + + def test_w_config_seir_exists_success(self): + # if seir_config is None and config["seir"].exists() then update + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_seir.yml") + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={}, + outcome_scenario=None, + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, + ) + + assert s.seir_config != None + + assert s.integration_method == 'legacy' + + def test_w_config_seir_integration_method_rk4_1_success(self): + # if seir_config["integration"]["method"] is best.current + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_seir_integration_method_rk4_1.yml") + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={}, + outcome_scenario=None, + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, + ) + assert s.integration_method == "rk4.jit" + + assert s.dt == float(1/6) + + def test_w_config_seir_integration_method_rk4_2_success(self): + # if seir_config["integration"]["method"] is rk4 + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_seir_integration_method_rk4_2.yml") + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={}, + outcome_scenario=None, + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, + ) + assert s.integration_method == "rk4.jit" + + def test_w_config_seir_no_integration_success(self): + # if not seir_config["integration"] + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_seir_no_integration.yml") + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={}, + outcome_scenario=None, + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, + ) + assert s.integration_method == "rk4.jit" + + assert s.dt == 2.0 + + def test_w_config_seir_unknown_integration_method_fail(self): + with pytest.raises(ValueError, match=r".*Unknown.*integration.*"): + # if in seir unknown integration method + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_seir_unknown_integration.yml") + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + # first_sim_index=1, + ) + # print(s.integration_method) + + def test_w_config_seir_integration_but_no_dt_success(self): + # if not seir_config["integration"]["dt"] + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_seir_no_dt.yml") + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + dt=None, # step size, in days + ) + + assert s.dt == 2.0 + + def test_w_config_seir_old_integration_method_fail(self): + with pytest.raises(ValueError, match=r".*Configuration.*no.*longer.*"): + # if old method in seir + #config.clear() + #config.read(user=False) + #config.set_file(f"{DATA_DIR}/config_seir_unknown_integration.yml") + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + config_version="v2", + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + ) + + def test_w_config_seir_config_version_not_provided_fail(self): + with pytest.raises(ValueError, match=r".*Should.*non-specified.*"): + # if not seir_config["integration"]["dt"] + # config.clear() + # config.read(user=False) + # config.set_file(f"{DATA_DIR}/config_seir_no_dt.yml") + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + config_version="v1", + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + dt=None, # step size, in days + ) + + def test_w_config_compartments_and_seir_config_not_None_success(self): + # if config["compartments"] and iself.seir_config was set + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_compartment.yml") + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + dt=None, # step size, in days + ) + + def test_config_outcome_config_and_scenario_success(self): + # if outcome_config and outcome_scenario were set + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + dt=None, # step size, in days + outcomes_config={"interventions":{"settings":{"None": + {"template":"Reduce", + "parameter":"r0", + "value": + { + "distribution":"fixed", + "value":0 + } + } + }}}, + outcome_scenario="None", # caution! selected the defined "None" + write_csv=True, + ) + assert s.npi_config_outcomes == s.outcomes_config["interventions"]["settings"]["None"] + assert s.extension == "csv" + + def test_config_write_csv_and_write_parquet_success(self): + # if both write_csv and write_parquet are True + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + dt=None, # step size, in days + outcomes_config={"interventions":{"settings":{"None": + {"template":"Reduce", + "parameter":"r0", + "value": + { + "distribution":"fixed", + "value":0 + } + } + }}}, + outcome_scenario="None", # caution! selected the defined "None" + write_csv=True, + write_parquet=True, + ) + assert s.write_parquet + + def test_w_config_seir_exists_and_outcomes_config(self): + # if seir_config is None and config["seir"].exists() then update + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_seir.yml") + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={"interventions":{"settings":{"None": + {"template":"Reduce", + "parameter":"r0", + "value": + { + "distribution":"fixed", + "value":0 + } + } + }}}, + outcome_scenario="None", + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id="in_run_id_0", + in_prefix=None, + out_run_id="out_run_id_0", + out_prefix=None, + stoch_traj_flag=False, + ) + #s.get_input_filename(ftype="spar", sim_id=0, extension_override="") + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="seir", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="spar", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="snpi", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hosp", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hpar", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hnpi", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="seir", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="spar", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="snpi", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hosp", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hpar", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hnpi", sim_id=0)) + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="seir", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="spar", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="snpi", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hosp", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hpar", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hnpi", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="seir", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="spar", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="snpi", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hosp", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hpar", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hnpi", sim_id=1, extension_override="csv")) + + + ''' + def test_SpatialSetup_npz_success3(self): + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.npz", + popnodes_key="population", + nodenames_key="geoid", + ) + def test_SpatialSetup_wihout_mobility_success3(self): + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility0.csv", popnodes_key="population", nodenames_key="geoid", ) @@ -36,6 +568,26 @@ def test_bad_popnodes_key_fail(self): nodenames_key="geoid", ) + def test_population_0_nodes_fail(self): + with pytest.raises(ValueError, match=r".*population.*zero.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata0.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + + def test_mobility_fileformat_fail(self): + with pytest.raises(ValueError, match=r".*Mobility.*longform.*matrix.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility", + popnodes_key="population", + nodenames_key="geoid", + ) + def test_bad_nodenames_key_fail(self): with pytest.raises(ValueError, match=r".*nodenames_key.*"): setup.SpatialSetup( @@ -46,6 +598,26 @@ def test_bad_nodenames_key_fail(self): nodenames_key="wrong", ) + def test_duplicate_nodenames_key_fail(self): + with pytest.raises(ValueError, match=r".*duplicate.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata_dup.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + ''' + def test_mobility_shape_in_npz_fail(self): + with pytest.raises(ValueError, match=r".*mobility.*Actual.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility_2x3.npz", + popnodes_key="population", + nodenames_key="geoid", + ) + ''' def test_mobility_dimensions_fail(self): with pytest.raises(ValueError, match=r".*mobility.*dimensions.*"): setup.SpatialSetup( @@ -56,6 +628,16 @@ def test_mobility_dimensions_fail(self): nodenames_key="geoid", ) + def test_mobility_same_ori_dest_fail(self): + with pytest.raises(ValueError, match=r".*Mobility.*same.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility_same_ori_dest.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + def test_mobility_too_big_fail(self): with pytest.raises(ValueError, match=r".*mobility.*population.*"): setup.SpatialSetup( @@ -65,3 +647,13 @@ def test_mobility_too_big_fail(self): popnodes_key="population", nodenames_key="geoid", ) + def test_mobility_data_exceeded_fail(self): + with pytest.raises(ValueError, match=r".*mobility.*exceed.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility1001.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + ''' From c4d83397666fbaf83267f0bc56537dde5bc37faa Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 31 Aug 2023 11:39:06 -0400 Subject: [PATCH 08/50] added tests/interface --- .../tests/interface/data/config_min_test.yml | 123 + .../tests/interface/data/config_minimal.yaml | 123 + .../tests/interface/data/geodata.csv | 6 + .../data/geodata_2019_statelevel.csv | 52 + .../tests/interface/data/mobility.csv | 12 + .../data/mobility_2011-2015_statelevel.csv | 2330 +++++++++++++++++ .../tests/interface/test_interface.py | 50 + 7 files changed, 2696 insertions(+) create mode 100644 flepimop/gempyor_pkg/tests/interface/data/config_min_test.yml create mode 100644 flepimop/gempyor_pkg/tests/interface/data/config_minimal.yaml create mode 100644 flepimop/gempyor_pkg/tests/interface/data/geodata.csv create mode 100644 flepimop/gempyor_pkg/tests/interface/data/geodata_2019_statelevel.csv create mode 100644 flepimop/gempyor_pkg/tests/interface/data/mobility.csv create mode 100644 flepimop/gempyor_pkg/tests/interface/data/mobility_2011-2015_statelevel.csv create mode 100644 flepimop/gempyor_pkg/tests/interface/test_interface.py diff --git a/flepimop/gempyor_pkg/tests/interface/data/config_min_test.yml b/flepimop/gempyor_pkg/tests/interface/data/config_min_test.yml new file mode 100644 index 000000000..e155a65d8 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/interface/data/config_min_test.yml @@ -0,0 +1,123 @@ +name: minimal for interface +setup_name: minimal4interface +start_date: 2020-01-31 +end_date: 2020-05-31 +data_path: data +nslots: 1 + + +spatial_setup: + geodata: geodata.csv + mobility: mobility.csv + popnodes: population + nodenames: geoid + +seeding: + method: FolderDraw + seeding_file_type: seed + +initial_conditions: + method: Default + +compartments: + infection_stage: ["S", "E", "I1", "I2", "I3", "R"] + vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: legacy + dt: 1/6 + parameters: + alpha: + value: + distribution: fixed + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +interventions: + scenarios: + - None + - Scenario1 + - Scenario2 + settings: + None: + template: ReduceR0 + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: fixed + value: 0 + Wuhan: + template: Reduce + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: uniform + low: .14 + high: .33 + KansasCity: + template: MultiTimeReduce + parameter: r0 + groups: + - periods: + - start_date: 2020-04-01 + end_date: 2020-05-15 + affected_geoids: "all" + value: + distribution: uniform + low: .04 + high: .23 + Scenario1: + template: Stacked + scenarios: + - KansasCity + - Wuhan + - None + Scenario2: + template: Stacked + scenarios: + - Wuhan diff --git a/flepimop/gempyor_pkg/tests/interface/data/config_minimal.yaml b/flepimop/gempyor_pkg/tests/interface/data/config_minimal.yaml new file mode 100644 index 000000000..15ab5792b --- /dev/null +++ b/flepimop/gempyor_pkg/tests/interface/data/config_minimal.yaml @@ -0,0 +1,123 @@ +name: minimal +setup_name: minimal +start_date: 2020-01-31 +end_date: 2020-05-31 +data_path: data +nslots: 15 + + +spatial_setup: + geodata: geodata.csv + mobility: mobility.txt + popnodes: population + nodenames: geoid + +seeding: + method: FolderDraw + seeding_file_type: seed + +initial_conditions: + method: Default + +compartments: + infection_stage: ["S", "E", "I1", "I2", "I3", "R"] + vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: legacy + dt: 1/6 + parameters: + alpha: + value: + distribution: fixed + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +interventions: + scenarios: + - None + - Scenario1 + - Scenario2 + settings: + None: + template: ReduceR0 + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: fixed + value: 0 + Wuhan: + template: Reduce + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: uniform + low: .14 + high: .33 + KansasCity: + template: MultiTimeReduce + parameter: r0 + groups: + - periods: + - start_date: 2020-04-01 + end_date: 2020-05-15 + affected_geoids: "all" + value: + distribution: uniform + low: .04 + high: .23 + Scenario1: + template: Stacked + scenarios: + - KansasCity + - Wuhan + - None + Scenario2: + template: Stacked + scenarios: + - Wuhan diff --git a/flepimop/gempyor_pkg/tests/interface/data/geodata.csv b/flepimop/gempyor_pkg/tests/interface/data/geodata.csv new file mode 100644 index 000000000..f4fa78f6a --- /dev/null +++ b/flepimop/gempyor_pkg/tests/interface/data/geodata.csv @@ -0,0 +1,6 @@ +"geoid","USPS","population" +"15005","HI",75 +"15007","HI",71377 +"15009","HI",165281 +"15001","HI",197658 +"15003","HI",987638 diff --git a/flepimop/gempyor_pkg/tests/interface/data/geodata_2019_statelevel.csv b/flepimop/gempyor_pkg/tests/interface/data/geodata_2019_statelevel.csv new file mode 100644 index 000000000..f0bbbd8f7 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/interface/data/geodata_2019_statelevel.csv @@ -0,0 +1,52 @@ +USPS,geoid,pop2019est +WY,56000,581024 +VT,50000,624313 +DC,11000,692683 +AK,02000,737068 +ND,38000,756717 +SD,46000,870638 +DE,10000,957248 +MT,30000,1050649 +RI,44000,1057231 +ME,23000,1335492 +NH,33000,1348124 +HI,15000,1422094 +ID,16000,1717750 +WV,54000,1817305 +NE,31000,1914571 +NM,35000,2092454 +KS,20000,2910652 +NV,32000,2972382 +MS,28000,2984418 +AR,05000,2999370 +UT,49000,3096848 +IA,19000,3139508 +CT,09000,3575074 +OK,40000,3932870 +OR,41000,4129803 +KY,21000,4449052 +LA,22000,4664362 +AL,01000,4876250 +SC,45000,5020806 +MN,27000,5563378 +CO,08000,5610349 +WI,55000,5790716 +MD,24000,6018848 +MO,29000,6104910 +IN,18000,6665703 +TN,47000,6709356 +MA,25000,6850553 +AZ,04000,7050299 +WA,53000,7404107 +VA,51000,8454463 +NJ,34000,8878503 +MI,26000,9965265 +NC,37000,10264876 +GA,13000,10403847 +OH,39000,11655397 +IL,17000,12770631 +PA,42000,12791530 +NY,36000,19572319 +FL,12000,20901636 +TX,48000,28260856 +CA,06000,39283497 diff --git a/flepimop/gempyor_pkg/tests/interface/data/mobility.csv b/flepimop/gempyor_pkg/tests/interface/data/mobility.csv new file mode 100644 index 000000000..c850a7021 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/interface/data/mobility.csv @@ -0,0 +1,12 @@ +"ori","dest","amount" +"15001","15003",625 +"15001","15007",4 +"15001","15009",181 +"15003","15001",62 +"15003","15007",34 +"15003","15009",614 +"15005","15009",4 +"15007","15003",232 +"15007","15009",97 +"15009","15001",25 +"15009","15003",418 diff --git a/flepimop/gempyor_pkg/tests/interface/data/mobility_2011-2015_statelevel.csv b/flepimop/gempyor_pkg/tests/interface/data/mobility_2011-2015_statelevel.csv new file mode 100644 index 000000000..a2da772ba --- /dev/null +++ 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diff --git a/flepimop/gempyor_pkg/tests/interface/test_interface.py b/flepimop/gempyor_pkg/tests/interface/test_interface.py new file mode 100644 index 000000000..38ba7a396 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/interface/test_interface.py @@ -0,0 +1,50 @@ +import pytest +import datetime +import os +import pandas as pd +#import dask.dataframe as dd +import pyarrow as pa +import time +import confuse + +from gempyor import utils, interface, setup, parameters +from gempyor.utils import config + +TEST_SETUP_NAME = "minimal_test" + +DATA_DIR = os.path.dirname(__file__) + "/data" +os.chdir(os.path.dirname(__file__)) + +tmp_path = "/tmp" + +class TestInferenceSimulator: + def test_InferenceSimulator_success(self): + # the minimum model test, choices are: npi_scenario="None" + # config.set_file(f"{DATA_DIR}/config_min_test.yml") + i = interface.InferenceSimulator(config_path=f"{DATA_DIR}/config_min_test.yml", npi_scenario="None") + ''' run_id="test_run_id" = in_run_id, + prefix="test_prefix" = in_prefix = out_prefix, + out_run_id = in_run_id, + ''' + + i.update_prefix("test_new_in_prefix") + assert i.s.in_prefix == "test_new_in_prefix" + assert i.s.out_prefix == "test_new_in_prefix" + + i.update_prefix("test_newer_in_prefix", "test_newer_out_prefix") + assert i.s.in_prefix == "test_newer_in_prefix" + assert i.s.out_prefix == "test_newer_out_prefix" + + i.update_run_id("test_new_run_id") + assert i.s.in_run_id == "test_new_run_id" + assert i.s.out_run_id == "test_new_run_id" + + i.update_run_id("test_newer_in_run_id", "test_newer_out_run_id") + assert i.s.in_run_id == "test_newer_in_run_id" + assert i.s.out_run_id == "test_newer_out_run_id" + + # i.one_simulation_legacy(sim_id2write=0) + i.build_structure() + assert i.already_built + + # i.one_simulation(sim_id2write=0) From fcc46b4f90c9ed49d394ba4a70c7b02604764c44 Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 31 Aug 2023 11:40:19 -0400 Subject: [PATCH 09/50] create tests/utils/* to cover utils.py --- .../gempyor_pkg/tests/utils/data/mobility | 3 + .../gempyor_pkg/tests/utils/data/mobility.csv | 12 ++++ .../data/usa-geoid-params-output.parquet | Bin 0 -> 86209 bytes .../gempyor_pkg/tests/utils/test_utils.py | 67 ++++++++++++++++++ 4 files changed, 82 insertions(+) create mode 100644 flepimop/gempyor_pkg/tests/utils/data/mobility create mode 100644 flepimop/gempyor_pkg/tests/utils/data/mobility.csv create mode 100644 flepimop/gempyor_pkg/tests/utils/data/usa-geoid-params-output.parquet create mode 100644 flepimop/gempyor_pkg/tests/utils/test_utils.py diff --git a/flepimop/gempyor_pkg/tests/utils/data/mobility b/flepimop/gempyor_pkg/tests/utils/data/mobility new file mode 100644 index 000000000..82b7fe6c3 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/utils/data/mobility @@ -0,0 +1,3 @@ +ori,dest,amount +10001,20002,500 +20002,10001,1500 diff --git a/flepimop/gempyor_pkg/tests/utils/data/mobility.csv b/flepimop/gempyor_pkg/tests/utils/data/mobility.csv new file mode 100644 index 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os.path.dirname(__file__) + "/data" +#os.chdir(os.path.dirname(__file__)) + +tmp_path = "/tmp" + +@pytest.mark.parametrize(('fname','extension'),[ + ('mobility','csv'), + ('usa-geoid-params-output','parquet'), +]) +def test_read_df_and_write_success(fname, extension): + os.chdir(tmp_path) + os.makedirs("data",exist_ok=True) + os.chdir("data") + df1 = utils.read_df(fname=f"{DATA_DIR}/"+fname, extension=extension) + if extension == "csv": + df2 = pd.read_csv(f"{DATA_DIR}/"+fname+"."+extension) + assert df2.equals(df1) + utils.write_df(tmp_path+"/data/"+fname,df2, extension=extension) + assert os.path.isfile(tmp_path+"/data/"+fname+"."+extension) + elif extension == "parquet": + df2 = pa.parquet.read_table(f"{DATA_DIR}/"+fname+"."+extension).to_pandas() + assert df2.equals(df1) + utils.write_df(tmp_path+"/data/"+fname,df2, extension=extension) + assert os.path.isfile(tmp_path+"/data/"+fname+"."+extension) + +@pytest.mark.parametrize(('fname','extension'),[ + ('mobility','csv'), + ('usa-geoid-params-output','parquet') +]) +def test_read_df_and_write_fail(fname, extension): + with pytest.raises(NotImplementedError, match=r".*Invalid.*extension.*Must.*"): + os.chdir(tmp_path) + os.makedirs("data",exist_ok=True) + os.chdir("data") + df1 = utils.read_df(fname=f"{DATA_DIR}/"+fname, extension=extension) + if extension == "csv": + df2 = pd.read_csv(f"{DATA_DIR}/"+fname+"."+extension) + assert df2.equals(df1) + utils.write_df(tmp_path+"/data/"+fname,df2, extension='') + elif extension == "parquet": + df2 = pa.parquet.read_table(f"{DATA_DIR}/"+fname+"."+extension).to_pandas() + assert df2.equals(df1) + utils.write_df(tmp_path+"/data/"+fname,df2, extension='') + +@pytest.mark.parametrize(('fname','extension'),[ + ('mobility','') +]) +def test_read_df_fail(fname, extension): + with pytest.raises(NotImplementedError, match=r".*Invalid.*extension.*"): + os.chdir(tmp_path) + utils.read_df(fname=f"{DATA_DIR}/"+fname, extension=extension) +def test_Timer_with_statement_success(): + with utils.Timer(name="test") as t: + time.sleep(1) + +def test_aws_disk_diagnosis_success(): + utils.aws_disk_diagnosis() From 2517e5f0a3b7908802255115065e16c6cda1c8cd Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 31 Aug 2023 11:44:54 -0400 Subject: [PATCH 10/50] separated tests/seir/test_SpatialSetup.py from tests/seir/test_setup.py --- .../tests/seir/test_SpatialSetup.py | 152 ++++++++++++++++++ 1 file changed, 152 insertions(+) create mode 100644 flepimop/gempyor_pkg/tests/seir/test_SpatialSetup.py diff --git a/flepimop/gempyor_pkg/tests/seir/test_SpatialSetup.py b/flepimop/gempyor_pkg/tests/seir/test_SpatialSetup.py new file mode 100644 index 000000000..e2291f20d --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/test_SpatialSetup.py @@ -0,0 +1,152 @@ +import datetime +import numpy as np +import os +import pandas as pd +import pytest +import confuse + +from gempyor import setup + +from gempyor.utils import config + +TEST_SETUP_NAME = "minimal_test" + +DATA_DIR = os.path.dirname(__file__) + "/data" +os.chdir(os.path.dirname(__file__)) + + +class TestSpatialSetup: + def test_SpatialSetup_success(self): + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.txt", # but warning message presented + popnodes_key="population", + nodenames_key="geoid", + ) + def test_SpatialSetup_success2(self): + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + ''' + def test_SpatialSetup_npz_success3(self): + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.npz", + popnodes_key="population", + nodenames_key="geoid", + ) + ''' + def test_SpatialSetup_wihout_mobility_success3(self): + ss = setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility0.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + + def test_bad_popnodes_key_fail(self): + # Bad popnodes_key error + with pytest.raises(ValueError, match=r".*popnodes_key.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility_small.txt", + popnodes_key="wrong", + nodenames_key="geoid", + ) + + def test_population_0_nodes_fail(self): + with pytest.raises(ValueError, match=r".*population.*zero.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata0.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + + def test_mobility_fileformat_fail(self): + with pytest.raises(ValueError, match=r".*Mobility.*longform.*matrix.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility", + popnodes_key="population", + nodenames_key="geoid", + ) + + def test_bad_nodenames_key_fail(self): + with pytest.raises(ValueError, match=r".*nodenames_key.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.txt", + popnodes_key="population", + nodenames_key="wrong", + ) + + def test_duplicate_nodenames_key_fail(self): + with pytest.raises(ValueError, match=r".*duplicate.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata_dup.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + + def test_mobility_shape_in_npz_fail(self): + with pytest.raises(ValueError, match=r".*mobility.*Actual.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility_2x3.npz", + popnodes_key="population", + nodenames_key="geoid", + ) + + def test_mobility_dimensions_fail(self): + with pytest.raises(ValueError, match=r".*mobility.*dimensions.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility_small.txt", + popnodes_key="population", + nodenames_key="geoid", + ) + + def test_mobility_same_ori_dest_fail(self): + with pytest.raises(ValueError, match=r".*Mobility.*same.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility_same_ori_dest.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + + def test_mobility_too_big_fail(self): + with pytest.raises(ValueError, match=r".*mobility.*population.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility_big.txt", + popnodes_key="population", + nodenames_key="geoid", + ) + def test_mobility_data_exceeded_fail(self): + with pytest.raises(ValueError, match=r".*mobility.*exceed.*"): + setup.SpatialSetup( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility1001.csv", + popnodes_key="population", + nodenames_key="geoid", + ) From 1580008ed85bafdfa5d4ee0d9a989e8fa35251ae Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 31 Aug 2023 11:46:06 -0400 Subject: [PATCH 11/50] added tests/npi/test_ReduceR0.py and its data --- .../tests/npi/data/config_minimal.yaml | 123 ++++++++++++++++++ .../gempyor_pkg/tests/npi/test_ReduceR0.py | 48 +++++++ 2 files changed, 171 insertions(+) create mode 100644 flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml create mode 100644 flepimop/gempyor_pkg/tests/npi/test_ReduceR0.py diff --git a/flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml b/flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml new file mode 100644 index 000000000..15ab5792b --- /dev/null +++ b/flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml @@ -0,0 +1,123 @@ +name: minimal +setup_name: minimal +start_date: 2020-01-31 +end_date: 2020-05-31 +data_path: data +nslots: 15 + + +spatial_setup: + geodata: geodata.csv + mobility: mobility.txt + popnodes: population + nodenames: geoid + +seeding: + method: FolderDraw + seeding_file_type: seed + +initial_conditions: + method: Default + +compartments: + infection_stage: ["S", "E", "I1", "I2", "I3", "R"] + vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: legacy + dt: 1/6 + parameters: + alpha: + value: + distribution: fixed + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +interventions: + scenarios: + - None + - Scenario1 + - Scenario2 + settings: + None: + template: ReduceR0 + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: fixed + value: 0 + Wuhan: + template: Reduce + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: uniform + low: .14 + high: .33 + KansasCity: + template: MultiTimeReduce + parameter: r0 + groups: + - periods: + - start_date: 2020-04-01 + end_date: 2020-05-15 + affected_geoids: "all" + value: + distribution: uniform + low: .04 + high: .23 + Scenario1: + template: Stacked + scenarios: + - KansasCity + - Wuhan + - None + Scenario2: + template: Stacked + scenarios: + - Wuhan diff --git a/flepimop/gempyor_pkg/tests/npi/test_ReduceR0.py b/flepimop/gempyor_pkg/tests/npi/test_ReduceR0.py new file mode 100644 index 000000000..ca6ec548c --- /dev/null +++ b/flepimop/gempyor_pkg/tests/npi/test_ReduceR0.py @@ -0,0 +1,48 @@ +import pandas as pd +import numpy as np +import os +import pathlib +import confuse + +from gempyor import NPI, setup +from gempyor.utils import config + +DATA_DIR = os.path.dirname(__file__) + "/data" +os.chdir(os.path.dirname(__file__)) + +class Test_ReduceR0: + def test_ReduceR0_success(self): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_minimal.yaml") + + ss = setup.SpatialSetup( + setup_name="test_seir", + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + + s = setup.Setup( + setup_name="test_seir", + spatial_setup=ss, + nslots=1, + npi_scenario="None", + npi_config_seir=config["interventions"]["settings"]["None"], + parameters_config=config["seir"]["parameters"], + seeding_config=config["seeding"], + ti=config["start_date"].as_date(), + tf=config["end_date"].as_date(), + interactive=True, + write_csv=False, + # first_sim_index=first_sim_index, + # in_run_id=run_id, + # in_prefix=prefix, + # out_run_id=run_id, + # out_prefix=prefix, + dt=0.25, + ) + + test = NPI.ReduceR0(npi_config=s.npi_config_seir, global_config=config,geoids=s.spatset.nodenames) + From 14b05592e8f8122e718dd02d4f4da2e5911e254d Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 31 Aug 2023 11:48:40 -0400 Subject: [PATCH 12/50] added data for test_setup.py --- .../tests/seir/data/config_compartment.yml | 119 +++++++++++++++++ .../tests/seir/data/config_seir.yml | 123 ++++++++++++++++++ .../config_seir_integration_method_rk4_1.yml | 123 ++++++++++++++++++ .../config_seir_integration_method_rk4_2.yml | 123 ++++++++++++++++++ .../tests/seir/data/config_seir_no_dt.yml | 123 ++++++++++++++++++ .../seir/data/config_seir_no_integration.yml | 123 ++++++++++++++++++ .../data/config_seir_unknown_integration.yml | 123 ++++++++++++++++++ .../gempyor_pkg/tests/seir/data/geodata0.csv | 2 + .../tests/seir/data/geodata_dup.csv | 4 + .../gempyor_pkg/tests/seir/data/mobility.npz | Bin 0 -> 976 bytes .../gempyor_pkg/tests/seir/data/mobility0.csv | 3 + .../tests/seir/data/mobility1001.csv | 3 + .../gempyor_pkg/tests/seir/data/mobility_.npz | Bin 0 -> 981 bytes .../tests/seir/data/mobility_2x3.npz | Bin 0 -> 977 bytes .../tests/seir/data/mobility_pd.npz | Bin 0 -> 296 bytes .../seir/data/mobility_same_ori_dest.csv | 3 + 16 files changed, 872 insertions(+) create mode 100644 flepimop/gempyor_pkg/tests/seir/data/config_compartment.yml create mode 100644 flepimop/gempyor_pkg/tests/seir/data/config_seir.yml create mode 100644 flepimop/gempyor_pkg/tests/seir/data/config_seir_integration_method_rk4_1.yml create mode 100644 flepimop/gempyor_pkg/tests/seir/data/config_seir_integration_method_rk4_2.yml create mode 100644 flepimop/gempyor_pkg/tests/seir/data/config_seir_no_dt.yml create mode 100644 flepimop/gempyor_pkg/tests/seir/data/config_seir_no_integration.yml create mode 100644 flepimop/gempyor_pkg/tests/seir/data/config_seir_unknown_integration.yml create mode 100644 flepimop/gempyor_pkg/tests/seir/data/geodata0.csv create mode 100644 flepimop/gempyor_pkg/tests/seir/data/geodata_dup.csv create mode 100644 flepimop/gempyor_pkg/tests/seir/data/mobility.npz create mode 100644 flepimop/gempyor_pkg/tests/seir/data/mobility0.csv create mode 100644 flepimop/gempyor_pkg/tests/seir/data/mobility1001.csv create mode 100644 flepimop/gempyor_pkg/tests/seir/data/mobility_.npz create mode 100644 flepimop/gempyor_pkg/tests/seir/data/mobility_2x3.npz create mode 100644 flepimop/gempyor_pkg/tests/seir/data/mobility_pd.npz create mode 100644 flepimop/gempyor_pkg/tests/seir/data/mobility_same_ori_dest.csv diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_compartment.yml b/flepimop/gempyor_pkg/tests/seir/data/config_compartment.yml new file mode 100644 index 000000000..6763af77a --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/config_compartment.yml @@ -0,0 +1,119 @@ +#name: minimal +#setup_name: minimal +#start_date: 2020-01-31 +#end_date: 2020-05-31 +#data_path: data +#nslots: 15 + + +#spatial_setup: +# geodata: geodata.csv +# mobility: mobility.txt +# popnodes: population +# nodenames: geoid + +#seeding: +# method: FolderDraw +# seeding_file_type: seed + +#initial_conditions: +# method: Default + +compartments: + infection_stage: ["S", "E", "I1", "I2", "I3", "R"] + vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: legacy + dt: 1/6 +# parameters: +# alpha: +# value: +# distribution: fixed +# value: .9 +# sigma: value: distribution: fixed value: 1 / 5.2 +# value: +# distribution: uniform +# low: 1 / 6 +# high: 1 / 2.6 +# R0s: +# value: +# distribution: uniform +# low: 2 +# high: 3 +# transitions: +# - source: ["S", "unvaccinated"] +# destination: ["E", "unvaccinated"] +# rate: ["R0s * gamma", 1] +# proportional_to: [ +# ["S", "unvaccinated"], +# [[["I1", "I2", "I3"]], "unvaccinated"], +# ] +# proportion_exponent: [["1", "1"], ["alpha", "1"]] +# - source: [["E"], ["unvaccinated"]] +# destination: [["I1"], ["unvaccinated"]] +# rate: ["sigma", 1] +# proportional_to: [[["E"], ["unvaccinated"]]] +# proportion_exponent: [["1", "1"]] +# - source: [["I1"], ["unvaccinated"]] +# destination: [["I2"], ["unvaccinated"]] +# rate: ["3 * gamma", 1] +# proportional_to: [[["I1"], ["unvaccinated"]]] +# proportion_exponent: [["1", "1"]] +# - source: [["I2"], ["unvaccinated"]] +# destination: [["I3"], ["unvaccinated"]] +# rate: ["3 * gamma", 1] +# proportional_to: [[["I2"], ["unvaccinated"]]] +# proportion_exponent: [["1", "1"]] +# - source: [["I3"], ["unvaccinated"]] +# destination: [["R"], ["unvaccinated"]] +# rate: ["3 * gamma", 1] +# proportional_to: [[["I3"], ["unvaccinated"]]] +# proportion_exponent: [["1", "1"]] + +#interventions: +# scenarios: +# - None +# - Scenario1 +# - Scenario2 +# settings: +# None: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: fixed +# value: 0 +# Wuhan: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: uniform +# low: .14 +# high: .33 +# KansasCity: +# template: MultiTimeReduce +# parameter: r0 +# groups: +# - periods: +# - start_date: 2020-04-01 +# end_date: 2020-05-15 +# affected_geoids: "all" +# value: +# distribution: uniform +# low: .04 +# high: .23 +# Scenario1: +# template: Stacked +# scenarios: +# - KansasCity +# - Wuhan +# - None +# Scenario2: +# template: Stacked +# scenarios: +# - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_seir.yml b/flepimop/gempyor_pkg/tests/seir/data/config_seir.yml new file mode 100644 index 000000000..bc6f8e13f --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/config_seir.yml @@ -0,0 +1,123 @@ +#name: minimal +#setup_name: minimal +#start_date: 2020-01-31 +#end_date: 2020-05-31 +#data_path: data +#nslots: 15 + + +#spatial_setup: +# geodata: geodata.csv +# mobility: mobility.txt +# popnodes: population +# nodenames: geoid + +#seeding: +# method: FolderDraw +# seeding_file_type: seed + +#initial_conditions: +# method: Default + +#compartments: +# infection_stage: ["S", "E", "I1", "I2", "I3", "R"] +# vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: legacy + dt: 1/6 + parameters: + alpha: + value: + distribution: fixed + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +#interventions: +# scenarios: +# - None +# - Scenario1 +# - Scenario2 +# settings: +# None: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: fixed +# value: 0 +# Wuhan: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: uniform +# low: .14 +# high: .33 +# KansasCity: +# template: MultiTimeReduce +# parameter: r0 +# groups: +# - periods: +# - start_date: 2020-04-01 +# end_date: 2020-05-15 +# affected_geoids: "all" +# value: +# distribution: uniform +# low: .04 +# high: .23 +# Scenario1: +# template: Stacked +# scenarios: +# - KansasCity +# - Wuhan +# - None +# Scenario2: +# template: Stacked +# scenarios: +# - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_seir_integration_method_rk4_1.yml b/flepimop/gempyor_pkg/tests/seir/data/config_seir_integration_method_rk4_1.yml new file mode 100644 index 000000000..79624bc4b --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/config_seir_integration_method_rk4_1.yml @@ -0,0 +1,123 @@ +#name: minimal +#setup_name: minimal +#start_date: 2020-01-31 +#end_date: 2020-05-31 +#data_path: data +#nslots: 15 + + +#spatial_setup: +# geodata: geodata.csv +# mobility: mobility.txt +# popnodes: population +# nodenames: geoid + +#seeding: +# method: FolderDraw +# seeding_file_type: seed + +#initial_conditions: +# method: Default + +#compartments: +# infection_stage: ["S", "E", "I1", "I2", "I3", "R"] +# vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: best.current + dt: 1/6 + parameters: + alpha: + value: + distribution: fixed + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +#interventions: +# scenarios: +# - None +# - Scenario1 +# - Scenario2 +# settings: +# None: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: fixed +# value: 0 +# Wuhan: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: uniform +# low: .14 +# high: .33 +# KansasCity: +# template: MultiTimeReduce +# parameter: r0 +# groups: +# - periods: +# - start_date: 2020-04-01 +# end_date: 2020-05-15 +# affected_geoids: "all" +# value: +# distribution: uniform +# low: .04 +# high: .23 +# Scenario1: +# template: Stacked +# scenarios: +# - KansasCity +# - Wuhan +# - None +# Scenario2: +# template: Stacked +# scenarios: +# - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_seir_integration_method_rk4_2.yml b/flepimop/gempyor_pkg/tests/seir/data/config_seir_integration_method_rk4_2.yml new file mode 100644 index 000000000..2118f30a3 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/config_seir_integration_method_rk4_2.yml @@ -0,0 +1,123 @@ +#name: minimal +#setup_name: minimal +#start_date: 2020-01-31 +#end_date: 2020-05-31 +#data_path: data +#nslots: 15 + + +#spatial_setup: +# geodata: geodata.csv +# mobility: mobility.txt +# popnodes: population +# nodenames: geoid + +#seeding: +# method: FolderDraw +# seeding_file_type: seed + +#initial_conditions: +# method: Default + +#compartments: +# infection_stage: ["S", "E", "I1", "I2", "I3", "R"] +# vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: rk4 + dt: 1/6 + parameters: + alpha: + value: + distribution: fixed + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +#interventions: +# scenarios: +# - None +# - Scenario1 +# - Scenario2 +# settings: +# None: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: fixed +# value: 0 +# Wuhan: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: uniform +# low: .14 +# high: .33 +# KansasCity: +# template: MultiTimeReduce +# parameter: r0 +# groups: +# - periods: +# - start_date: 2020-04-01 +# end_date: 2020-05-15 +# affected_geoids: "all" +# value: +# distribution: uniform +# low: .04 +# high: .23 +# Scenario1: +# template: Stacked +# scenarios: +# - KansasCity +# - Wuhan +# - None +# Scenario2: +# template: Stacked +# scenarios: +# - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_seir_no_dt.yml b/flepimop/gempyor_pkg/tests/seir/data/config_seir_no_dt.yml new file mode 100644 index 000000000..3a0a2fd90 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/config_seir_no_dt.yml @@ -0,0 +1,123 @@ +#name: minimal +#setup_name: minimal +#start_date: 2020-01-31 +#end_date: 2020-05-31 +#data_path: data +#nslots: 15 + + +#spatial_setup: +# geodata: geodata.csv +# mobility: mobility.txt +# popnodes: population +# nodenames: geoid + +#seeding: +# method: FolderDraw +# seeding_file_type: seed + +#initial_conditions: +# method: Default + +#compartments: +# infection_stage: ["S", "E", "I1", "I2", "I3", "R"] +# vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: legacy +# dt: 1/6 + parameters: + alpha: + value: + distribution: fixed + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +#interventions: +# scenarios: +# - None +# - Scenario1 +# - Scenario2 +# settings: +# None: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: fixed +# value: 0 +# Wuhan: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: uniform +# low: .14 +# high: .33 +# KansasCity: +# template: MultiTimeReduce +# parameter: r0 +# groups: +# - periods: +# - start_date: 2020-04-01 +# end_date: 2020-05-15 +# affected_geoids: "all" +# value: +# distribution: uniform +# low: .04 +# high: .23 +# Scenario1: +# template: Stacked +# scenarios: +# - KansasCity +# - Wuhan +# - None +# Scenario2: +# template: Stacked +# scenarios: +# - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_seir_no_integration.yml b/flepimop/gempyor_pkg/tests/seir/data/config_seir_no_integration.yml new file mode 100644 index 000000000..226892884 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/config_seir_no_integration.yml @@ -0,0 +1,123 @@ +#name: minimal +#setup_name: minimal +#start_date: 2020-01-31 +#end_date: 2020-05-31 +#data_path: data +#nslots: 15 + + +#spatial_setup: +# geodata: geodata.csv +# mobility: mobility.txt +# popnodes: population +# nodenames: geoid + +#seeding: +# method: FolderDraw +# seeding_file_type: seed + +#initial_conditions: +# method: Default + +#compartments: +# infection_stage: ["S", "E", "I1", "I2", "I3", "R"] +# vaccination_stage: ["unvaccinated"] + +seir: +# integration: +# method: legacy +# dt: 1/6 + parameters: + alpha: + value: + distribution: fixed + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +#interventions: +# scenarios: +# - None +# - Scenario1 +# - Scenario2 +# settings: +# None: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: fixed +# value: 0 +# Wuhan: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: uniform +# low: .14 +# high: .33 +# KansasCity: +# template: MultiTimeReduce +# parameter: r0 +# groups: +# - periods: +# - start_date: 2020-04-01 +# end_date: 2020-05-15 +# affected_geoids: "all" +# value: +# distribution: uniform +# low: .04 +# high: .23 +# Scenario1: +# template: Stacked +# scenarios: +# - KansasCity +# - Wuhan +# - None +# Scenario2: +# template: Stacked +# scenarios: +# - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_seir_unknown_integration.yml b/flepimop/gempyor_pkg/tests/seir/data/config_seir_unknown_integration.yml new file mode 100644 index 000000000..c76410e9f --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/config_seir_unknown_integration.yml @@ -0,0 +1,123 @@ +#name: minimal +#setup_name: minimal +#start_date: 2020-01-31 +#end_date: 2020-05-31 +#data_path: data +#nslots: 15 + + +#spatial_setup: +# geodata: geodata.csv +# mobility: mobility.txt +# popnodes: population +# nodenames: geoid + +#seeding: +# method: FolderDraw +# seeding_file_type: seed + +#initial_conditions: +# method: Default + +#compartments: +# infection_stage: ["S", "E", "I1", "I2", "I3", "R"] +# vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: unknown + dt: 1/6 + parameters: + alpha: + value: + distribution: fixed + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +#interventions: +# scenarios: +# - None +# - Scenario1 +# - Scenario2 +# settings: +# None: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: fixed +# value: 0 +# Wuhan: +# template: Reduce +# parameter: r0 +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-15 +# value: +# distribution: uniform +# low: .14 +# high: .33 +# KansasCity: +# template: MultiTimeReduce +# parameter: r0 +# groups: +# - periods: +# - start_date: 2020-04-01 +# end_date: 2020-05-15 +# affected_geoids: "all" +# value: +# distribution: uniform +# low: .04 +# high: .23 +# Scenario1: +# template: Stacked +# scenarios: +# - KansasCity +# - Wuhan +# - None +# Scenario2: +# template: Stacked +# scenarios: +# - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/geodata0.csv b/flepimop/gempyor_pkg/tests/seir/data/geodata0.csv new file mode 100644 index 000000000..3e787eb34 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/geodata0.csv @@ -0,0 +1,2 @@ +geoid,population,include_in_report +10001,0,TRUE diff --git a/flepimop/gempyor_pkg/tests/seir/data/geodata_dup.csv b/flepimop/gempyor_pkg/tests/seir/data/geodata_dup.csv new file mode 100644 index 000000000..f126d7e40 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/geodata_dup.csv @@ -0,0 +1,4 @@ +geoid,population,include_in_report +10001,1000,TRUE +10001,1000,TRUE +20002,2000,FALSE diff --git a/flepimop/gempyor_pkg/tests/seir/data/mobility.npz b/flepimop/gempyor_pkg/tests/seir/data/mobility.npz new file mode 100644 index 0000000000000000000000000000000000000000..91b86992472fa70b40cb2748d1c6e14f34819e5c GIT binary patch literal 976 zcmWIWW@Zs#U|`??Vnqgy-9|P(K-L5x=4KFK$jnR0OinG<%PXj4WDo!g17#RMN-$24avq((;RP6H8$30EvPCNCgOB4UahCoHc~P0}>jH z>z+JPLUX`Bk1Gc}fkuO3gcIn1;*7+CRG9rBK@b2b00FG_1LI>MA^Y9n;h@IG(QpyX z4ao&Ht|36<7XUE_&Gg}K6q53YeNlhWNl(h4Diqo@MdKL$*}^V7|^&SKsg2m0K<{)KL7v# literal 0 HcmV?d00001 diff --git a/flepimop/gempyor_pkg/tests/seir/data/mobility0.csv b/flepimop/gempyor_pkg/tests/seir/data/mobility0.csv new file mode 100644 index 000000000..43ab71907 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/mobility0.csv @@ -0,0 +1,3 @@ +ori,dest,amount +10001,20002,0 +20002,10001,0 diff --git a/flepimop/gempyor_pkg/tests/seir/data/mobility1001.csv b/flepimop/gempyor_pkg/tests/seir/data/mobility1001.csv new file mode 100644 index 000000000..d3429cc4a --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/mobility1001.csv @@ -0,0 +1,3 @@ +ori,dest,amount +10001,20002,1001 +20002,10001,1500 diff --git a/flepimop/gempyor_pkg/tests/seir/data/mobility_.npz b/flepimop/gempyor_pkg/tests/seir/data/mobility_.npz new file mode 100644 index 0000000000000000000000000000000000000000..e6d4a7ca95c4309644fa776b43cfab6f5a63545f GIT binary patch literal 981 zcmWIWW@Zs#U|`??Vnv42F`H-j0a-JEn43X_Au}%}GdZWE9QODy2G-$24avq((;RP6H8$30EvPCNCgOB4UahCoHc~P0}>jH z>z+JPLUTZZM;(tB&}dMMZ~`4roRL_N3bP+12m&AlAb{0=V02B#x(?J{sJK806HQiu_O`Z29OvCfaF1dY&Rg%gsMH8!H+{63t6u!evu910HqB^ zCJ|;_$rNHQh-_d4kw}RaT_b9;0x5=o2F6Yx1E>+2$k6qnCIgr*kcEOkD-nqTT_0-f pA?uq2)CZ4CbZw~NjjTstT- literal 0 HcmV?d00001 diff --git a/flepimop/gempyor_pkg/tests/seir/data/mobility_pd.npz b/flepimop/gempyor_pkg/tests/seir/data/mobility_pd.npz new file mode 100644 index 0000000000000000000000000000000000000000..5dc17b29131ee79bbd595cbfc5c3d0338732a846 GIT binary patch literal 296 zcmWIWW@Zs#fB;2?I_RtO#7&B!FejLUNnH6XG9tPk$h0B=?{kT4? Date: Thu, 31 Aug 2023 11:49:40 -0400 Subject: [PATCH 13/50] added tests/seir/data/mobility to use in the test of no extension --- flepimop/gempyor_pkg/tests/seir/data/mobility | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 flepimop/gempyor_pkg/tests/seir/data/mobility diff --git a/flepimop/gempyor_pkg/tests/seir/data/mobility b/flepimop/gempyor_pkg/tests/seir/data/mobility new file mode 100644 index 000000000..82b7fe6c3 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/mobility @@ -0,0 +1,3 @@ +ori,dest,amount +10001,20002,500 +20002,10001,1500 From c110f10eaa9ee5a59f9a60172fd2a3758efe0cc7 Mon Sep 17 00:00:00 2001 From: kjsato Date: Wed, 6 Sep 2023 16:39:34 -0400 Subject: [PATCH 14/50] inserted a NOTE which should be commented out --- flepimop/gempyor_pkg/src/gempyor/parameters.py | 1 + 1 file changed, 1 insertion(+) diff --git a/flepimop/gempyor_pkg/src/gempyor/parameters.py b/flepimop/gempyor_pkg/src/gempyor/parameters.py index 993bcc31f..0e7d25410 100644 --- a/flepimop/gempyor_pkg/src/gempyor/parameters.py +++ b/flepimop/gempyor_pkg/src/gempyor/parameters.py @@ -37,6 +37,7 @@ def __init__( if self.npar != len(set([name.lower() for name in self.pnames])): raise ValueError( "Parameters of the SEIR model have the same name (remember that case is not sufficient!)" + #NOTE: should this lines be eliminated? ) # Attributes of dictionary From e2ec6e4f9c90d99f79fe98f06540d73f1fa45213 Mon Sep 17 00:00:00 2001 From: kjsato Date: Wed, 6 Sep 2023 16:42:35 -0400 Subject: [PATCH 15/50] added try except on checking config[seir][parameters].existed() in such a case confuse.exception.NotFoundError will be returned --- flepimop/gempyor_pkg/src/gempyor/setup.py | 125 ++++++++++++---------- 1 file changed, 68 insertions(+), 57 deletions(-) diff --git a/flepimop/gempyor_pkg/src/gempyor/setup.py b/flepimop/gempyor_pkg/src/gempyor/setup.py index 664c576a6..25d5ca01f 100644 --- a/flepimop/gempyor_pkg/src/gempyor/setup.py +++ b/flepimop/gempyor_pkg/src/gempyor/setup.py @@ -8,6 +8,7 @@ import scipy.sparse import pyarrow as pa import copy +import confuse from . import compartments from . import parameters from . import seeding_ic @@ -85,72 +86,82 @@ def __init__( # I'm not really sure if we should impose defaut or make setup really explicit and # have users pass #if seir_config is None and config["seir"].exists(): - if not seir_config and config["seir"].exists(): - self.seir_config = config["seir"] + try: + if not seir_config and config["seir"].exists(): + self.seir_config = config["seir"] # added below to cope with the imcompleteness of config["seir"] - if not parameters_config and config["seir"]["parameters"].exists(): - self.parameters_config = config["seir"]["parameters"] + if (not any(parameters_config)) and config["seir"]["parameters"].exists(): + self.parameters_config = config["seir"]["parameters"] + #if (not parameters_config ) and config["seir"]["parameters"].exists(): + #if (not any(parameters_config) ) and self.seir_config["parameters"].keys(): + #if self.seir_config): + #except confuse.exceptions.NotFoundError as e: + # print("catch NotFoundError:",e) # Set-up the integration method and the time step #if config["seir"].exists() and (seir_config or parameters_config): - if (self.seir_config and self.parameters_config): + if (self.seir_config or config["seir"].exists()) and (any(self.parameters_config) or config["seir"]["parameters"].exists()): + #if (self.seir_config and self.parameters_config): #if ((seir_config or self.seir_config) and parameters_config): # modified to handle the case of "T and (F or F)) -> F" - if "integration" in self.seir_config.keys(): - if "method" in self.seir_config["integration"].keys(): - self.integration_method = self.seir_config["integration"]["method"].get() - if self.integration_method == "best.current": - self.integration_method = "rk4.jit" - if self.integration_method == "rk4": - self.integration_method = "rk4.jit" - if self.integration_method not in ["rk4.jit", "legacy"]: - raise ValueError(f"Unknown integration method {self.integration_method}.") - if "dt" in self.seir_config["integration"].keys() and self.dt is None: - self.dt = float( - eval(str(self.seir_config["integration"]["dt"].get())) + if "integration" in self.seir_config.keys(): + if "method" in self.seir_config["integration"].keys(): + self.integration_method = self.seir_config["integration"]["method"].get() + print(self.integration_method) + if self.integration_method == "best.current": + self.integration_method = "rk4.jit" + if self.integration_method == "rk4": + self.integration_method = "rk4.jit" + if self.integration_method not in ["rk4.jit", "legacy"]: + raise ValueError(f"Unknown integration method {self.integration_method}.") + if "dt" in self.seir_config["integration"].keys() and self.dt is None: + self.dt = float( + eval(str(self.seir_config["integration"]["dt"].get())) ) # ugly way to parse string and formulas - elif self.dt is None: - self.dt = 2.0 - else: - self.integration_method = "rk4.jit" - if self.dt is None: - self.dt = 2.0 - logging.info(f"Integration method not provided, assuming type {self.integration_method}") - if self.dt is not None: - self.dt = float(self.dt) - - if config_version is None: - config_version = "v3" - logging.debug(f"Config version not provided, infering type {config_version}") - - if config_version not in ["old", "v2", "v3"]: - raise ValueError( - f"Configuration version unknown: {config_version}. \n" - f"Should be either non-specified (default: 'v3'), or set to 'old' or 'v2'." - ) - elif config_version == "old" or config_version == "v2": + elif self.dt is None: + self.dt = 2.0 + else: + self.integration_method = "rk4.jit" + if self.dt is None: + self.dt = 2.0 + logging.info(f"Integration method not provided, assuming type {self.integration_method}") + except confuse.exceptions.NotFoundError as e: + print("catch NotFoundError:",e) + if self.dt is not None: + self.dt = float(self.dt) + + if config_version is None: + config_version = "v3" + logging.debug(f"Config version not provided, infering type {config_version}") + + if config_version not in ["old", "v2", "v3"]: + raise ValueError( + f"Configuration version unknown: {config_version}. \n" + f"Should be either non-specified (default: 'v3'), or set to 'old' or 'v2'." + ) + elif config_version == "old" or config_version == "v2": # NOTE: even behaved as old, "v2" seems by default in parameter.py - raise ValueError( - f"Configuration version 'old' and 'v2' are no longer supported by flepiMoP\n" - f"Please use a 'v3' instead, or use the COVIDScenarioPipeline package. " - ) - - # Think if we really want to hold this up. - self.parameters = parameters.Parameters( - parameter_config=self.parameters_config, - config_version=config_version, - ti=self.ti, - tf=self.tf, - nodenames=self.spatset.nodenames, + raise ValueError( + f"Configuration version 'old' and 'v2' are no longer supported by flepiMoP\n" + f"Please use a 'v3' instead, or use the COVIDScenarioPipeline package. " ) - self.seedingAndIC = seeding_ic.SeedingAndIC( - seeding_config=self.seeding_config, - initial_conditions_config=self.initial_conditions_config, + + # Think if we really want to hold this up. + self.parameters = parameters.Parameters( + parameter_config=self.parameters_config, + config_version=config_version, + ti=self.ti, + tf=self.tf, + nodenames=self.spatset.nodenames, + ) + self.seedingAndIC = seeding_ic.SeedingAndIC( + seeding_config=self.seeding_config, + initial_conditions_config=self.initial_conditions_config, + ) + # really ugly references to the config globally here. + if config["compartments"].exists() and self.seir_config is not None: + self.compartments = compartments.Compartments( + seir_config=self.seir_config, compartments_config=config["compartments"] ) - # really ugly references to the config globally here. - if config["compartments"].exists() and self.seir_config is not None: - self.compartments = compartments.Compartments( - seir_config=self.seir_config, compartments_config=config["compartments"] - ) # 3. Outcomes self.npi_config_outcomes = None From 72749d9af5300fd406aa62d2bb065359e2784d00 Mon Sep 17 00:00:00 2001 From: kjsato Date: Wed, 6 Sep 2023 16:45:08 -0400 Subject: [PATCH 16/50] modified to go through the currect test cases --- .../tests/outcomes/test_outcomes0.py | 43 +++++++++++++ .../gempyor_pkg/tests/seir/dev_new_test0.py | 63 +++++++++++++++++++ flepimop/gempyor_pkg/tests/seir/test_setup.py | 8 ++- 3 files changed, 112 insertions(+), 2 deletions(-) create mode 100644 flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py create mode 100644 flepimop/gempyor_pkg/tests/seir/dev_new_test0.py diff --git a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py new file mode 100644 index 000000000..53e93a6ed --- /dev/null +++ b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py @@ -0,0 +1,43 @@ +import gempyor +import numpy as np +import pandas as pd +import datetime +import pytest + +from gempyor.utils import config + +import pandas as pd +import numpy as np +import datetime +import matplotlib.pyplot as plt +import glob, os, sys +from pathlib import Path + +# import seaborn as sns +import pyarrow.parquet as pq +import pyarrow as pa +from gempyor import file_paths, setup, outcomes + +config_path_prefix = "" #'tests/outcomes/' + +### To generate files for this test, see notebook Test Outcomes playbook.ipynb in COVID19_Maryland + +geoid = ["15005", "15007", "15009", "15001", "15003"] +diffI = np.arange(5) * 2 +date_data = datetime.date(2020, 4, 15) +subclasses = ["_A", "_B"] + +os.chdir(os.path.dirname(__file__)) + + +def test_outcome_scenario(): + os.chdir(os.path.dirname(__file__)) ## this is redundant but necessary. Why ? + inference_simulator = gempyor.InferenceSimulator( + config_path=f"{config_path_prefix}config.yml", + run_id=1, + prefix="", + first_sim_index=1, + outcome_scenario="high_death_rate", + stoch_traj_flag=False, + ) + diff --git a/flepimop/gempyor_pkg/tests/seir/dev_new_test0.py b/flepimop/gempyor_pkg/tests/seir/dev_new_test0.py new file mode 100644 index 000000000..ec5ad3108 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/dev_new_test0.py @@ -0,0 +1,63 @@ +import numpy as np +import pandas as pd +import os +import pytest +import warnings +import shutil + +import pathlib +import pyarrow as pa +import pyarrow.parquet as pq +import filecmp + +from gempyor import setup, seir, NPI, file_paths, parameters + +from gempyor.utils import config, write_df, read_df +import gempyor + +DATA_DIR = os.path.dirname(__file__) + "/data" +os.chdir(os.path.dirname(__file__)) + + +def test_parameters_from_timeserie_file(): +# if True: + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_compartmental_model_format_with_covariates.yml") + inference_simulator = gempyor.InferenceSimulator( + config_path=f"{DATA_DIR}/config_compartmental_model_format_with_covariates.yml", + run_id=1, + prefix="", + first_sim_index=1, + outcome_scenario="high_death_rate", + stoch_traj_flag=False, + ) + + p = parameters.Parameters( + parameter_config=config["seir"]["parameters"], + ti=config["start_date"].as_date(), + tf=config["end_date"].as_date(), + nodenames=inference_simulator.s.spatset.nodenames, + config_version="v3") + + #p = inference_simulator.s.parameters + p_draw = p.parameters_quick_draw(n_days=inference_simulator.s.n_days, nnodes=inference_simulator.s.nnodes) + + p_df = p.getParameterDF(p_draw)["parameter"] + + for pn in p.pnames: + if pn == "R0s": + assert pn not in p_df + else: + assert pn in p_df + + initial_df = read_df("data/r0s_ts.csv").set_index("date") + + assert (p_draw[p.pnames2pindex["R0s"]] == initial_df.values).all() + + ### test what happen when the order of geoids is not respected (expected: reput them in order) + + ### test what happens with incomplete data (expected: fail) + + ### test what happens when loading from file + # write_df(fname="test_pwrite.parquet", df=p.getParameterDF(p_draw=p_draw)) diff --git a/flepimop/gempyor_pkg/tests/seir/test_setup.py b/flepimop/gempyor_pkg/tests/seir/test_setup.py index c38941e6f..e7e5d3630 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_setup.py +++ b/flepimop/gempyor_pkg/tests/seir/test_setup.py @@ -111,6 +111,7 @@ def test_w_config_seir_exists_success(self): npi_config_seir={}, seeding_config={}, initial_conditions_config={}, + # parameters_config={"alpha":{"value":{"distribution":"fixed","value":.9}}}, parameters_config={}, seir_config=None, outcomes_config={}, @@ -128,7 +129,9 @@ def test_w_config_seir_exists_success(self): ) assert s.seir_config != None - + #print(s.seir_config["parameters"]) + assert s.parameters_config != None + #print(s.integration_method) assert s.integration_method == 'legacy' def test_w_config_seir_integration_method_rk4_1_success(self): @@ -308,6 +311,7 @@ def test_w_config_seir_integration_but_no_dt_success(self): assert s.dt == 2.0 + ''' not needed any longer def test_w_config_seir_old_integration_method_fail(self): with pytest.raises(ValueError, match=r".*Configuration.*no.*longer.*"): # if old method in seir @@ -329,7 +333,6 @@ def test_w_config_seir_old_integration_method_fail(self): ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), ) - def test_w_config_seir_config_version_not_provided_fail(self): with pytest.raises(ValueError, match=r".*Should.*non-specified.*"): # if not seir_config["integration"]["dt"] @@ -358,6 +361,7 @@ def test_w_config_seir_config_version_not_provided_fail(self): seir_config=None, dt=None, # step size, in days ) + ''' def test_w_config_compartments_and_seir_config_not_None_success(self): # if config["compartments"] and iself.seir_config was set From ccaf151f569513b812f5bdb90390dc496dd7fb9a Mon Sep 17 00:00:00 2001 From: kjsato Date: Fri, 8 Sep 2023 11:24:07 -0400 Subject: [PATCH 17/50] added some testing functions --- .../tests/outcomes/test_outcomes.py | 4 +- .../gempyor_pkg/tests/seir/test_seeding_ic.py | 158 ++++++++++++++++++ flepimop/gempyor_pkg/tests/seir/test_seir.py | 136 +++++++++++++++ 3 files changed, 296 insertions(+), 2 deletions(-) create mode 100644 flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py diff --git a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py index b10e97fa6..9cc7cc090 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py +++ b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py @@ -773,8 +773,8 @@ def test_outcomes_read_write_hnpi2_custom_pname(): first_sim_index=1, outcome_scenario="high_death_rate", stoch_traj_flag=False, - out_run_id=107, - ) +out_run_id=107, +) outcomes.onerun_delayframe_outcomes(sim_id2write=1, s=inference_simulator.s, load_ID=True, sim_id2load=1) diff --git a/flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py b/flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py new file mode 100644 index 000000000..4755d0186 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py @@ -0,0 +1,158 @@ +import numpy as np +import os +import pytest +import warnings +import shutil + +import pathlib +import pyarrow as pa +import pyarrow.parquet as pq + +from gempyor import setup, seir, NPI, file_paths, seeding_ic + +from gempyor.utils import config + +DATA_DIR = os.path.dirname(__file__) + "/data" +os.chdir(os.path.dirname(__file__)) + + +class TestSeedingAndIC: + def test_SeedingAndIC_success(self): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config.yml") + + ss = setup.SpatialSetup( + setup_name="test_values", + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + + s = setup.Setup( + setup_name="test_seeding and ic", + spatial_setup=ss, + nslots=1, + npi_scenario="None", + npi_config_seir=config["interventions"]["settings"]["None"], + parameters_config=config["seir"]["parameters"], + seeding_config=config["seeding"], + initial_conditions_config=config["initial_conditions"], + ti=config["start_date"].as_date(), + tf=config["end_date"].as_date(), + interactive=True, + write_csv=False, + dt=0.25, + ) + sic = seeding_ic.SeedingAndIC(seeding_config=s.seeding_config, + initial_conditions_config = s.initial_conditions_config) + assert sic.seeding_config == s.seeding_config + assert sic.initial_conditions_config == s.initial_conditions_config + + def test_SeedingAndIC_allow_missing_node_compartments_success(self): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config.yml") + + ss = setup.SpatialSetup( + setup_name="test_values", + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name="test_seeding and ic", + spatial_setup=ss, + nslots=1, + npi_scenario="None", + npi_config_seir=config["interventions"]["settings"]["None"], + parameters_config=config["seir"]["parameters"], + seeding_config=config["seeding"], + initial_conditions_config=config["initial_conditions"], + ti=config["start_date"].as_date(), + tf=config["end_date"].as_date(), + interactive=True, + write_csv=False, + dt=0.25, + ) + s.initial_conditions_config["allow_missing_nodes"] = True + s.initial_conditions_config["allow_missing_compartments"] = True + sic = seeding_ic.SeedingAndIC(seeding_config=s.seeding_config, + initial_conditions_config = s.initial_conditions_config) + + initial_conditions = sic.draw_ic(sim_id=100, setup=s) + + # print(initial_conditions) + #integration_method = "legacy" + + def test_SeedingAndIC_IC_notImplemented_fail(self): + with pytest.raises(NotImplementedError, match=r".*unknown.*initial.*conditions.*"): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config.yml") + + ss = setup.SpatialSetup( + setup_name="test_values", + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name="test_seeding and ic", + spatial_setup=ss, + nslots=1, + npi_scenario="None", + npi_config_seir=config["interventions"]["settings"]["None"], + parameters_config=config["seir"]["parameters"], + seeding_config=config["seeding"], + initial_conditions_config=config["initial_conditions"], + ti=config["start_date"].as_date(), + tf=config["end_date"].as_date(), + interactive=True, + write_csv=False, + dt=0.25, + ) + s.initial_conditions_config["method"] = "unknown" + sic = seeding_ic.SeedingAndIC(seeding_config=s.seeding_config, + initial_conditions_config = s.initial_conditions_config) + + sic.draw_ic(sim_id=100, setup=s) + + def test_SeedingAndIC_draw_seeding_success(self): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config.yml") + + ss = setup.SpatialSetup( + setup_name="test_values", + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + nodenames_key="geoid", + ) + s = setup.Setup( + setup_name="test_seeding and ic", + spatial_setup=ss, + nslots=1, + npi_scenario="None", + npi_config_seir=config["interventions"]["settings"]["None"], + parameters_config=config["seir"]["parameters"], + seeding_config=config["seeding"], + initial_conditions_config=config["initial_conditions"], + ti=config["start_date"].as_date(), + tf=config["end_date"].as_date(), + interactive=True, + write_csv=False, + dt=0.25, + ) + sic = seeding_ic.SeedingAndIC(seeding_config=s.seeding_config, + initial_conditions_config = s.initial_conditions_config) + s.seeding_config["method"] = "NoSeeding" + + seeding = sic.draw_seeding(sim_id=100, setup=s) + print(seeding) + # print(initial_conditions) + diff --git a/flepimop/gempyor_pkg/tests/seir/test_seir.py b/flepimop/gempyor_pkg/tests/seir/test_seir.py index 2127034ed..4402e8051 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_seir.py +++ b/flepimop/gempyor_pkg/tests/seir/test_seir.py @@ -142,6 +142,142 @@ def test_constant_population_legacy_integration(): assert completepop - 1e-3 < totalpop < completepop + 1e-3 +def test_constant_population_rk4jit_integration_fail(): + with pytest.raises(ValueError, match=r".*with.*method.*integration.*"): + config.set_file(f"{DATA_DIR}/config.yml") + + ss = setup.SpatialSetup( + setup_name="test_seir", + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.txt", + popnodes_key="population", + nodenames_key="geoid", + ) + + first_sim_index = 1 + run_id = "test" + prefix = "" + s = setup.Setup( + setup_name="test_seir", + spatial_setup=ss, + nslots=1, + npi_scenario="None", + npi_config_seir=config["interventions"]["settings"]["None"], + parameters_config=config["seir"]["parameters"], + seeding_config=config["seeding"], + ti=config["start_date"].as_date(), + tf=config["end_date"].as_date(), + interactive=True, + write_csv=False, + first_sim_index=first_sim_index, + in_run_id=run_id, + in_prefix=prefix, + out_run_id=run_id, + out_prefix=prefix, + dt=0.25, + stoch_traj_flag=True + ) + s.integration_method = "rk4.jit" + + seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) + initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) + + npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) + + params = s.parameters.parameters_quick_draw(s.n_days, s.nnodes) + params = s.parameters.parameters_reduce(params, npi) + + ( + unique_strings, + transition_array, + proportion_array, + proportion_info, + ) = s.compartments.get_transition_array() + parsed_parameters = s.compartments.parse_parameters(params, s.parameters.pnames, unique_strings) + + states = seir.steps_SEIR( + s, + parsed_parameters, + transition_array, + proportion_array, + proportion_info, + initial_conditions, + seeding_data, + seeding_amounts, + ) + +def test_constant_population_rk4jit_integration(): + #config.set_file(f"{DATA_DIR}/config.yml") + config.set_file(f"{DATA_DIR}/config_seir_integration_method_rk4_2.yml") + + ss = setup.SpatialSetup( + setup_name="test_seir", + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.txt", + popnodes_key="population", + nodenames_key="geoid", + ) + + first_sim_index = 1 + run_id = "test" + prefix = "" + s = setup.Setup( + setup_name="test_seir", + spatial_setup=ss, + nslots=1, + npi_scenario="None", + npi_config_seir=config["interventions"]["settings"]["None"], + parameters_config=config["seir"]["parameters"], + seeding_config=config["seeding"], + ti=config["start_date"].as_date(), + tf=config["end_date"].as_date(), + interactive=True, + write_csv=False, + first_sim_index=first_sim_index, + in_run_id=run_id, + in_prefix=prefix, + out_run_id=run_id, + out_prefix=prefix, + dt=0.25, + stoch_traj_flag=False + ) + #s.integration_method = "rk4.jit" + assert s.integration_method == "rk4.jit" + + seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) + initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) + + npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) + + params = s.parameters.parameters_quick_draw(s.n_days, s.nnodes) + params = s.parameters.parameters_reduce(params, npi) + + ( + unique_strings, + transition_array, + proportion_array, + proportion_info, + ) = s.compartments.get_transition_array() + parsed_parameters = s.compartments.parse_parameters(params, s.parameters.pnames, unique_strings) + states = seir.steps_SEIR( + s, + parsed_parameters, + transition_array, + proportion_array, + proportion_info, + initial_conditions, + seeding_data, + seeding_amounts, + ) + completepop = s.popnodes.sum() + origpop = s.popnodes + for it in range(s.n_days): + totalpop = 0 + for i in range(s.nnodes): + totalpop += states[0].sum(axis=1)[it, i] + assert states[0].sum(axis=1)[it, i] - 1e-3 < origpop[i] < states[0].sum(axis=1)[it, i] + 1e-3 + assert completepop - 1e-3 < totalpop < completepop + 1e-3 + def test_steps_SEIR_nb_simple_spread_with_txt_matrices(): os.chdir(os.path.dirname(__file__)) config.clear() From 64b22a4712b890fc8b447dab6e3e03a2ecf09cb6 Mon Sep 17 00:00:00 2001 From: kjsato Date: Tue, 12 Sep 2023 12:05:27 -0400 Subject: [PATCH 18/50] added tests/interface/test_interface.py as a new --- flepimop/gempyor_pkg/tests/interface/test_interface.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/flepimop/gempyor_pkg/tests/interface/test_interface.py b/flepimop/gempyor_pkg/tests/interface/test_interface.py index 38ba7a396..e4e0f348d 100644 --- a/flepimop/gempyor_pkg/tests/interface/test_interface.py +++ b/flepimop/gempyor_pkg/tests/interface/test_interface.py @@ -7,7 +7,7 @@ import time import confuse -from gempyor import utils, interface, setup, parameters +from gempyor import utils, interface, seir, setup, parameters from gempyor.utils import config TEST_SETUP_NAME = "minimal_test" @@ -34,6 +34,7 @@ def test_InferenceSimulator_success(self): i.update_prefix("test_newer_in_prefix", "test_newer_out_prefix") assert i.s.in_prefix == "test_newer_in_prefix" assert i.s.out_prefix == "test_newer_out_prefix" + i.update_prefix("", "") i.update_run_id("test_new_run_id") assert i.s.in_run_id == "test_new_run_id" @@ -43,8 +44,10 @@ def test_InferenceSimulator_success(self): assert i.s.in_run_id == "test_newer_in_run_id" assert i.s.out_run_id == "test_newer_out_run_id" + i.update_run_id("test", "test") + # i.one_simulation_legacy(sim_id2write=0) i.build_structure() assert i.already_built - # i.one_simulation(sim_id2write=0) + i.one_simulation(sim_id2write=0) From cbf49a016a219bb035274b52fa10e6f08e26a69e Mon Sep 17 00:00:00 2001 From: kjsato Date: Wed, 13 Sep 2023 09:46:34 -0400 Subject: [PATCH 19/50] initial commit by merging unittest with breaking-improvment --- batch/inference_job_launcher.py | 59 +- datasetup/build_US_setup.R | 39 +- datasetup/build_covid_data.R | 12 +- datasetup/build_flu_data.R | 50 +- datasetup/build_nonUS_setup.R | 4 +- datasetup/usdata/geoid-params.csv | 2 +- .../config.writer/R/create_config_data.R | 660 +- .../config.writer/R/process_npi_list.R | 74 +- .../R_packages/config.writer/R/yaml_utils.R | 156 +- .../config.writer/tests/testthat/geodata.csv | 2 +- .../tests/testthat/outcome_adj.csv | 2 +- .../testthat/processed_intervention_data.csv | 2982 +- .../tests/testthat/sample_config.yml | 4314 ++- .../tests/testthat/test-gen_npi.R | 8 +- .../tests/testthat/test-print_config.R | 4 +- .../tests/testthat/vacc_rates.csv | 2 +- flepimop/R_packages/flepicommon/NAMESPACE | 2 +- flepimop/R_packages/flepicommon/R/DataUtils.R | 34 +- .../flepicommon/R/config_test_new.R | 10 +- .../R_packages/inference/R/documentation.Rmd | 28 +- flepimop/R_packages/inference/R/functions.R | 68 +- .../inference/R/inference_slot_runner_funcs.R | 26 +- .../inference/archive/InferenceTest.R | 96 +- .../test-accept_reject_new_seeding_npis.R | 50 +- .../test-aggregate_and_calc_loc_likelihoods.R | 100 +- .../testthat/test-calc_hierarchical_likadj.R | 34 +- .../tests/testthat/test-perturb_npis.R | 12 +- .../tests/testthat/test-perturb_seeding.R | 4 +- flepimop/gempyor_pkg/docs/Rinterface.Rmd | 22 +- flepimop/gempyor_pkg/docs/Rinterface.html | 46 +- .../docs/integration_benchmark.ipynb | 144 +- .../gempyor_pkg/docs/integration_doc.ipynb | 4 +- flepimop/gempyor_pkg/docs/interface.ipynb | 2 +- flepimop/gempyor_pkg/setup.cfg | 1 + ...uceIntervention.py => ModifierModifier.py} | 38 +- ...tiTimeReduce.py => MultiPeriodModifier.py} | 164 +- .../gempyor_pkg/src/gempyor/NPI/ReduceR0.py | 17 - .../{Reduce.py => SinglePeriodModifier.py} | 54 +- .../NPI/{Stacked.py => StackedModifier.py} | 10 +- flepimop/gempyor_pkg/src/gempyor/NPI/base.py | 6 +- .../gempyor_pkg/src/gempyor/NPI/helpers.py | 24 +- .../gempyor_pkg/src/gempyor/compartments.py | 3 +- .../data/usa-geoid-params-output.parquet | Bin 86209 -> 84637 bytes .../gempyor_pkg/src/gempyor/dev/dev_seir.py | 10 +- flepimop/gempyor_pkg/src/gempyor/dev/steps.py | 100 +- flepimop/gempyor_pkg/src/gempyor/interface.py | 59 +- flepimop/gempyor_pkg/src/gempyor/outcomes.py | 94 +- .../gempyor_pkg/src/gempyor/parameters.py | 191 +- .../gempyor_pkg/src/gempyor/seeding_ic.py | 164 +- flepimop/gempyor_pkg/src/gempyor/seir.py | 36 +- flepimop/gempyor_pkg/src/gempyor/setup.py | 155 +- flepimop/gempyor_pkg/src/gempyor/simulate.py | 417 + .../src/gempyor/simulate_outcome.py | 10 +- .../gempyor_pkg/src/gempyor/simulate_seir.py | 30 +- flepimop/gempyor_pkg/src/gempyor/steps_rk4.py | 12 +- .../gempyor_pkg/src/gempyor/steps_source.py | 14 +- .../src/gempyor/subpopulation_structure.py | 103 + flepimop/gempyor_pkg/src/gempyor/utils.py | 3 +- flepimop/gempyor_pkg/tests/npi/config_npi.yml | 22700 ++++++++-------- .../npi/config_test_spatial_group_npi.yml | 34 +- .../gempyor_pkg/tests/npi/data/geodata.csv | 2 +- .../npi/data/geodata_2019_statelevel.csv | 2 +- flepimop/gempyor_pkg/tests/npi/test_npis.py | 38 +- .../gempyor_pkg/tests/outcomes/config.yml | 2 - .../tests/outcomes/config_load.yml | 4 +- .../tests/outcomes/config_load_subclasses.yml | 4 +- .../tests/outcomes/config_mc_selection.yml | 34 +- .../gempyor_pkg/tests/outcomes/config_npi.yml | 34 +- .../outcomes/config_npi_custom_pnames.yml | 34 +- .../tests/outcomes/config_subclasses.yml | 2 - .../tests/outcomes/data/geodata.csv | 2 +- .../data/usa-geoid-params-output.parquet | Bin 86209 -> 84637 bytes .../tests/outcomes/make_seir_test_file.py | 6 +- .../tests/outcomes/test_outcomes.py | 330 +- .../tests/outcomes/test_rel.parquet | Bin 3567 -> 1996 bytes .../outcomes/test_rel_subclasses.parquet | Bin 3682 -> 2111 bytes .../gempyor_pkg/tests/seir/data/config.yml | 14 +- .../config_compartmental_model_format.yml | 2 - ...artmental_model_format_with_covariates.yml | 2 - .../data/config_compartmental_model_full.yml | 14 +- .../seir/data/config_continuation_resume.yml | 12 +- .../seir/data/config_inference_resume.yml | 20 +- .../tests/seir/data/config_parallel.yml | 20 +- .../tests/seir/data/config_resume.yml | 12 +- .../gempyor_pkg/tests/seir/data/geodata.csv | 2 +- .../gempyor_pkg/tests/seir/dev_new_test.py | 6 +- .../gempyor_pkg/tests/seir/interface.ipynb | 2 +- .../model_output/seed/000000100.test.seed.csv | 2 +- .../seed/000000100.test_SeedOneNode.seed.csv | 2 +- .../seed/000000100.test_parallel.seed.csv | 2 +- .../tests/seir/test_compartments.py | 8 +- .../gempyor_pkg/tests/seir/test_new_seir.py | 8 +- .../gempyor_pkg/tests/seir/test_parameters.py | 38 +- flepimop/gempyor_pkg/tests/seir/test_seir.py | 66 +- flepimop/gempyor_pkg/tests/seir/test_setup.py | 644 +- flepimop/main_scripts/create_seeding.R | 22 +- flepimop/main_scripts/create_seeding_added.R | 20 +- flepimop/main_scripts/inference_slot.R | 44 +- .../main_scripts/seir_init_immuneladder.R | 18 +- postprocessing/groundtruth_source.R | 4 +- postprocessing/plot_predictions.R | 4 +- postprocessing/postprocess_auto.py | 20 +- postprocessing/postprocess_snapshot.R | 124 +- postprocessing/processing_diagnostics.R | 26 +- postprocessing/processing_diagnostics_AWS.R | 26 +- postprocessing/processing_diagnostics_SLURM.R | 26 +- .../run_sim_processing_FluSightExample.R | 6 +- postprocessing/run_sim_processing_SLURM.R | 6 +- postprocessing/run_sim_processing_TEMPLATE.R | 6 +- postprocessing/sim_processing_source.R | 172 +- .../seir_init_immuneladder_r17phase3.R | 17 +- .../seir_init_immuneladder_r17phase3_preOm.R | 18 +- ...nit_immuneladder_r17phase3_preOm_noDelta.R | 17 +- utilities/prune_by_llik.py | 150 +- 114 files changed, 18034 insertions(+), 17563 deletions(-) rename flepimop/gempyor_pkg/src/gempyor/NPI/{ReduceIntervention.py => ModifierModifier.py} (91%) rename flepimop/gempyor_pkg/src/gempyor/NPI/{MultiTimeReduce.py => MultiPeriodModifier.py} (67%) delete mode 100644 flepimop/gempyor_pkg/src/gempyor/NPI/ReduceR0.py rename flepimop/gempyor_pkg/src/gempyor/NPI/{Reduce.py => SinglePeriodModifier.py} (87%) rename flepimop/gempyor_pkg/src/gempyor/NPI/{Stacked.py => StackedModifier.py} (94%) create mode 100644 flepimop/gempyor_pkg/src/gempyor/simulate.py create mode 100644 flepimop/gempyor_pkg/src/gempyor/subpopulation_structure.py diff --git a/batch/inference_job_launcher.py b/batch/inference_job_launcher.py index 0b81c4cb6..f2715a7f7 100755 --- a/batch/inference_job_launcher.py +++ b/batch/inference_job_launcher.py @@ -241,7 +241,7 @@ def user_confirmation(question="Continue?", default=False): "slack_channel", envvar="SLACK_CHANNEL", default="cspproduction", - type=click.Choice(['cspproduction', 'debug', 'noslack']), + type=click.Choice(["cspproduction", "debug", "noslack"]), help="Slack channel, either 'csp-production' or 'debug', or 'noslack' to disable slack", ) @click.option( @@ -331,22 +331,26 @@ def launch_batch( print(f"WARNING: no inference section found in {config_file}!") if "s3://" in str(restart_from_location): # ugly hack: str because it might be None - restart_from_run_id = aws_countfiles_autodetect_runid(s3_bucket=s3_bucket, - restart_from_location=restart_from_location, - restart_from_run_id=restart_from_run_id, - num_jobs=num_jobs, - strict=False) + restart_from_run_id = aws_countfiles_autodetect_runid( + s3_bucket=s3_bucket, + restart_from_location=restart_from_location, + restart_from_run_id=restart_from_run_id, + num_jobs=num_jobs, + strict=False, + ) else: if restart_from_run_id is None and restart_from_location is not None: raise Exception( "No auto-detection of run_id from local folder, please specify --restart_from_run_id (or fixme)" ) if "s3://" in str(continuation_location): - continuation_run_id = aws_countfiles_autodetect_runid(s3_bucket=s3_bucket, - restart_from_location=continuation_location, - restart_from_run_id=continuation_run_id, - num_jobs=num_jobs, - strict=True) + continuation_run_id = aws_countfiles_autodetect_runid( + s3_bucket=s3_bucket, + restart_from_location=continuation_location, + restart_from_run_id=continuation_run_id, + num_jobs=num_jobs, + strict=True, + ) else: if continuation_run_id is None and continuation_location is not None: raise Exception( @@ -355,9 +359,9 @@ def launch_batch( if continuation and continuation_location is None: continuation_location = restart_from_location continuation_run_id = restart_from_run_id - print("Continuation enabled but no continuation location provided. Assuming that continuation location is the same as resume location") - - + print( + "Continuation enabled but no continuation location provided. Assuming that continuation location is the same as resume location" + ) handler = BatchJobHandler( batch_system, @@ -421,13 +425,13 @@ def autodetect_params(config, data_path, *, num_jobs=None, sims_per_job=None, nu print(f"Setting number of blocks to {num_blocks} [via num_blocks (-k) argument]") print(f"Setting sims per job to {sims_per_job} [via {iterations_per_slot} iterations_per_slot in config]") else: - geoid_fname = pathlib.Path(data_path, config["data_path"]) / config["spatial_setup"]["geodata"] - with open(geoid_fname) as geoid_fp: - num_geoids = sum(1 for line in geoid_fp) + geodata_fname = pathlib.Path(data_path, config["data_path"]) / config["spatial_setup"]["geodata"] + with open(geodata_fname) as geodata_fp: + num_subpops = sum(1 for line in geodata_fp) if batch_system == "aws": - # formula based on a simple regression of geoids (based on known good performant params) - sims_per_job = max(60 - math.sqrt(num_geoids), 10) + # formula based on a simple regression of subpops (based on known good performant params) + sims_per_job = max(60 - math.sqrt(num_subpops), 10) sims_per_job = 5 * int(math.ceil(sims_per_job / 5)) # multiple of 5 num_blocks = int(math.ceil(iterations_per_slot / sims_per_job)) elif batch_system == "slurm" or batch_system == "local": @@ -439,7 +443,7 @@ def autodetect_params(config, data_path, *, num_jobs=None, sims_per_job=None, nu print( f"Setting sims per job to {sims_per_job} " - f"[estimated based on {num_geoids} geoids and {iterations_per_slot} iterations_per_slot in config]" + f"[estimated based on {num_subpops} subpop(s) and {iterations_per_slot} iterations_per_slot in config]" ) print(f"Setting number of blocks to {num_blocks} [via math]") @@ -484,7 +488,7 @@ def aws_countfiles_autodetect_runid(s3_bucket, restart_from_location, restart_fr final_llik = [f for f in all_files if ("llik" in f) and ("final" in f)] if len(final_llik) == 0: # hacky: there might be a bucket with no llik files, e.g if init. - final_llik = [f for f in all_files if ("init" in f) and ("final" in f)] + final_llik = [f for f in all_files if ("init" in f) and ("final" in f)] if len(final_llik) != num_jobs: if strict: @@ -497,7 +501,7 @@ def aws_countfiles_autodetect_runid(s3_bucket, restart_from_location, restart_fr ) if (num_jobs - len(final_llik)) > 50: user_confirmation(question=f"Difference > 50. Should we continue ?") - + return restart_from_run_id @@ -720,8 +724,13 @@ def launch(self, job_name, config_file, npi_scenarios, outcome_scenarios): cur_env_vars.append({"name": "FLEPI_CONTINUATION", "value": f"TRUE"}) cur_env_vars.append({"name": "FLEPI_CONTINUATION_RUN_ID", "value": f"{self.continuation_run_id}"}) cur_env_vars.append({"name": "FLEPI_CONTINUATION_LOCATION", "value": f"{self.continuation_location}"}) - cur_env_vars.append({"name": "FLEPI_CONTINUATION_FTYPE", "value": f"{config['initial_conditions']['initial_file_type']}"}) - + cur_env_vars.append( + { + "name": "FLEPI_CONTINUATION_FTYPE", + "value": f"{config['initial_conditions']['initial_file_type']}", + } + ) + # First job: if self.batch_system == "aws": cur_env_vars.append({"name": "JOB_NAME", "value": f"{cur_job_name}_block0"}) @@ -816,8 +825,6 @@ def launch(self, job_name, config_file, npi_scenarios, outcome_scenarios): print(f"""export {envar["name"]}="{envar["value"]}" """) print(f"--- end env var to set ---") - - # On aws: create all other jobs + the copy job. slurm script is only one block and copies itself at the end. if self.batch_system == "aws": block_idx = 1 diff --git a/datasetup/build_US_setup.R b/datasetup/build_US_setup.R index 974052c61..d15befb14 100644 --- a/datasetup/build_US_setup.R +++ b/datasetup/build_US_setup.R @@ -12,7 +12,6 @@ # modeled_states: e.g. MD, CA, NY # mobility: optional; default is 'mobility.csv' # geodata: optional; default is 'geodata.csv' -# popnodes: optional; default is 'population' # # importation: # census_api_key: default is environment variable CENSUS_API_KEY. Environment variable is preferred so you don't accidentally commit your key. @@ -84,25 +83,25 @@ census_data <- tidycensus::get_acs(geography="county", state=filterUSPS, variables="B01003_001", year=config$spatial_setup$census_year, keep_geo_vars=TRUE, geometry=FALSE, show_call=TRUE) census_data <- census_data %>% - dplyr::rename(population=estimate, geoid=GEOID) %>% - dplyr::select(geoid, population) %>% - dplyr::mutate(geoid = substr(geoid,1,5)) + dplyr::rename(population=estimate, subpop=GEOID) %>% + dplyr::select(subpop, population) %>% + dplyr::mutate(subpop = substr(subpop,1,5)) # Add USPS column data(fips_codes) -fips_geoid_codes <- dplyr::mutate(fips_codes, geoid=paste0(state_code,county_code)) %>% - dplyr::group_by(geoid) %>% +fips_subpop_codes <- dplyr::mutate(fips_codes, subpop=paste0(state_code,county_code)) %>% + dplyr::group_by(subpop) %>% dplyr::summarize(USPS=unique(state)) -census_data <- dplyr::left_join(census_data, fips_geoid_codes, by="geoid") +census_data <- dplyr::left_join(census_data, fips_subpop_codes, by="subpop") # Make each territory one county. # Puerto Rico is the only one in the 2018 ACS estimates right now. Aggregate it. # Keeping the other territories in the aggregation just in case they're there in the future. name_changer <- setNames( - unique(census_data$geoid), - unique(census_data$geoid) + unique(census_data$subpop), + unique(census_data$subpop) ) name_changer[grepl("^60",name_changer)] <- "60000" # American Samoa name_changer[grepl("^66",name_changer)] <- "66000" # Guam @@ -111,8 +110,8 @@ name_changer[grepl("^72",name_changer)] <- "72000" # Puerto Rico name_changer[grepl("^78",name_changer)] <- "78000" # Virgin Islands census_data <- census_data %>% - dplyr::mutate(geoid = name_changer[geoid]) %>% - dplyr::group_by(geoid) %>% + dplyr::mutate(subpop = name_changer[subpop]) %>% + dplyr::group_by(subpop) %>% dplyr::summarize(USPS = unique(USPS), population = sum(population)) @@ -127,8 +126,8 @@ census_data <- terr_census_data %>% # State-level aggregation if desired if (state_level){ census_data <- census_data %>% - dplyr::mutate(geoid = as.character(paste0(substr(geoid,1,2), "000"))) %>% - dplyr::group_by(USPS, geoid) %>% + dplyr::mutate(subpop = as.character(paste0(substr(subpop,1,2), "000"))) %>% + dplyr::group_by(USPS, subpop) %>% dplyr::summarise(population=sum(population, na.rm=TRUE)) %>% tibble::as_tibble() } @@ -170,7 +169,7 @@ if(state_level & !file.exists(paste0(config$data_path, "/", config$spatial_setup commute_data <- commute_data %>% dplyr::mutate(OFIPS = substr(OFIPS,1,5), DFIPS = substr(DFIPS,1,5)) %>% dplyr::mutate(OFIPS = name_changer[OFIPS], DFIPS = name_changer[DFIPS]) %>% - dplyr::filter(OFIPS %in% census_data$geoid, DFIPS %in% census_data$geoid) %>% + dplyr::filter(OFIPS %in% census_data$subpop, DFIPS %in% census_data$subpop) %>% dplyr::group_by(OFIPS,DFIPS) %>% dplyr::summarize(FLOW = sum(FLOW)) %>% dplyr::filter(OFIPS != DFIPS) @@ -185,19 +184,19 @@ if(state_level & !file.exists(paste0(config$data_path, "/", config$spatial_setup if(endsWith(mobility_file, '.txt')) { - # Pads 0's for every geoid and itself, so that nothing gets dropped on the pivot + # Pads 0's for every subpop and itself, so that nothing gets dropped on the pivot padding_table <- tibble::tibble( - OFIPS = census_data$geoid, - DFIPS = census_data$geoid, + OFIPS = census_data$subpop, + DFIPS = census_data$subpop, FLOW = 0 ) rc <- dplyr::bind_rows(padding_table, commute_data) %>% - dplyr::arrange(match(OFIPS, census_data$geoid), match(DFIPS, census_data$geoid)) %>% + dplyr::arrange(match(OFIPS, census_data$subpop), match(DFIPS, census_data$subpop)) %>% tidyr::pivot_wider(OFIPS,names_from=DFIPS,values_from=FLOW, values_fill=c("FLOW"=0),values_fn = list(FLOW=sum)) - if(!isTRUE(all(rc$OFIPS == census_data$geoid))){ + if(!isTRUE(all(rc$OFIPS == census_data$subpop))){ print(rc$OFIPS) - print(census_data$geoid) + print(census_data$subpop) stop("There was a problem generating the mobility matrix") } write.table(file = file.path(outdir, mobility_file), as.matrix(rc[,-1]), row.names=FALSE, col.names = FALSE, sep = " ") diff --git a/datasetup/build_covid_data.R b/datasetup/build_covid_data.R index 2aa339315..d44d8b1bd 100644 --- a/datasetup/build_covid_data.R +++ b/datasetup/build_covid_data.R @@ -49,12 +49,12 @@ source(file.path(opt$path, "datasetup/data_setup_source.R")) # SET DELPHI API KEY ------------------------------------------------------ if (any(grepl("nchs|hhs", opt$gt_data_source))){ - if (!is.null(opt$delphi_api_key)){ - cat(paste0("Using Environment variable for Delphi API key: ", opt$delphi_api_key)) - options(covidcast.auth = opt$delphi_api_key) - } else if (!is.null(config$inference$gt_api_key)){ + if (!is.null(config$inference$gt_api_key)){ cat(paste0("Using Config variable for Delphi API key: ", config$inference$gt_api_key)) options(covidcast.auth = config$inference$gt_api_key) + } else if (!is.null(opt$delphi_api_key)){ + cat(paste0("Using Environment variable for Delphi API key: ", opt$delphi_api_key)) + options(covidcast.auth = opt$delphi_api_key) } else { newkey <- readline(prompt = "Please enter your Delphi API key before proceeding:") #check @@ -220,7 +220,7 @@ if (any(grepl("fluview", opt$gt_data_source))){ census_data <- read_csv(file = file.path(config$data_path, config$spatial_setup$geodata)) fluview_data <- fluview_data %>% - dplyr::inner_join(census_data %>% dplyr::select(source = USPS, FIPS = geoid)) %>% + dplyr::inner_join(census_data %>% dplyr::select(source = USPS, FIPS = subpop)) %>% dplyr::select(Update, source, FIPS, incidD) @@ -285,7 +285,7 @@ if (any(grepl("fluview", opt$gt_data_source))){ # # census_data <- read_csv(file = file.path(config$data_path, config$spatial_setup$geodata)) # fluview_data <- fluview_data %>% -# left_join(census_data %>% dplyr::select(source = USPS, FIPS = geoid)) %>% +# left_join(census_data %>% dplyr::select(source = USPS, FIPS = subpop)) %>% # dplyr::select(Update, source, FIPS, incidD) # # diff --git a/datasetup/build_flu_data.R b/datasetup/build_flu_data.R index 34fb5d59c..f44ea0568 100644 --- a/datasetup/build_flu_data.R +++ b/datasetup/build_flu_data.R @@ -65,14 +65,14 @@ locs <- read_csv(file.path(config$data_path, config$spatial_setup$geodata)) us_data <- us_data %>% mutate(location = stringr::str_pad(location, width = 2, side = "left", pad = "0")) -us_data <- us_data %>% +us_data <- us_data %>% filter(location != "US") %>% mutate(location = stringr::str_pad(location, width=5, side="right", pad="0")) %>% - left_join(locs, by = c("location"="geoid")) %>% - rename(FIPS = location, + left_join(locs, by = c("location"="subpop")) %>% + rename(FIPS = location, incidH = value, source = USPS) %>% - select(-location_name, -pop2019est) + select(-location_name, -population) # Filter to dates we care about for speed and space end_date_ <- config$end_date_groundtruth @@ -98,24 +98,24 @@ variant_props_file <- config$seeding$variant_filename adjust_for_variant <- !is.null(variant_props_file) # if (adjust_for_variant){ -# +# # # Variant Data (need to automate this data pull still) # #variant_data <- read_csv(file.path(config$data_path, "variant/WHO_NREVSS_Clinical_Labs.csv"), skip = 1) # variant_data <- cdcfluview::who_nrevss(region="state", years = 2022)$clinical_labs -# +# # # location data # loc_data <- read_csv("data-locations/locations.csv") -# -# +# +# # # CLEAN DATA -# +# # variant_data <- variant_data %>% # select(state = region, # week = week, # year = year, # FluA = total_a, # FluB = total_b) %>% -# # select(state = REGION, +# # select(state = REGION, # # week = WEEK, # # year = YEAR, # # FluA = `TOTAL A`, @@ -145,14 +145,14 @@ adjust_for_variant <- !is.null(variant_props_file) # mutate(prop = ifelse(is.na(prop), 0, prop)) %>% # filter(!is.na(week_end)) %>% # filter(week_end <= as_date(end_date_)) -# +# # variant_data <- variant_data %>% # left_join(loc_data %>% select(state = location_name, source = abbreviation)) %>% # mutate(week = epiweek(week_end), year = epiyear(week_end)) -# +# # if(end_date_ != max(variant_data$week_end)){ # # Extend to dates of groundtruth -# var_max_dates <- variant_data %>% +# var_max_dates <- variant_data %>% # group_by(source, state) %>% # filter(week_end == max(week_end)) %>% # ungroup() %>% @@ -164,50 +164,50 @@ adjust_for_variant <- !is.null(variant_props_file) # ungroup() # var_max_dates <- var_max_dates %>% # rename(max_current = week_end) %>% -# mutate(week_end = strsplit(as.character(weeks_missing), ",")) %>% +# mutate(week_end = strsplit(as.character(weeks_missing), ",")) %>% # unnest(week_end) %>% # select(state, week, year, variant, prop, week_end, source) %>% # mutate(week_end = as_date(week_end)) # variant_data <- variant_data %>% # bind_rows(var_max_dates) # } -# +# # variant_data <- variant_data %>% # mutate(week = epiweek(week_end), year = epiyear(week_end)) -# +# # variant_data <- variant_data %>% # expand_grid(day = 1:7) %>% # mutate(date = as_date(MMWRweek::MMWRweek2Date(year, week, day))) %>% # select(c(variant, prop, source, date)) -# -# variant_data <- variant_data %>% +# +# variant_data <- variant_data %>% # filter(date >= as_date(config$start_date) & date <= as_date(config$end_date_groundtruth)) -# +# # write_csv(variant_data, variant_props_file) # } -# +# # APPLY VARIANTS ---------------------------------------------------------- if (adjust_for_variant) { - + us_data <- read_csv(config$inference$gt_data_path) - + tryCatch({ us_data <- flepicommon::do_variant_adjustment(us_data, variant_props_file) - us_data <- us_data %>% + us_data <- us_data %>% filter(date >= as_date(config$start_date) & date <= as_date(config$end_date_groundtruth)) write_csv(us_data, config$inference$gt_data_path) }, error = function(e) { - stop(paste0("Could not use variant file |", variant_props_file, + stop(paste0("Could not use variant file |", variant_props_file, "|, with error message", e$message)) }) } -cat(paste0("Ground truth data saved\n", +cat(paste0("Ground truth data saved\n", " -- file: ", config$inference$gt_data_path,".\n", " -- outcomes: ", paste(grep("incid", colnames(us_data), value = TRUE), collapse = ", "))) diff --git a/datasetup/build_nonUS_setup.R b/datasetup/build_nonUS_setup.R index ef32b9c80..60926450d 100644 --- a/datasetup/build_nonUS_setup.R +++ b/datasetup/build_nonUS_setup.R @@ -12,8 +12,6 @@ # modeled_states: e.g. ZMB, BGD, CAN # mobility: optional; default is 'mobility.csv' # geodata: optional; default is 'geodata.csv' -# popnodes: optional; default is 'pop' -# # # ## Input Data # @@ -107,7 +105,7 @@ if(opt$w){ } # Save population geodata -names(census_data) <- c("geoid","admin2","admin0","pop") +names(census_data) <- c("subpop","admin2","admin0","pop") write.csv(file = file.path(outdir,'geodata.csv'), census_data,row.names=FALSE) print("Census Data Check (up to 6 rows)") diff --git a/datasetup/usdata/geoid-params.csv b/datasetup/usdata/geoid-params.csv index e6593b927..5db66a0b0 100644 --- a/datasetup/usdata/geoid-params.csv +++ b/datasetup/usdata/geoid-params.csv @@ -1,4 +1,4 @@ -geoid,parameter,value +subpop,parameter,value 01001,p_symp_inf,0.48210587170307384 01003,p_symp_inf,0.5085175350771249 01005,p_symp_inf,0.4955007483173164 diff --git a/flepimop/R_packages/config.writer/R/create_config_data.R b/flepimop/R_packages/config.writer/R/create_config_data.R index 7388ce5d6..4c7a796b7 100644 --- a/flepimop/R_packages/config.writer/R/create_config_data.R +++ b/flepimop/R_packages/config.writer/R/create_config_data.R @@ -4,7 +4,7 @@ #' #' @param sim_start_date simulation start date #' @param sim_end_date simulation end date -#' @param incl_geoid +#' @param incl_subpop #' @param v_dist type of distribution for reduction #' @param v_mean reduction mean #' @param v_sd reduction sd @@ -16,7 +16,7 @@ #' @param p_sd perturbation sd #' @param p_a perturbation a #' @param p_b perturbation b -#' @param compartment +#' @param compartment #' #' @return data frame with columns for #' @export @@ -28,7 +28,7 @@ #' set_incidH_params <- function(start_date=Sys.Date()-42, sim_end_date=Sys.Date()+60, - incl_geoid = NULL, + incl_subpop = NULL, inference = TRUE, v_dist="truncnorm", v_mean = 0, v_sd = 0.1, v_a = -1, v_b = 1, # TODO: add check on limits @@ -37,19 +37,19 @@ set_incidH_params <- function(start_date=Sys.Date()-42, ){ start_date <- as.Date(start_date) sim_end_date <- as.Date(sim_end_date) - - template = "Reduce" + + template = "SinglePeriodModifier" param_val <- "incidH::probability" - - if(is.null(incl_geoid)){ - affected_geoids = "all" + + if(is.null(incl_subpop)){ + affected_subpop = "all" } else{ - affected_geoids = paste0(incl_geoid, collapse='", "') + affected_subpop = paste0(incl_subpop, collapse='", "') } - - + + local_var <- dplyr::tibble(USPS = "", - geoid = affected_geoids, + subpop = affected_subpop, name = "incidH_adj", type = "outcome", category = "incidH_adjustment", @@ -71,8 +71,8 @@ set_incidH_params <- function(start_date=Sys.Date()-42, pert_b = p_b) %>% dplyr::mutate(pert_dist = ifelse(inference, as.character(pert_dist), NA_character_), dplyr::across(pert_mean:pert_b, ~ifelse(inference, as.numeric(.x), NA_real_))) %>% - dplyr::select(USPS, geoid, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) - + dplyr::select(USPS, subpop, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) + return(local_var) } @@ -82,7 +82,7 @@ set_incidH_params <- function(start_date=Sys.Date()-42, #' @param sim_start_date simulation start date #' @param sim_end_date simulation end date #' @param npi_cutoff_date only interventions that start before or on npi_cuttof_date are included -#' @param redux_geoids string or vector of characters indicating which geoids will have an intervention with the ReduceIntervention template; it accepts "all". If any values are specified, the intervention in the geoid with the maximum start date will be selected. It defaults to NULL. . +#' @param redux_subpop string or vector of characters indicating which subpop will have an intervention with the ModifierModifier template; it accepts "all". If any values are specified, the intervention in the subpop with the maximum start date will be selected. It defaults to NULL. . #' @param v_dist type of distribution for reduction #' @param v_mean reduction mean #' @param v_sd reduction sd @@ -111,20 +111,20 @@ set_npi_params_old <- function(intervention_file, sim_end_date=Sys.Date()+60, npi_cutoff_date=Sys.Date()-7, inference = TRUE, - redux_geoids = NULL, + redux_subpop = NULL, v_dist = "truncnorm", v_mean=0.6, v_sd=0.05, v_a=0.0, v_b=0.9, p_dist = "truncnorm", p_mean=0, p_sd=0.05, p_a=-1, p_b=1, compartment = TRUE){ - + param_val <- ifelse(compartment, "r0", "R0") sim_start_date <- lubridate::ymd(sim_start_date) sim_end_date <- lubridate::ymd(sim_end_date) npi_cuttoff_date <- lubridate::ymd(npi_cutoff_date) - + npi <- intervention_file %>% dplyr::filter(start_date <= npi_cutoff_date) %>% dplyr::filter(start_date >= sim_start_date | end_date > sim_start_date) %>% # add warning about npi period <7 days? - dplyr::group_by(USPS, geoid) %>% + dplyr::group_by(USPS, subpop) %>% dplyr::mutate(end_date = dplyr::case_when(is.na(end_date) | end_date == max(end_date) | end_date > sim_end_date ~ sim_end_date, TRUE ~ end_date), @@ -141,44 +141,44 @@ set_npi_params_old <- function(intervention_file, type = "transmission", category = "NPI", baseline_scenario = "", - parameter = dplyr::if_else(template=="MultiTimeReduce", param_val, NA_character_) + parameter = dplyr::if_else(template=="MultiPeriodModifier", param_val, NA_character_) ) - + if(any(stringr::str_detect(npi$name, "^\\d$"))) stop("Intervention names must include at least one non-numeric character.") - + npi <- npi %>% dplyr::mutate(dplyr::across(pert_mean:pert_b, ~ifelse(inference, .x, NA_real_)), pert_dist = ifelse(inference, pert_dist, NA_character_)) %>% - dplyr::select(USPS, geoid, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) - - if(!is.null(redux_geoids)){ - if(redux_geoids == 'all'){ - redux_geoids <- unique(npi$geoid) + dplyr::select(USPS, subpop, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) + + if(!is.null(redux_subpop)){ + if(redux_subpop == 'all'){ + redux_subpop <- unique(npi$subpop) } - + npi <- npi %>% - dplyr::filter(geoid %in% redux_geoids) %>% - dplyr::group_by(geoid) %>% + dplyr::filter(subpop %in% redux_subpop) %>% + dplyr::group_by(subpop) %>% dplyr::filter(start_date == max(start_date)) %>% dplyr::mutate(category = "base_npi", name = paste0(name, "_last")) %>% dplyr::bind_rows( npi %>% - dplyr::group_by(geoid) %>% - dplyr::filter(start_date != max(start_date) |! geoid %in% redux_geoids) + dplyr::group_by(subpop) %>% + dplyr::filter(start_date != max(start_date) |! subpop %in% redux_subpop) ) %>% dplyr::ungroup() } - + npi <- npi %>% dplyr::ungroup() %>% dplyr::add_count(name) %>% - dplyr::mutate(template = dplyr::if_else(n==1 & template == "MultiTimeReduce", "Reduce", template), - parameter = dplyr::if_else(n==1 & template == "Reduce", param_val, parameter)) %>% + dplyr::mutate(template = dplyr::if_else(n==1 & template == "MultiPeriodModifier", "SinglePeriodModifier", template), + parameter = dplyr::if_else(n==1 & template == "SinglePeriodModifier", param_val, parameter)) %>% dplyr::select(-n) - + return(npi) - + } @@ -189,7 +189,7 @@ set_npi_params_old <- function(intervention_file, #' @param sim_start_date simulation start date #' @param sim_end_date simulation end date #' @param npi_cutoff_date only interventions that start before or on npi_cuttof_date are included -#' @param redux_geoids string or vector of characters indicating which geoids will have an intervention with the ReduceIntervention template; it accepts "all". If any values are specified, the intervention in the geoid with the maximum start date will be selected. It defaults to NULL. . +#' @param redux_subpop string or vector of characters indicating which subpop will have an intervention with the ModifierModifier template; it accepts "all". If any values are specified, the intervention in the subpop with the maximum start date will be selected. It defaults to NULL. . #' @param v_dist type of distribution for reduction #' @param v_mean reduction mean #' @param v_sd reduction sd @@ -213,47 +213,47 @@ set_npi_params_old <- function(intervention_file, #' #' npi_dat <- set_npi_params(intervention_file = npi_dat, sim_start_date = "2020-01-15", sim_end_date = "2021-07-30") #' -set_npi_params <- function (intervention_file, sim_start_date = as.Date("2020-01-31"), - sim_end_date = Sys.Date() + 60, npi_cutoff_date = Sys.Date() - 7, - inference = TRUE, redux_geoids = NULL, v_dist = "truncnorm", - v_mean = 0.6, v_sd = 0.05, v_a = 0, v_b = 0.9, p_dist = "truncnorm", +set_npi_params <- function (intervention_file, sim_start_date = as.Date("2020-01-31"), + sim_end_date = Sys.Date() + 60, npi_cutoff_date = Sys.Date() - 7, + inference = TRUE, redux_subpop = NULL, v_dist = "truncnorm", + v_mean = 0.6, v_sd = 0.05, v_a = 0, v_b = 0.9, p_dist = "truncnorm", p_mean = 0, p_sd = 0.05, p_a = -1, p_b = 1, compartment = TRUE) { - + param_val <- ifelse(compartment, "r0", "R0") sim_start_date <- lubridate::ymd(sim_start_date) sim_end_date <- lubridate::ymd(sim_end_date) npi_cuttoff_date <- lubridate::ymd(npi_cutoff_date) - npi <- intervention_file %>% - dplyr::filter(start_date <= npi_cutoff_date) %>% - dplyr::filter(start_date >= sim_start_date | end_date > sim_start_date | is.na(end_date)) %>% - dplyr::group_by(USPS, geoid) %>% - dplyr::mutate(end_date = dplyr::case_when(is.na(end_date) | end_date == max(end_date) | end_date > sim_end_date ~ sim_end_date, TRUE ~ end_date), - value_dist = v_dist, - value_mean = v_mean, value_sd = v_sd, value_a = v_a, - value_b = v_b, pert_dist = p_dist, pert_mean = p_mean, - pert_sd = p_sd, pert_a = p_a, pert_b = p_b, type = "transmission", - category = "NPI", baseline_scenario = "", parameter = dplyr::if_else(template == "MultiTimeReduce", param_val, NA_character_)) - if (any(stringr::str_detect(npi$name, "^\\d$"))) + npi <- intervention_file %>% + dplyr::filter(start_date <= npi_cutoff_date) %>% + dplyr::filter(start_date >= sim_start_date | end_date > sim_start_date | is.na(end_date)) %>% + dplyr::group_by(USPS, subpop) %>% + dplyr::mutate(end_date = dplyr::case_when(is.na(end_date) | end_date == max(end_date) | end_date > sim_end_date ~ sim_end_date, TRUE ~ end_date), + value_dist = v_dist, + value_mean = v_mean, value_sd = v_sd, value_a = v_a, + value_b = v_b, pert_dist = p_dist, pert_mean = p_mean, + pert_sd = p_sd, pert_a = p_a, pert_b = p_b, type = "transmission", + category = "NPI", baseline_scenario = "", parameter = dplyr::if_else(template == "MultiPeriodModifier", param_val, NA_character_)) + if (any(stringr::str_detect(npi$name, "^\\d$"))) stop("Intervention names must include at least one non-numeric character.") - npi <- npi %>% dplyr::mutate(dplyr::across(pert_mean:pert_b, ~ifelse(inference, .x, NA_real_)), pert_dist = ifelse(inference, pert_dist, NA_character_)) %>% - dplyr::select(USPS, geoid, - start_date, end_date, name, template, type, category, - parameter, baseline_scenario, tidyselect::starts_with("value_"), + npi <- npi %>% dplyr::mutate(dplyr::across(pert_mean:pert_b, ~ifelse(inference, .x, NA_real_)), pert_dist = ifelse(inference, pert_dist, NA_character_)) %>% + dplyr::select(USPS, subpop, + start_date, end_date, name, template, type, category, + parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) - if (!is.null(redux_geoids)) { - if (redux_geoids == "all") { - redux_geoids <- unique(npi$geoid) + if (!is.null(redux_subpop)) { + if (redux_subpop == "all") { + redux_subpop <- unique(npi$subpop) } - npi <- npi %>% dplyr::filter(geoid %in% redux_geoids) %>% - dplyr::group_by(geoid) %>% dplyr::filter(start_date == max(start_date)) %>% - dplyr::mutate(category = "base_npi", name = paste0(name, "_last")) %>% - dplyr::bind_rows(npi %>% dplyr::group_by(geoid) %>% dplyr::filter(start_date != max(start_date) | !geoid %in% redux_geoids)) %>% + npi <- npi %>% dplyr::filter(subpop %in% redux_subpop) %>% + dplyr::group_by(subpop) %>% dplyr::filter(start_date == max(start_date)) %>% + dplyr::mutate(category = "base_npi", name = paste0(name, "_last")) %>% + dplyr::bind_rows(npi %>% dplyr::group_by(subpop) %>% dplyr::filter(start_date != max(start_date) | !subpop %in% redux_subpop)) %>% dplyr::ungroup() } - npi <- npi %>% dplyr::ungroup() %>% - dplyr::add_count(name) %>% - dplyr::mutate(template = dplyr::if_else(n == 1 & template == "MultiTimeReduce", "Reduce", template), - parameter = dplyr::if_else(n == 1 & template == "Reduce", param_val, parameter)) %>% + npi <- npi %>% dplyr::ungroup() %>% + dplyr::add_count(name) %>% + dplyr::mutate(template = dplyr::if_else(n == 1 & template == "MultiPeriodModifier", "SinglePeriodModifier", template), + parameter = dplyr::if_else(n == 1 & template == "SinglePeriodModifier", param_val, parameter)) %>% dplyr::select(-n) return(npi) } @@ -294,21 +294,21 @@ set_npi_params <- function (intervention_file, sim_start_date = as.Date("2020-01 set_seasonality_params <- function(sim_start_date=as.Date("2020-03-31"), sim_end_date=Sys.Date()+60, inference = TRUE, - template = "MultiTimeReduce", + template = "MultiPeriodModifier", v_dist="truncnorm", v_mean = c(-0.2, -0.133, -0.067, 0, 0.067, 0.133, 0.2, 0.133, 0.067, 0, -0.067, -0.133), # TODO function? v_sd = 0.05, v_a = -1, v_b = 1, p_dist="truncnorm", p_mean = 0, p_sd = 0.05, p_a = -1, p_b = 1, compartment = TRUE){ - + sim_start_date <- as.Date(sim_start_date) sim_end_date <- as.Date(sim_end_date) - + param_val <- ifelse(compartment, "r0", "R0") - + years_ <- unique(lubridate::year(seq(sim_start_date, sim_end_date, 1))) - + seas <- tidyr::expand_grid( tidyr::tibble(month= tolower(month.abb), month_num = 1:12, @@ -333,7 +333,7 @@ set_seasonality_params <- function(sim_start_date=as.Date("2020-03-31"), category = "seasonal", template = template, baseline_scenario = "", - geoid = "all", + subpop = "all", name = paste0("Seas_", month), pert_dist = ifelse(inference, as.character(pert_dist), NA_character_), dplyr::across(pert_sd:pert_a, ~ifelse(inference, as.numeric(.x), NA_real_)) @@ -343,12 +343,12 @@ set_seasonality_params <- function(sim_start_date=as.Date("2020-03-31"), lubridate::ceiling_date(end_date, "months") <= lubridate::ceiling_date(sim_end_date, "months") ) %>% dplyr::add_count(name) %>% - dplyr::mutate(template = dplyr::if_else(n > 1, template, "Reduce"), + dplyr::mutate(template = dplyr::if_else(n > 1, template, "SinglePeriodModifier"), end_date = dplyr::if_else(end_date > sim_end_date, sim_end_date, end_date), start_date = dplyr::if_else(start_date < sim_start_date, sim_start_date, start_date) ) %>% - dplyr::select(USPS, geoid, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) - + dplyr::select(USPS, subpop, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) + return(seas) } @@ -367,7 +367,7 @@ set_seasonality_params <- function(sim_start_date=as.Date("2020-03-31"), #' @param p_sd perturbation sd #' @param p_a perturbation a #' @param p_b perturbation b -#' @param compartment +#' @param compartment #' #' @return data frame with columns for #' @export @@ -383,18 +383,18 @@ set_localvar_params <- function(sim_start_date=as.Date("2020-03-31"), v_dist="truncnorm", v_mean = 0, v_sd = 0.05, v_a = -1, v_b = 1, # TODO: add check on limits p_dist="truncnorm", - p_mean = 0, p_sd = 0.05, p_a = -1, p_b = 1, + p_mean = 0, p_sd = 0.05, p_a = -1, p_b = 1, compartment = TRUE ){ sim_start_date <- as.Date(sim_start_date) sim_end_date <- as.Date(sim_end_date) - - template = "Reduce" + + template = "SinglePeriodModifier" param_val <- ifelse(compartment, "r0", "R0") - affected_geoids = "all" - + affected_subpop = "all" + local_var <- dplyr::tibble(USPS = "", - geoid = "all", + subpop = "all", name = "local_variance", type = "transmission", category = "local_variance", @@ -404,7 +404,7 @@ set_localvar_params <- function(sim_start_date=as.Date("2020-03-31"), end_date = sim_end_date, template = template, param = param_val, - affected_geoids = affected_geoids, + affected_subpop = affected_subpop, value_dist = v_dist, value_mean = v_mean, value_sd = v_sd, @@ -417,15 +417,15 @@ set_localvar_params <- function(sim_start_date=as.Date("2020-03-31"), pert_b = p_b) %>% dplyr::mutate(pert_dist = ifelse(inference, as.character(pert_dist), NA_character_), dplyr::across(pert_mean:pert_b, ~ifelse(inference, as.numeric(.x), NA_real_))) %>% - dplyr::select(USPS, geoid, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) - + dplyr::select(USPS, subpop, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) + return(local_var) } #' Generate NPI reduction interventions #' #' @param npi_file output from set_npi_params -#' @param incl_geoid vector of geoids to include; NULL will generate interventions for all geographies +#' @param incl_subpop vector of subpop to include; NULL will generate interventions for all geographies #' @param projection_start_date first date without data to fit #' @param redux_end_date end date for reduction interventions; default NULL uses sim_end_date in npi_file #' @param redux_level reduction to intervention effectiveness; used to estimate mean value of reduction by month @@ -452,46 +452,46 @@ set_redux_params <- function(npi_file, v_b=1, compartment = TRUE ){ - + projection_start_date <- as.Date(projection_start_date) param_val <- ifelse(compartment, "r0", "R0") - + if(!is.null(redux_end_date)){ redux_end_date <- as.Date(redux_end_date) - + if(redux_end_date > max(npi_file$end_date)) stop("The end date for reduction interventions should be less than or equal to the sim_end_date in the npi_file.") - + } - + og <- npi_file %>% dplyr::filter(category == "base_npi") %>% - dplyr::group_by(USPS, geoid) %>% + dplyr::group_by(USPS, subpop) %>% dplyr::mutate(end_date = dplyr::if_else(is.null(redux_end_date), end_date, redux_end_date)) - + if(any(projection_start_date < unique(og$start_date))){warning("Some interventions start after the projection_start_date")} - + months_start <- seq(lubridate::floor_date(projection_start_date, "month"), max(og$end_date), by="month") months_start[1] <- projection_start_date - + months_end <- lubridate::ceiling_date(months_start, "months")-1 months_end[length(months_end)] <- max(og$end_date) - + month_n <- length(months_start) - + reduction <- rep(redux_level/month_n, month_n) %>% cumsum() - + redux <- dplyr::tibble( start_date = months_start, end_date = months_end, month = lubridate::month(months_start, label=TRUE, abbr=TRUE) %>% tolower(), value_mean = reduction, # TODO: reduction to value_mean type = rep("transmission", month_n), - geoid = og$geoid %>% paste0(collapse = '", "')) %>% + subpop = og$subpop %>% paste0(collapse = '", "')) %>% mutate(USPS = "", category = "NPI_redux", name = paste0(category, '_', month), baseline_scenario = c("base_npi", paste0("NPI_redux_", month[-length(month)])), - template = "ReduceIntervention", + template = "ModifierModifier", parameter = param_val, value_dist = v_dist, value_sd = v_sd, @@ -502,8 +502,8 @@ set_redux_params <- function(npi_file, pert_sd = NA_real_, pert_a = NA_real_, pert_b = NA_real_) %>% - dplyr::select(USPS, geoid, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) - + dplyr::select(USPS, subpop, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) + return(redux) } @@ -514,7 +514,7 @@ set_redux_params <- function(npi_file, #' @param vacc_path path to vaccination rates #' @param vacc_start_date simulation start date #' @param sim_end_date simulation end date -#' @param incl_geoid vector of geoids to include +#' @param incl_subpop vector of subpop to include #' @param scenario_num which baseline scenario will be selected from the vaccination rate file #' @param compartment #' @@ -523,45 +523,45 @@ set_redux_params <- function(npi_file, #' #' @examples #' -set_vacc_rates_params <- function (vacc_path, - vacc_start_date = "2021-01-01", - sim_end_date = Sys.Date() + 60, - incl_geoid = NULL, - scenario_num = 1, +set_vacc_rates_params <- function (vacc_path, + vacc_start_date = "2021-01-01", + sim_end_date = Sys.Date() + 60, + incl_subpop = NULL, + scenario_num = 1, compartment = TRUE) { - + vacc_start_date <- as.Date(vacc_start_date) sim_end_date <- as.Date(sim_end_date) - vacc <- readr::read_csv(vacc_path) %>% dplyr::filter(!is.na(month) & + vacc <- readr::read_csv(vacc_path) %>% dplyr::filter(!is.na(month) & scenario == scenario_num) - if (!is.null(incl_geoid)) { - vacc <- vacc %>% dplyr::filter(geoid %in% incl_geoid) + if (!is.null(incl_subpop)) { + vacc <- vacc %>% dplyr::filter(subpop %in% incl_subpop) } - vacc <- vacc %>% dplyr::filter(start_date <= sim_end_date) %>% + vacc <- vacc %>% dplyr::filter(start_date <= sim_end_date) %>% dplyr::mutate(end_date = lubridate::as_date(ifelse(end_date > sim_end_date, sim_end_date, end_date))) %>% - dplyr::rename(value_mean = vacc_rate) %>% - dplyr::mutate(geoid = as.character(geoid), month = lubridate::month(start_date, label = TRUE), - type = "transmission", category = "vaccination", - name = paste0("Dose1_", tolower(month), lubridate::year(start_date)), - template = "Reduce", baseline_scenario = "", + dplyr::rename(value_mean = vacc_rate) %>% + dplyr::mutate(subpop = as.character(subpop), month = lubridate::month(start_date, label = TRUE), + type = "transmission", category = "vaccination", + name = paste0("Dose1_", tolower(month), lubridate::year(start_date)), + template = "SinglePeriodModifier", baseline_scenario = "", value_mean = round(value_mean, 5), - value_dist = "fixed", value_sd = NA_real_, value_a = NA_real_, - value_b = NA_real_, pert_dist = NA_character_, pert_mean = NA_real_, - pert_sd = NA_real_, pert_a = NA_real_, pert_b = NA_real_) - + value_dist = "fixed", value_sd = NA_real_, value_a = NA_real_, + value_b = NA_real_, pert_dist = NA_character_, pert_mean = NA_real_, + pert_sd = NA_real_, pert_a = NA_real_, pert_b = NA_real_) + if(compartment){ vacc <- vacc %>% mutate(parameter = rate_param) } else { vacc <- vacc %>% mutate(parameter = "transition_rate 0") } - + if("age_group" %in% colnames(vacc)){ vacc <- vacc %>% mutate(name = paste0(name, "_age", age_group)) } vacc <- vacc %>% - dplyr::select(USPS, geoid, start_date, end_date, name, - template, type, category, parameter, baseline_scenario, - tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) %>% + dplyr::select(USPS, subpop, start_date, end_date, name, + template, type, category, parameter, baseline_scenario, + tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) %>% dplyr::filter(start_date >= vacc_start_date & value_mean > 0) return(vacc) } @@ -573,7 +573,7 @@ set_vacc_rates_params <- function (vacc_path, #' @param vacc_path path to vaccination rates #' @param vacc_start_date simulation start date #' @param sim_end_date simulation end date -#' @param incl_geoid vector of geoids to include +#' @param incl_subpop vector of subpop to include #' @param scenario_num which baseline scenario will be selected from the vaccination rate file #' @param compartment #' @param rate_param @@ -583,44 +583,44 @@ set_vacc_rates_params <- function (vacc_path, #' #' @examples #' -set_vacc_rates_params_dose3 <- function (vacc_path, - vacc_start_date = "2021-01-01", sim_end_date = Sys.Date() + 60, - incl_geoid = NULL, +set_vacc_rates_params_dose3 <- function (vacc_path, + vacc_start_date = "2021-01-01", sim_end_date = Sys.Date() + 60, + incl_subpop = NULL, rate_groups = c("nu_3y","nu_3o"), - scenario_num = 1, + scenario_num = 1, compartment = TRUE, rate_param=NA) { - + vacc_start_date <- as.Date(vacc_start_date) sim_end_date <- as.Date(sim_end_date) - vacc <- readr::read_csv(vacc_path) %>% dplyr::filter(!is.na(month) & + vacc <- readr::read_csv(vacc_path) %>% dplyr::filter(!is.na(month) & scenario == scenario_num) - if (!is.null(incl_geoid)) { - vacc <- vacc %>% dplyr::filter(geoid %in% incl_geoid) + if (!is.null(incl_subpop)) { + vacc <- vacc %>% dplyr::filter(subpop %in% incl_subpop) } - + if(compartment){ vacc <- vacc %>% mutate(parameter=rate_param) } else { vacc <- vacc %>% mutate(parameter="transition_rate 0") } - - vacc <- vacc %>% dplyr::filter(start_date <= sim_end_date) %>% - dplyr::mutate(end_date = lubridate::as_date(ifelse(end_date > - sim_end_date, sim_end_date, end_date))) %>% dplyr::rename(value_mean = vacc_rate) %>% - dplyr::mutate(geoid = as.character(geoid), month = lubridate::month(start_date, - label = TRUE), type = "transmission", category = "vaccination", - name = paste0("Dose3_", tolower(month), lubridate::year(start_date), "_",age_group), - template = "Reduce", - baseline_scenario = "", - value_dist = "fixed", value_sd = NA_real_, value_a = NA_real_, - value_b = NA_real_, pert_dist = NA_character_, pert_mean = NA_real_, - pert_sd = NA_real_, pert_a = NA_real_, pert_b = NA_real_) %>% - dplyr::select(USPS, geoid, start_date, end_date, name, - template, type, category, parameter, baseline_scenario, - tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) %>% + + vacc <- vacc %>% dplyr::filter(start_date <= sim_end_date) %>% + dplyr::mutate(end_date = lubridate::as_date(ifelse(end_date > + sim_end_date, sim_end_date, end_date))) %>% dplyr::rename(value_mean = vacc_rate) %>% + dplyr::mutate(subpop = as.character(subpop), month = lubridate::month(start_date, + label = TRUE), type = "transmission", category = "vaccination", + name = paste0("Dose3_", tolower(month), lubridate::year(start_date), "_",age_group), + template = "SinglePeriodModifier", + baseline_scenario = "", + value_dist = "fixed", value_sd = NA_real_, value_a = NA_real_, + value_b = NA_real_, pert_dist = NA_character_, pert_mean = NA_real_, + pert_sd = NA_real_, pert_a = NA_real_, pert_b = NA_real_) %>% + dplyr::select(USPS, subpop, start_date, end_date, name, + template, type, category, parameter, baseline_scenario, + tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) %>% dplyr::filter(start_date >= vacc_start_date & value_mean > 0) - + return(vacc) } @@ -641,7 +641,7 @@ set_vacc_rates_params_dose3 <- function (vacc_path, #' @param variant_lb #' @param varian_effect change in transmission for variant default is 50% from Davies et al 2021 #' @param month_shift -#' @param geodata file with columns for state/county abbreviation (USPS) and admin code (geoid); only required if state_level is TRUE +#' @param geodata file with columns for state/county abbreviation (USPS) and admin code (subpop); only required if state_level is TRUE #' @param state_level whether there is state-level data on the variant; requires a geodata file #' @param transmission_increase transmission increase in B1617 relative to B117 #' @param inference logical indicating whether inference will be performed on intervention (default is TRUE); perturbation values are replaced with NA if set to FALSE. @@ -662,29 +662,29 @@ set_vacc_rates_params_dose3 <- function (vacc_path, #' #' @examples #' -set_variant_params <- function(b117_only = FALSE, variant_path, variant_path_2 = NULL, - sim_start_date, sim_end_date, inference_cutoff_date = Sys.Date() - 7, - variant_lb = 1.4, variant_effect = 1.5, month_shift = NULL, - state_level = TRUE, geodata = NULL, - transmission_increase = c(1, 1.45, (1.6 * 1.6)), - variant_compartments = c("WILD", "ALPHA", "DELTA"), - compartment = TRUE, inference = TRUE, - v_dist = "truncnorm", v_sd = 0.01, v_a = -1.5, v_b = 0, +set_variant_params <- function(b117_only = FALSE, variant_path, variant_path_2 = NULL, + sim_start_date, sim_end_date, inference_cutoff_date = Sys.Date() - 7, + variant_lb = 1.4, variant_effect = 1.5, month_shift = NULL, + state_level = TRUE, geodata = NULL, + transmission_increase = c(1, 1.45, (1.6 * 1.6)), + variant_compartments = c("WILD", "ALPHA", "DELTA"), + compartment = TRUE, inference = TRUE, + v_dist = "truncnorm", v_sd = 0.01, v_a = -1.5, v_b = 0, p_dist = "truncnorm", p_mean = 0, p_sd = 0.01, p_a = -1, p_b = 1){ - + inference_cutoff_date <- as.Date(inference_cutoff_date) if (compartment) { - variant_data <- generate_compartment_variant2(variant_path = variant_path, - variant_compartments = variant_compartments, transmission_increase = transmission_increase, - geodata = geodata, sim_start_date = sim_start_date, + variant_data <- generate_compartment_variant2(variant_path = variant_path, + variant_compartments = variant_compartments, transmission_increase = transmission_increase, + geodata = geodata, sim_start_date = sim_start_date, sim_end_date = sim_end_date) } else { # we can get rid of this B117 part eventually if (b117_only) { - variant_data <- config.writer::generate_variant_b117(variant_path = variant_path, - sim_start_date = sim_start_date, sim_end_date = sim_end_date, - variant_lb = variant_lb, variant_effect = variant_effect, - month_shift = month_shift) %>% dplyr::mutate(geoid = "all", + variant_data <- config.writer::generate_variant_b117(variant_path = variant_path, + sim_start_date = sim_start_date, sim_end_date = sim_end_date, + variant_lb = variant_lb, variant_effect = variant_effect, + month_shift = month_shift) %>% dplyr::mutate(subpop = "all", USPS = "") } else if (state_level) { if (is.null(variant_path_2)) { @@ -693,39 +693,39 @@ set_variant_params <- function(b117_only = FALSE, variant_path, variant_path_2 = if (is.null(geodata)) { stop("You must specify a geodata file") } - variant_data <- generate_multiple_variants_state(variant_path_1 = variant_path, - variant_path_2 = variant_path_2, sim_start_date = sim_start_date, - sim_end_date = sim_end_date, variant_lb = variant_lb, - variant_effect = variant_effect, transmission_increase = transmission_increase, + variant_data <- generate_multiple_variants_state(variant_path_1 = variant_path, + variant_path_2 = variant_path_2, sim_start_date = sim_start_date, + sim_end_date = sim_end_date, variant_lb = variant_lb, + variant_effect = variant_effect, transmission_increase = transmission_increase, geodata = geodata) } else { if (is.null(variant_path_2)) { stop("You must specify a path for the second variant.") } - variant_data <- generate_multiple_variants(variant_path_1 = variant_path, - variant_path_2 = variant_path_2, sim_start_date = sim_start_date, - sim_end_date = sim_end_date, variant_lb = variant_lb, - variant_effect = variant_effect, transmission_increase = transmission_increase) %>% - dplyr::mutate(geoid = "all", USPS = "") + variant_data <- generate_multiple_variants(variant_path_1 = variant_path, + variant_path_2 = variant_path_2, sim_start_date = sim_start_date, + sim_end_date = sim_end_date, variant_lb = variant_lb, + variant_effect = variant_effect, transmission_increase = transmission_increase) %>% + dplyr::mutate(subpop = "all", USPS = "") } } - variant_data <- variant_data %>% dplyr::mutate(type = "transmission", - category = "variant", - name = paste(USPS, "variantR0adj", paste0("Week", lubridate::week(start_date)), sep = "_"), + variant_data <- variant_data %>% dplyr::mutate(type = "transmission", + category = "variant", + name = paste(USPS, "variantR0adj", paste0("Week", lubridate::week(start_date)), sep = "_"), name = stringr::str_remove(name, "^\\_"), - template = "Reduce", - parameter = "R0", - value_dist = v_dist, value_mean = 1 - R_ratio, value_sd = v_sd, value_a = v_a, value_b = v_b, - pert_dist = p_dist, pert_mean = p_mean, pert_sd = p_sd, - pert_a = p_a, pert_b = p_b, baseline_scenario = "") %>% - dplyr::mutate(dplyr::across(pert_mean:pert_b, ~ifelse(inference & start_date < inference_cutoff_date, .x, NA_real_)), - pert_dist = ifelse(inference & start_date < inference_cutoff_date, - pert_dist, NA_character_)) %>% - dplyr::select(USPS, - geoid, start_date, end_date, name, template, type, category, - parameter, baseline_scenario, tidyselect::starts_with("value_"), + template = "SinglePeriodModifier", + parameter = "R0", + value_dist = v_dist, value_mean = 1 - R_ratio, value_sd = v_sd, value_a = v_a, value_b = v_b, + pert_dist = p_dist, pert_mean = p_mean, pert_sd = p_sd, + pert_a = p_a, pert_b = p_b, baseline_scenario = "") %>% + dplyr::mutate(dplyr::across(pert_mean:pert_b, ~ifelse(inference & start_date < inference_cutoff_date, .x, NA_real_)), + pert_dist = ifelse(inference & start_date < inference_cutoff_date, + pert_dist, NA_character_)) %>% + dplyr::select(USPS, + subpop, start_date, end_date, name, template, type, category, + parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) - + return(variant_data) } @@ -736,7 +736,7 @@ set_variant_params <- function(b117_only = FALSE, variant_path, variant_path_2 = #' @param outcome_path path to vaccination adjusted outcome interventions #' @param sim_start_date simulation start date #' @param sim_end_date simulation end date -#' @param incl_geoid vector of geoids to include +#' @param incl_subpop vector of subpop to include #' @param scenario which scenario will be selected from the outcome intervention file #' @param v_dist type of distribution for reduction #' @param v_sd reduction sd @@ -756,85 +756,85 @@ set_variant_params <- function(b117_only = FALSE, variant_path, variant_path_2 = #' #' @examples #' -set_vacc_outcome_params <- function(age_strat = "under65", +set_vacc_outcome_params <- function(age_strat = "under65", variant_compartments = c("WILD","ALPHA","DELTA"), vaccine_compartments = c("unvaccinated"), national_level = TRUE, # whether to do national interventions to reduce number redux_round = 0.1, - outcome_path, - sim_start_date = as.Date("2020-03-31"), - sim_end_date = Sys.Date() + 60, - inference = FALSE, - incl_geoid = NULL, - scenario_num = 1, - v_dist = "truncnorm", v_sd = 0.01, v_a = 0, v_b = 1, - p_dist = "truncnorm", p_mean = 0, p_sd = 0.05, + outcome_path, + sim_start_date = as.Date("2020-03-31"), + sim_end_date = Sys.Date() + 60, + inference = FALSE, + incl_subpop = NULL, + scenario_num = 1, + v_dist = "truncnorm", v_sd = 0.01, v_a = 0, v_b = 1, + p_dist = "truncnorm", p_mean = 0, p_sd = 0.05, p_a = -1, p_b = 1){ - + sim_start_date <- as.Date(sim_start_date) sim_end_date <- as.Date(sim_end_date) - outcome <- readr::read_csv(outcome_path) %>% - dplyr::filter(!is.na(month) & month != "baseline") %>% + outcome <- readr::read_csv(outcome_path) %>% + dplyr::filter(!is.na(month) & month != "baseline") %>% dplyr::filter(scenario == scenario_num) %>% dplyr::filter(prob_redux!=1) - - if (!is.null(incl_geoid)){ - outcome <- outcome %>% dplyr::filter(geoid %in% incl_geoid) + + if (!is.null(incl_subpop)){ + outcome <- outcome %>% dplyr::filter(subpop %in% incl_subpop) } if(!is.null(outcome$age_strata)){ if(!is.null(age_strat)){ outcome <- outcome %>% filter(age_strata %in% age_strat) } } - + if(national_level){ - outcome <- outcome %>% + outcome <- outcome %>% group_by(age_strata, start_date, end_date, month, year, var) %>% summarise(prob_redux = mean(prob_redux, na.rm=TRUE)) %>% - mutate(USPS="US", geoid='all') + mutate(USPS="US", subpop='all') } - - outcome <- outcome %>% + + outcome <- outcome %>% mutate(prob_redux = round(prob_redux / redux_round)*redux_round) %>% filter(prob_redux!=1) - - outcome <- outcome %>% - dplyr::mutate(month = tolower(month)) %>% - dplyr::mutate(prob_redux = 1 - prob_redux) %>% - dplyr::filter(start_date <= sim_end_date) %>% - dplyr::mutate(end_date = lubridate::as_date(ifelse(end_date > sim_end_date, sim_end_date, end_date)), - start_date = lubridate::as_date(ifelse(end_date > start_date & start_date < sim_start_date, sim_start_date, start_date))) %>% - dplyr::filter(start_date >= sim_start_date) %>% - dplyr::rename(value_mean = prob_redux) %>% - dplyr::mutate(geoid = as.character(geoid), - type = "outcome", - category = "vacc_outcome",baseline_scenario = "", - value_dist = v_dist, value_sd = v_sd, value_a = v_a, - value_b = v_b, pert_dist = p_dist, pert_mean = p_mean, - pert_sd = p_sd, pert_a = p_a, pert_b = p_b) - - outcome <- outcome %>% + + outcome <- outcome %>% + dplyr::mutate(month = tolower(month)) %>% + dplyr::mutate(prob_redux = 1 - prob_redux) %>% + dplyr::filter(start_date <= sim_end_date) %>% + dplyr::mutate(end_date = lubridate::as_date(ifelse(end_date > sim_end_date, sim_end_date, end_date)), + start_date = lubridate::as_date(ifelse(end_date > start_date & start_date < sim_start_date, sim_start_date, start_date))) %>% + dplyr::filter(start_date >= sim_start_date) %>% + dplyr::rename(value_mean = prob_redux) %>% + dplyr::mutate(subpop = as.character(subpop), + type = "outcome", + category = "vacc_outcome",baseline_scenario = "", + value_dist = v_dist, value_sd = v_sd, value_a = v_a, + value_b = v_b, pert_dist = p_dist, pert_mean = p_mean, + pert_sd = p_sd, pert_a = p_a, pert_b = p_b) + + outcome <- outcome %>% dplyr::full_join( expand_grid(var = c("rr_death_inf", "rr_hosp_inf"), variant=variant_compartments, vacc=vaccine_compartments, age_strata=unique(outcome$age_strata)) %>% - dplyr::mutate(param = dplyr::case_when(var == "rr_death_inf" ~ "incidD", var == "rr_hosp_inf" ~ "incidH", + dplyr::mutate(param = dplyr::case_when(var == "rr_death_inf" ~ "incidD", var == "rr_hosp_inf" ~ "incidH", TRUE ~ NA_character_), - param = paste(param, vacc, variant, age_strat, sep="_")) %>% + param = paste(param, vacc, variant, age_strat, sep="_")) %>% dplyr::filter(!is.na(param))) %>% dplyr::mutate( - # name = paste(param, "vaccadj", month, sep = "_"), template = "Reduce", - # name = paste(param, "vaccadj", USPS, (1-value_mean), sep = "_"), template = "Reduce", - name = paste(param, "vaccadj", (1-value_mean), sep = "_"), template = "Reduce", - parameter = paste0(param, "::probability")) %>% - dplyr::mutate(dplyr::across(pert_mean:pert_b, - ~ifelse(inference, .x, NA_real_)), - pert_dist = ifelse(inference, - pert_dist, NA_character_)) %>% - dplyr::select(USPS, geoid, - start_date, end_date, name, template, type, category, - parameter, baseline_scenario, tidyselect::starts_with("value_"), + # name = paste(param, "vaccadj", month, sep = "_"), template = "SinglePeriodModifier", + # name = paste(param, "vaccadj", USPS, (1-value_mean), sep = "_"), template = "SinglePeriodModifier", + name = paste(param, "vaccadj", (1-value_mean), sep = "_"), template = "SinglePeriodModifier", + parameter = paste0(param, "::probability")) %>% + dplyr::mutate(dplyr::across(pert_mean:pert_b, + ~ifelse(inference, .x, NA_real_)), + pert_dist = ifelse(inference, + pert_dist, NA_character_)) %>% + dplyr::select(USPS, subpop, + start_date, end_date, name, template, type, category, + parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) return(outcome) } @@ -845,11 +845,11 @@ set_vacc_outcome_params <- function(age_strat = "under65", #' Generate incidC shift interventions #' #' @param periods vector of dates that include a shift in incidC -#' @param geodata df with USPS and geoid column for geoids with a shift in incidC +#' @param geodata df with USPS and subpop column for subpop with a shift in incidC #' @param baseline_ifr assumed true infection fatality rate #' @param cfr_data optional file with estimates of cfr by state #' @param epochs character vector with the selection of epochs from the cfr_data file, any of "NoSplit", "MarJun", "JulOct", "NovJan". Required if cfr_data is specified. -#' @param outcomes_parquet_file path to file with geoid-specific adjustments to IFR; required if cfr_data is specified +#' @param outcomes_parquet_file path to file with subpop-specific adjustments to IFR; required if cfr_data is specified #' @param inference logical indicating whether inference will be performed on intervention (default is TRUE); perturbation values are replaced with NA if set to FALSE. #' @param v_dist type of distribution for reduction #' @param v_mean state-specific initial value. will be taken from empirical CFR estimates if it exists, otherwise this used. If a vector is specified, then each value is added to the corresponding period @@ -878,57 +878,57 @@ set_incidC_shift <- function(periods, p_mean = 0, p_sd = 0.01, p_a = -1, p_b = 1 ){ periods <- as.Date(periods) - + if(is.null(cfr_data)){ epochs <- 1:(length(periods)-1) - + cfr_data <- geodata %>% - dplyr::select(USPS, geoid) %>% + dplyr::select(USPS, subpop) %>% tidyr::expand_grid(value_mean = v_mean, epoch=epochs) } else{ if(is.null(epochs) | length(epochs) != (length(periods)-1)){stop("The number of epochs selected should be equal to the number of periods with a shift in incidC")} if(any(!epochs %in% c("NoSplit", "MarJun", "JulOct", "NovJan"))){stop('Unknown epoch selected, choose from: "NoSplit", "MarJun", "JulOct", "NovJan"')} if(is.null(outcomes_parquet_file)){stop("Must specify a file with the age-adjustments to IFR by state")} - + relative_outcomes <- arrow::read_parquet(outcomes_parquet_file) - + relative_ifr <- relative_outcomes %>% dplyr::filter(source == 'incidI' & outcome == "incidD") %>% - dplyr::filter(geoid %in% geodata$geoid) %>% - dplyr::select(USPS,geoid,value) %>% + dplyr::filter(subpop %in% geodata$subpop) %>% + dplyr::select(USPS,subpop,value) %>% dplyr::rename(rel_ifr=value) %>% dplyr::mutate(ifr=baseline_ifr*rel_ifr) - + cfr_data <- readr::read_csv(cfr_data) %>% dplyr::rename(USPS=state, delay=lag) %>% dplyr::select(USPS, epoch, delay, cfr) %>% dplyr::filter(epoch %in% epochs) %>% dplyr::left_join(relative_ifr) %>% - dplyr::filter(geoid %in% geodata$geoid) %>% + dplyr::filter(subpop %in% geodata$subpop) %>% dplyr::mutate(incidC = pmin(0.99,ifr/cfr), # get effective case detection rate based in assumed IFR. value_mean = pmax(0,1-incidC), value_mean = signif(value_mean, digits = 2)) %>% # get effective reduction in incidC assuming baseline incidC - dplyr::select(USPS,geoid, epoch, value_mean) - - + dplyr::select(USPS,subpop, epoch, value_mean) + + no_cfr_data <- relative_ifr %>% tidyr::expand_grid(value_mean = v_mean, epoch = epochs) %>% - dplyr::filter(!geoid %in% cfr_data$geoid) %>% - dplyr::select(USPS, geoid, epoch, value_mean) - + dplyr::filter(!subpop %in% cfr_data$subpop) %>% + dplyr::select(USPS, subpop, epoch, value_mean) + cfr_data <- dplyr::bind_rows(cfr_data, no_cfr_data) } - + outcome <- list() for(i in 1:(length(periods)-1)){ outcome[[i]] <- cfr_data %>% dplyr::filter(epoch == epochs[i]) %>% dplyr::select(-epoch) %>% dplyr::mutate( - template = "Reduce", + template = "SinglePeriodModifier", name = paste0("incidCshift_", i), type = "outcome", category = "incidCshift", @@ -947,16 +947,16 @@ set_incidC_shift <- function(periods, pert_a = p_a, pert_b = p_b ) - + } - + outcome <- dplyr::bind_rows(outcome) %>% dplyr::mutate(dplyr::across(pert_mean:pert_b, ~ifelse(inference, .x, NA_real_)), pert_dist = ifelse(inference, pert_dist, NA_character_)) %>% - dplyr::select(USPS, geoid, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) - + dplyr::select(USPS, subpop, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) + return(outcome) - + } #' Generate interventions to adjust hospitalizations @@ -964,7 +964,7 @@ set_incidC_shift <- function(periods, #' @param outcome_path path to vaccination adjusted outcome interventions #' @param sim_start_date simulation start date #' @param sim_end_date simulation end date -#' @param geodata df with USPS and geoid column for geoids with an incidH adjustment +#' @param geodata df with USPS and subpop column for subpop with an incidH adjustment #' @param v_dist type of distribution for reduction #' @param v_sd reduction sd #' @param v_a reduction a @@ -995,15 +995,15 @@ set_incidH_adj_params <- function(outcome_path, ) { variant_compartments <- stringr::str_to_upper(variant_compartments) - + sim_start_date <- lubridate::as_date(sim_start_date) sim_end_date <- lubridate::as_date(sim_end_date) outcome <- readr::read_csv(outcome_path) %>% dplyr::filter(!is.na(ratio) & USPS != "US") - + outcome <- outcome %>% - dplyr::left_join(geodata %>% dplyr::select(USPS, geoid)) - + dplyr::left_join(geodata %>% dplyr::select(USPS, subpop)) + outcome <- outcome %>% dplyr::mutate(param = "incidH") %>% # dplyr::mutate(month = tolower(month)) %>% dplyr::mutate(prob_redux = 1 - (1/ratio)) %>% @@ -1011,11 +1011,11 @@ set_incidH_adj_params <- function(outcome_path, dplyr::mutate(end_date = sim_end_date, start_date = sim_start_date) %>% dplyr::rename(value_mean = prob_redux) %>% - dplyr::mutate(geoid = as.character(geoid), + dplyr::mutate(subpop = as.character(subpop), type = "outcome", category = "outcome_adj", name = paste(param, "adj",USPS, sep = "_"), - template = "Reduce", + template = "SinglePeriodModifier", parameter = paste0(param, "::probability"), baseline_scenario = "", value_dist = v_dist, @@ -1029,10 +1029,10 @@ set_incidH_adj_params <- function(outcome_path, pert_b = p_b) %>% dplyr::mutate(dplyr::across(pert_mean:pert_b, ~ifelse(inference, .x, NA_real_)), pert_dist = ifelse(inference, pert_dist, NA_character_)) %>% - dplyr::select(USPS, geoid, start_date, end_date, name, template, type, category, + dplyr::select(USPS, subpop, start_date, end_date, name, template, type, category, parameter, baseline_scenario, tidyselect::starts_with("value_"), tidyselect::starts_with("pert_")) - + if(compartment){ temp <- list() for(i in 1:length(variant_compartments)){ @@ -1040,11 +1040,11 @@ set_incidH_adj_params <- function(outcome_path, dplyr::mutate(parameter = stringr::str_replace(parameter, "::probability", paste0("_", variant_compartments[i],"::probability")), name = paste0(name, "_", variant_compartments[i])) } - + outcome <- dplyr::bind_rows(temp) - + } - + return(outcome) } @@ -1056,7 +1056,7 @@ set_incidH_adj_params <- function(outcome_path, #' @param VE_delta vaccine effectivenes against variant or the first and second doses, respectively #' @param sim_start_date simulation start date #' @param sim_end_date simulation end date -#' @param geodata df with USPS and geoid column for geoids with an incidH adjustment +#' @param geodata df with USPS and subpop column for subpop with an incidH adjustment #' @param v_dist type of distribution for reduction #' @param v_sd reduction sd #' @param v_a reduction a @@ -1083,12 +1083,12 @@ set_ve_shift_params <- function(variant_path, v_dist = "fixed", v_sd = 0.01, v_a = -1, v_b = 2, p_dist = "truncnorm", p_mean = 0, p_sd = 0.01, p_a = -1, p_b = 1, compartment = TRUE){ - + par_val_1 <- ifelse(compartment, "theta_1A", "susceptibility_reduction 1") par_val_2 <- ifelse(compartment, "theta_2A", "susceptibility_reduction 2") sim_start_date <- lubridate::as_date(sim_start_date) sim_end_date <- lubridate::as_date(sim_end_date) - + outcome <- readr::read_csv(variant_path) %>% dplyr::filter(location == "US", date >= "2021-04-01") %>% dplyr::mutate(month = lubridate::month(date, label=TRUE), year = lubridate::year(date), @@ -1109,19 +1109,19 @@ set_ve_shift_params <- function(variant_path, start_date = min(start_date), end_date = max(end_date)) %>% dplyr::filter(value_mean != 0) - - + + outcome <- outcome %>% dplyr::mutate(name = paste0("VEshift_", tolower(month), "_dose", stringr::str_sub(dose, 3, 3))) %>% dplyr::select(-dose) %>% dplyr::filter(start_date <= sim_end_date & end_date > sim_start_date) %>% dplyr::mutate(end_date = lubridate::as_date(ifelse(end_date > sim_end_date, sim_end_date, end_date))) %>% dplyr::mutate(USPS = "", - geoid = "all", + subpop = "all", type = "transmission", parameter = dplyr::if_else(stringr::str_detect(name, "ose1"), par_val_1, par_val_2), category = "ve_shift", - template = "Reduce", + template = "SinglePeriodModifier", baseline_scenario = "", value_dist = v_dist, value_sd = v_sd, @@ -1152,38 +1152,38 @@ set_ve_shift_params <- function(variant_path, #' #' @examples #' -bind_interventions <- function(..., - inference_cutoff_date = Sys.Date() - 7, +bind_interventions <- function(..., + inference_cutoff_date = Sys.Date() - 7, sim_start_date, - sim_end_date, + sim_end_date, save_name, filter_dates=FALSE) { - + inference_cutoff_date <- as.Date(inference_cutoff_date) sim_end_date <- as.Date(sim_end_date) sim_start_date <- as.Date(sim_start_date) dat <- dplyr::bind_rows(...) if (filter_dates){ - dat <- dat %>% + dat <- dat %>% filter(start_date < sim_end) %>% filter(end_date > sim_start) %>% mutate(start_date = as_date(ifelse(start_date sim_end_date) + if (max(dat$end_date) > sim_end_date) stop("At least one intervention has an end date after the sim_end_date.") } check <- dat %>% dplyr::filter(category == "NPI") %>% - dplyr::group_by(USPS, geoid, type, category) %>% dplyr::arrange(USPS, geoid, start_date) %>% - dplyr::mutate(note = dplyr::case_when(end_date >= dplyr::lead(start_date) ~ "Overlap", dplyr::lead(start_date) - end_date > 1 ~ "Gap", TRUE ~ NA_character_)) %>% + dplyr::group_by(USPS, subpop, type, category) %>% dplyr::arrange(USPS, subpop, start_date) %>% + dplyr::mutate(note = dplyr::case_when(end_date >= dplyr::lead(start_date) ~ "Overlap", dplyr::lead(start_date) - end_date > 1 ~ "Gap", TRUE ~ NA_character_)) %>% dplyr::mutate(dplyr::across(pert_mean:pert_b, ~ifelse(start_date < inference_cutoff_date, .x, NA_real_)), pert_dist = ifelse(start_date < inference_cutoff_date, pert_dist, NA_character_)) %>% dplyr::filter(!is.na(note)) if (nrow(check) > 0) { - if (any(check$note == "Overlap")) - warning(paste0("There are ", nrow(check[check$note == "Overlap", ]), " NPIs of the same category/geoid that overlap in time")) - if (any(check$note == "Gap")) - warning(paste0("There are ", nrow(check[check$note == "Gap", ]), " NPIs of the same category/geoid that are discontinuous.")) + if (any(check$note == "Overlap")) + warning(paste0("There are ", nrow(check[check$note == "Overlap", ]), " NPIs of the same category/subpop that overlap in time")) + if (any(check$note == "Gap")) + warning(paste0("There are ", nrow(check[check$note == "Gap", ]), " NPIs of the same category/subpop that are discontinuous.")) } if (!is.null(save_name)) { readr::write_csv(dat, file = save_name) @@ -1192,7 +1192,7 @@ bind_interventions <- function(..., } -#' Estimate average reduction in transmission per day per geoid +#' Estimate average reduction in transmission per day per subpop #' #' @param dat #' @param plot @@ -1205,7 +1205,7 @@ bind_interventions <- function(..., daily_mean_reduction <- function(dat, plot = FALSE){ - + dat <- dat %>% dplyr::filter(type == "transmission") %>% dplyr::mutate(mean = dplyr::case_when(value_dist == "truncnorm" ~ @@ -1215,51 +1215,51 @@ daily_mean_reduction <- function(dat, value_dist == "uniform" ~ (value_a+value_b)/2) ) %>% - dplyr::select(USPS, geoid, start_date, end_date, mean) - + dplyr::select(USPS, subpop, start_date, end_date, mean) + timeline <- tidyr::crossing(time = seq(from=min(dat$start_date), to=max(dat$end_date), by = 1), - geoid = unique(dat$geoid)) - - if(any(stringr::str_detect(dat$geoid, '", "'))){ - mtr_geoid <- dat %>% - dplyr::filter(stringr::str_detect(geoid, '", "')) - + subpop = unique(dat$subpop)) + + if(any(stringr::str_detect(dat$subpop, '", "'))){ + mtr_subpop <- dat %>% + dplyr::filter(stringr::str_detect(subpop, '", "')) + temp <- list() - for(i in 1:nrow(mtr_geoid)){ - temp[[i]] <- tidyr::expand_grid(geoid = mtr_geoid$geoid[i] %>% stringr::str_split('", "') %>% unlist(), - mtr_geoid[i,] %>% dplyr::ungroup() %>% dplyr::select(-geoid)) %>% - dplyr::select(colnames(mtr_geoid)) + for(i in 1:nrow(mtr_subpop)){ + temp[[i]] <- tidyr::expand_grid(subpop = mtr_subpop$subpop[i] %>% stringr::str_split('", "') %>% unlist(), + mtr_subpop[i,] %>% dplyr::ungroup() %>% dplyr::select(-subpop)) %>% + dplyr::select(colnames(mtr_subpop)) } - + dat <- dat %>% - dplyr::filter(stringr::str_detect(geoid, '", "', negate = TRUE)) %>% + dplyr::filter(stringr::str_detect(subpop, '", "', negate = TRUE)) %>% dplyr::bind_rows( dplyr::bind_rows(temp) ) } - + dat <- dat %>% - dplyr::filter(geoid=="all") %>% + dplyr::filter(subpop=="all") %>% dplyr::ungroup() %>% - dplyr::select(-geoid) %>% - tidyr::crossing(geoid=unique(dat$geoid[dat$geoid!="all"])) %>% - dplyr::select(geoid, start_date, end_date, mean) %>% - dplyr::bind_rows(dat %>% dplyr::filter(geoid!="all") %>% dplyr::ungroup() %>% dplyr::select(-USPS)) %>% + dplyr::select(-subpop) %>% + tidyr::crossing(subpop=unique(dat$subpop[dat$subpop!="all"])) %>% + dplyr::select(subpop, start_date, end_date, mean) %>% + dplyr::bind_rows(dat %>% dplyr::filter(subpop!="all") %>% dplyr::ungroup() %>% dplyr::select(-USPS)) %>% dplyr::left_join(timeline) %>% dplyr::filter(time >= start_date & time <= end_date) %>% - dplyr::group_by(geoid, time) %>% + dplyr::group_by(subpop, time) %>% dplyr::summarize(mean = prod(1-mean)) - + if(plot){ dat<- ggplot2::ggplot(data= dat, ggplot2::aes(x=time, y=mean))+ ggplot2::geom_line()+ - ggplot2::facet_wrap(~geoid)+ + ggplot2::facet_wrap(~subpop)+ ggplot2::theme_bw()+ ggplot2::ylab("Average reduction")+ ggplot2::scale_x_date(date_breaks = "3 months", date_labels = "%b\n%y")+ ggplot2::scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2.0)) - + } - + return(dat) } diff --git a/flepimop/R_packages/config.writer/R/process_npi_list.R b/flepimop/R_packages/config.writer/R/process_npi_list.R index 7313d23fe..8de91d446 100644 --- a/flepimop/R_packages/config.writer/R/process_npi_list.R +++ b/flepimop/R_packages/config.writer/R/process_npi_list.R @@ -19,14 +19,14 @@ NULL ##' Convenience function to load the geodata file ##' ##' @param filename filename of geodata file -##' @param geoid_len length of geoid character string -##' @param geoid_pad what to pad the geoid character string with +##' @param subpop_len length of subpop character string +##' @param subpop_pad what to pad the subpop character string with ##' @param state_name whether to add column state with the US state name; defaults to TRUE for forecast or scenario hub runs. ##' ##' @details -##' Currently, the package only supports a geodata object with at least two columns: USPS with the state abbreviation and geoid with the geo IDs of the area. . +##' Currently, the package only supports a geodata object with at least two columns: USPS with the state abbreviation and subpop with the geo IDs of the area. . ##' -##' @return a data frame with columns for state USPS, county geoid and population +##' @return a data frame with columns for state USPS, county subpop and population ##' @examples ##' geodata <- load_geodata_file(filename = system.file("extdata", "geodata_territories_2019_statelevel.csv", package = "config.writer")) ##' geodata @@ -34,20 +34,20 @@ NULL ##' @export load_geodata_file <- function(filename, - geoid_len = 0, - geoid_pad = "0", + subpop_len = 0, + subpop_pad = "0", state_name = TRUE) { if(!file.exists(filename)){stop(paste(filename,"does not exist in",getwd()))} geodata <- readr::read_csv(filename) %>% - dplyr::mutate(geoid = as.character(geoid)) + dplyr::mutate(subpop = as.character(subpop)) - if (!("geoid" %in% names(geodata))) { - stop(paste(filename, "does not have a column named geoid")) + if (!("subpop" %in% names(geodata))) { + stop(paste(filename, "does not have a column named subpop")) } - if (geoid_len > 0) { - geodata$geoid <- stringr::str_pad(geodata$geoid, geoid_len, pad = geoid_pad) + if (subpop_len > 0) { + geodata$subpop <- stringr::str_pad(geodata$subpop, subpop_len, pad = subpop_pad) } if(state_name) { @@ -94,7 +94,7 @@ find_truncnorm_mean_parameter <- function(a, b, mean, sd) { ) } -#' ScenarioHub: Recode scenario hub interventions for "ReduceR0" template +#' ScenarioHub: Recode scenario hub interventions for "SinglePeriodModifier" template #' #' @param data intervention list for the national forecast or the scenariohub #' @@ -118,11 +118,11 @@ npi_recode_scenario <- function(data } -#' ScenarioHub: Recode scenario hub interventions for "MultiTimeReduce" template +#' ScenarioHub: Recode scenario hub interventions for "MultiPeriodModifier" template #' #' @param data intervention list for the national forecast or the scenariohub #' -#' @return recoded npi names for use with MultiTimeReduce +#' @return recoded npi names for use with MultiPeriodModifier #' @export #' @@ -142,17 +142,17 @@ npi_recode_scenario_mult <- function(data){ #' ScenarioHub: Process scenario hub npi list #' #' @param intervention_path path to csv with intervention list -#' @param geodata df with state USPS and geoid from load_geodata_file -#' @param prevent_overlap whether to allow for interventions to overlap in time and geoid +#' @param geodata df with state USPS and subpop from load_geodata_file +#' @param prevent_overlap whether to allow for interventions to overlap in time and subpop #' @param prevent_gaps whether to prevent gaps in interventions (i.e. no interventions) #' #' @return df with six columns: #' - USPS: state abbreviation -#' - geoid: county ID +#' - subpop: county ID #' - start_date: intervention start date #' - end_date: intervention end date #' - name: intervention name -#' - template: intervention template (e.g. ReduceR0, MultiTimeReduce) +#' - template: intervention template (e.g. SinglePeriodModifier, MultiPeriodModifier) #' @export #' #' @examples @@ -174,18 +174,18 @@ process_npi_usa <- function (intervention_path, og <- og %>% dplyr::mutate(dplyr::across(tidyselect::ends_with("_date"), ~lubridate::mdy(.x))) } if ("template" %in% colnames(og)) { - og <- og %>% dplyr::mutate(name = dplyr::if_else(template == "MultiTimeReduce", scenario_mult, scenario)) %>% - dplyr::select(USPS, geoid, start_date, end_date, name, template) + og <- og %>% dplyr::mutate(name = dplyr::if_else(template == "MultiPeriodModifier", scenario_mult, scenario)) %>% + dplyr::select(USPS, subpop, start_date, end_date, name, template) } else { - og <- og %>% dplyr::mutate(template = "MultiTimeReduce") %>% - dplyr::select(USPS, geoid, start_date, end_date, name = scenario_mult, template) + og <- og %>% dplyr::mutate(template = "MultiPeriodModifier") %>% + dplyr::select(USPS, subpop, start_date, end_date, name = scenario_mult, template) } if (prevent_overlap) { - og <- og %>% dplyr::group_by(USPS, geoid) %>% + og <- og %>% dplyr::group_by(USPS, subpop) %>% dplyr::mutate(end_date = dplyr::if_else(end_date >= dplyr::lead(start_date), dplyr::lead(start_date) - 1, end_date)) } if (prevent_gaps) { - og <- og %>% dplyr::group_by(USPS, geoid) %>% + og <- og %>% dplyr::group_by(USPS, subpop) %>% dplyr::mutate(end_date = dplyr::if_else(end_date < dplyr::lead(start_date), dplyr::lead(start_date) - 1, end_date)) } return(og) @@ -196,17 +196,17 @@ process_npi_usa <- function (intervention_path, #' Process California intervention data #' #' @param intervention_path path to csv with intervention list -#' @param geodata df with state USPS and geoid from load_geodata_file -#' @param prevent_overlap whether to allow for interventions to overlap in time and geoid +#' @param geodata df with state USPS and subpop from load_geodata_file +#' @param prevent_overlap whether to allow for interventions to overlap in time and subpop #' @param prevent_gaps whether to prevent gaps in interventions (i.e. no interventions) #' #' @return df with six columns: #' - USPS: state abbreviation -#' - geoid: county ID +#' - subpop: county ID #' - start_date: intervention start date #' - end_date: intervention end date #' - name: intervention name -#' - template: intervention template (e.g. ReduceR0, MultiTimeReduce) +#' - template: intervention template (e.g. SinglePeriodModifier, MultiPeriodModifier) #' @export #' process_npi_ca <- function(intervention_path, @@ -221,25 +221,25 @@ process_npi_ca <- function(intervention_path, readr::col_character(), readr::col_character(), readr::col_date(format = date_format), readr::col_character()) ) %>% - dplyr::mutate(geoid = dplyr::if_else(stringr::str_length(geoid)==4, paste0(0, geoid), geoid)) %>% + dplyr::mutate(subpop = dplyr::if_else(stringr::str_length(subpop)==4, paste0(0, subpop), subpop)) %>% dplyr::left_join(geodata) %>% - dplyr::group_by(county, geoid) %>% + dplyr::group_by(county, subpop) %>% dplyr::arrange(start_date) %>% dplyr::mutate(end_date = dplyr::if_else(is.na(end_date), dplyr::lead(start_date)-1, end_date), end_date = dplyr::if_else(start_date == max(start_date), lubridate::NA_Date_, end_date), - template = "MultiTimeReduce") %>% + template = "MultiPeriodModifier") %>% dplyr::ungroup() %>% - dplyr::select(USPS, geoid, start_date, end_date, name = phase, template) + dplyr::select(USPS, subpop, start_date, end_date, name = phase, template) if(prevent_overlap){ og <- og %>% - dplyr::group_by(USPS, geoid) %>% + dplyr::group_by(USPS, subpop) %>% dplyr::mutate(end_date = dplyr::if_else(end_date >= dplyr::lead(start_date) & !is.na(end_date), dplyr::lead(start_date)-1, end_date)) } if(prevent_gaps){ og <- og %>% - dplyr::group_by(USPS, geoid) %>% + dplyr::group_by(USPS, subpop) %>% dplyr::mutate(end_date = dplyr::if_else(end_date < dplyr::lead(start_date) & !is.na(end_date), dplyr::lead(start_date)-1, end_date)) } @@ -543,8 +543,8 @@ generate_multiple_variants_state <- function(variant_path_1, dplyr::filter(R_ratio>1) %>% dplyr::filter(location != "US") %>% dplyr::rename("USPS" = "location") %>% - dplyr::left_join(geodata %>% dplyr::select(USPS, geoid)) %>% - dplyr::filter(!is.na(geoid)) %>% + dplyr::left_join(geodata %>% dplyr::select(USPS, subpop)) %>% + dplyr::filter(!is.na(subpop)) %>% dplyr::ungroup() } @@ -631,7 +631,7 @@ generate_compartment_variant <- function(variant_path = "../COVID19_USA/data/var variant_data <- variant_data %>% dplyr::filter(R_ratio>1) %>% dplyr::filter(USPS != "US") %>% - dplyr::left_join(geodata %>% dplyr::select(USPS, geoid)) + dplyr::left_join(geodata %>% dplyr::select(USPS, subpop)) return(variant_data) } diff --git a/flepimop/R_packages/config.writer/R/yaml_utils.R b/flepimop/R_packages/config.writer/R/yaml_utils.R index 41445d07d..4dc245308 100644 --- a/flepimop/R_packages/config.writer/R/yaml_utils.R +++ b/flepimop/R_packages/config.writer/R/yaml_utils.R @@ -81,7 +81,7 @@ collapse_intervention<- function(dat){ #TODO: add number to repeated names #TODO add a check that all end_dates are the same mtr <- dat %>% - dplyr::filter(template=="MultiTimeReduce") %>% + dplyr::filter(template=="MultiPeriodModifier") %>% dplyr::mutate(end_date=paste0("end_date: ", end_date), start_date=paste0("- start_date: ", start_date)) %>% tidyr::unite(col="period", sep="\n ", start_date:end_date) %>% @@ -91,21 +91,21 @@ collapse_intervention<- function(dat){ if (!all(is.na(mtr$spatial_groups)) & !all(is.null(mtr$spatial_groups))) { mtr <- mtr %>% - dplyr::group_by(dplyr::across(-geoid)) %>% - dplyr::summarize(geoid = paste0(geoid, collapse='", "'), + dplyr::group_by(dplyr::across(-subpop)) %>% + dplyr::summarize(subpop = paste0(subpop, collapse='", "'), spatial_groups = paste0(spatial_groups, collapse='", "')) %>% dplyr::mutate(period = paste0(" ", period)) } else { mtr <- mtr %>% - dplyr::group_by(dplyr::across(-geoid)) %>% - dplyr::summarize(geoid = paste0(geoid, collapse='", "')) %>% + dplyr::group_by(dplyr::across(-subpop)) %>% + dplyr::summarize(subpop = paste0(subpop, collapse='", "')) %>% dplyr::mutate(period = paste0(" ", period)) } reduce <- dat %>% - dplyr::select(USPS, geoid, contains("spatial_groups"), start_date, end_date, name, template, type, category, parameter, baseline_scenario, starts_with("value_"), starts_with("pert_")) %>% - dplyr::filter(template %in% c("ReduceR0", "Reduce", "ReduceIntervention")) %>% + dplyr::select(USPS, subpop, contains("spatial_groups"), start_date, end_date, name, template, type, category, parameter, baseline_scenario, starts_with("value_"), starts_with("pert_")) %>% + dplyr::filter(template %in% c("SinglePeriodModifier", "ModifierModifier")) %>% dplyr::mutate(end_date=paste0("period_end_date: ", end_date), start_date=paste0("period_start_date: ", start_date)) %>% tidyr::unite(col="period", sep="\n ", start_date:end_date) %>% @@ -113,22 +113,22 @@ collapse_intervention<- function(dat){ dplyr::ungroup() %>% dplyr::add_count(dplyr::across(-USPS)) %>% dplyr::mutate(name = dplyr::case_when(category =="local_variance" | USPS %in% c("all", "") | is.na(USPS) ~ name, - n==1 & template=="Reduce" ~ paste0(USPS, "_", name), - template=="Reduce" ~ paste0(geoid, "_", name), - n==1 & template!="ReduceIntervention" ~ paste0(USPS, name), - template!="ReduceIntervention" ~ paste0(geoid, name), + n==1 & template=="SinglePeriodModifier" ~ paste0(USPS, "_", name), + template=="SinglePeriodModifier" ~ paste0(subpop, "_", name), + n==1 & template!="ModifierModifier" ~ paste0(USPS, name), + template!="ModifierModifier" ~ paste0(subpop, name), TRUE ~ name), name = stringr::str_remove(name, "^_")) dat <- dplyr::bind_rows(mtr, reduce) %>% dplyr::mutate(interv_order = dplyr::recode(category, "universal_npi" = 1, "local_var" = 2, "seasonal" = 3, "NPI" = 4, "incidCshift" = 5)) %>% - dplyr::arrange(interv_order, USPS, category, geoid, parameter) %>% + dplyr::arrange(interv_order, USPS, category, subpop, parameter) %>% dplyr::ungroup() return(dat) } -#' Print intervention text for MultiTimeReduce interventions +#' Print intervention text for MultiPeriodModifier interventions #' #' @param dat df for an intervention with the MTR template with processed name/period; see collapsed_intervention. All rows in the dataframe should have the same intervention name. #' @@ -139,16 +139,16 @@ collapse_intervention<- function(dat){ #' yaml_mtr_template <- function(dat){ template <- unique(dat$template) - geoid_all <- any(unique(dat$geoid)=="all") + subpop_all <- any(unique(dat$subpop)=="all") inference <- !any(is.na(dat$pert_dist)) - if(template=="MultiTimeReduce" & geoid_all){ + if(template=="MultiPeriodModifier" & subpop_all){ cat(paste0( " ", dat$name, ":\n", - " template: MultiTimeReduce\n", + " template: MultiPeriodModifier\n", " parameter: ", dat$parameter, "\n", " groups:\n", - ' - affected_geoids: "all"\n' + ' - subpop: "all"\n' )) if(!all(is.na(dat$spatial_groups)) & !all(is.null(dat$spatial_groups))){ cat(paste0( @@ -162,17 +162,17 @@ yaml_mtr_template <- function(dat){ } } - if(template=="MultiTimeReduce" & !geoid_all){ + if(template=="MultiPeriodModifier" & !subpop_all){ cat(paste0( " ", dat$name[1], ":\n", - " template: MultiTimeReduce\n", + " template: MultiPeriodModifier\n", " parameter: ", dat$parameter[1], "\n", " groups:\n" )) for(j in 1:nrow(dat)){ cat(paste0( - ' - affected_geoids: ["', dat$geoid[j], '"]\n')) + ' - subpop: ["', dat$subpop[j], '"]\n')) if(!all(is.na(dat$spatial_groups)) & !all(is.null(dat$spatial_groups))){ cat(paste0( @@ -354,9 +354,9 @@ print_value1 <- function(value_type, value_dist, value_mean, -#' Print intervention text for Reduce interventions +#' Print intervention text for SinglePeriodModifier interventions #' -#' @param dat df row for an intervention with the Reduce, ReduceR0 or ReduceIntervention template that has been processed name/period; see collapsed_intervention. +#' @param dat df row for an intervention with the SinglePeriodModifier or ModifierModifier template that has been processed name/period; see collapsed_intervention. #' #' @return #' @export @@ -368,13 +368,13 @@ yaml_reduce_template<- function(dat){ cat(paste0( " ", dat$name, ":\n", " template: ", dat$template,"\n", - if(dat$template %in% c("Reduce", "ReduceIntervention")){ + if(dat$template %in% c("SinglePeriodModifier", "ModifierModifier")){ paste0(" parameter: ", dat$parameter, "\n") }, - if(all(dat$geoid == "all")){ - ' affected_geoids: "all"\n' + if(all(dat$subpop == "all")){ + ' subpop: "all"\n' } else { - paste0(' affected_geoids: ["', dat$geoid, '"]\n') + paste0(' subpop: ["', dat$subpop, '"]\n') }, if(!all(is.na(dat$spatial_groups)) & !all(is.null(dat$spatial_groups))){ if(all(dat$spatial_groups == "all")){ @@ -385,7 +385,7 @@ yaml_reduce_template<- function(dat){ } }, dat$period, - if(dat$template == "ReduceIntervention"){ + if(dat$template == "ModifierModifier"){ paste0(" baseline_scenario: ", dat$baseline_scenario, "\n") } )) @@ -426,7 +426,7 @@ yaml_reduce_template<- function(dat){ yaml_stack1 <- function (dat, scenario = "Inference", stack = TRUE){ if (stack) { - dat <- dat %>% dplyr::group_by(category, USPS, geoid) %>% + dat <- dat %>% dplyr::group_by(category, USPS, subpop) %>% dplyr::filter(category == "NPI_redux" & period == max(period)) %>% dplyr::bind_rows(dat %>% dplyr::filter(category != "NPI_redux")) %>% dplyr::distinct(name, category) %>% @@ -441,18 +441,18 @@ yaml_stack1 <- function (dat, scenario = "Inference", stack = TRUE){ next } - cat(paste0(" ", dat$category[i], ":\n", " template: Stacked\n", + cat(paste0(" ", dat$category[i], ":\n", " template: StackedModifier\n", " scenarios: [\"", dat$name[i], "\"]\n")) } dat <- dat %>% dplyr::filter(category != "base_npi") %>% dplyr::mutate(category = dplyr::if_else(category == "NPI_redux", name, category)) - cat(paste0(" ", scenario, ":\n", " template: Stacked\n", + cat(paste0(" ", scenario, ":\n", " template: StackedModifier\n", " scenarios: [\"", paste0(dat$category, collapse = "\", \""), "\"]\n")) } else { - dat <- dat %>% dplyr::group_by(category, USPS, geoid) %>% + dat <- dat %>% dplyr::group_by(category, USPS, subpop) %>% dplyr::filter(category == "NPI_redux" & period == max(period)) %>% dplyr::bind_rows(dat %>% dplyr::filter(category != "NPI_redux")) %>% dplyr::distinct(name, category) %>% @@ -461,7 +461,7 @@ yaml_stack1 <- function (dat, scenario = "Inference", stack = TRUE){ if (duplicate_names > 1) { stop("At least one intervention name is shared by distinct NPIs.") } - cat(paste0(" ", scenario, ":\n", " template: Stacked\n", + cat(paste0(" ", scenario, ":\n", " template: StackedModifier\n", " scenarios: [\"", paste0(dat, collapse = "\", \""), "\"]\n")) } @@ -484,7 +484,7 @@ yaml_stack2 <- function (dat, scenario = "Inference", stack = TRUE){ if (stack) { dat <- dat %>% - dplyr::group_by(category, USPS, geoid) %>% + dplyr::group_by(category, USPS, subpop) %>% dplyr::filter(category == "NPI_redux" & period == max(period)) %>% dplyr::bind_rows(dat %>% dplyr::filter(category != "NPI_redux")) %>% dplyr::distinct(name, category) %>% dplyr::group_by(category) %>% dplyr::summarize(name = paste0(unique(name), collapse = "\", \"")) @@ -497,16 +497,16 @@ yaml_stack2 <- function (dat, scenario = "Inference", stack = TRUE){ if (dat$category[i] %in% c("local_variance", "NPI_redux")) { next } - cat(paste0(" ", dat$category[i], ":\n", " template: Stacked\n", + cat(paste0(" ", dat$category[i], ":\n", " template: StackedModifier\n", " scenarios: [\"", dat$name[i], "\"]\n")) } dat <- dat %>% dplyr::filter(category != "base_npi") %>% dplyr::mutate(category = dplyr::if_else(category == "NPI_redux", name, category)) - cat(paste0(" ", scenario, ":\n", " template: Stacked\n", + cat(paste0(" ", scenario, ":\n", " template: StackedModifier\n", " scenarios: [\"", paste0(dat$category, collapse = "\", \""), "\"]\n")) } else { - dat <- dat %>% dplyr::group_by(category, USPS, geoid) %>% + dat <- dat %>% dplyr::group_by(category, USPS, subpop) %>% dplyr::filter(category == "NPI_redux" & period == max(period)) %>% dplyr::bind_rows(dat %>% dplyr::filter(category != "NPI_redux")) %>% dplyr::distinct(name, category) %>% @@ -515,7 +515,7 @@ yaml_stack2 <- function (dat, scenario = "Inference", stack = TRUE){ if (duplicate_names > 1) { stop("At least one intervention name is shared by distinct NPIs.") } - cat(paste0(" ", scenario, ":\n", " template: Stacked\n", + cat(paste0(" ", scenario, ":\n", " template: StackedModifier\n", " scenarios: [\"", paste0(dat, collapse = "\", \""), "\"]\n")) } @@ -585,11 +585,11 @@ print_header <- function ( #' @description Prints the global options and the spatial setup section of the configuration files. These typically sit at the top of the configuration file. #' #' @param census_year integer(year) -#' @param sim_states vector of locations that will be modeled -#' @param geodata_file path to file relative to data_path Geodata is a .csv with column headers, with at least two columns: nodenames and popnodes -#' @param popnodes is the name of a column in geodata that specifies the population of the nodenames column -#' @param nodenames is the name of a column in geodata that specifies the geo IDs of an area. This column must be unique. -#' @param mobility_file path to file relative to data_path. The mobility file is a .csv file (it has to contains .csv as extension) with long form comma separated values. Columns have to be named ori, dest, amount with amount being the amount of individual going from place ori to place dest. Unassigned relations are assumed to be zero. ori and dest should match exactly the nodenames column in geodata.csv. It is also possible, but NOT RECOMMENDED to specify the mobility file as a .txt with space-separated values in the shape of a matrix. This matrix is symmetric and of size K x K, with K being the number of rows in geodata. +#' @param modeled_states vector of sub-populations (i.e., locations) that will be modeled. This can be different from the subpop IDs. For the US, state abbreviations are often used. This component is only used for filtering the data to the set of populations. +#' @param geodata_file path to file relative to data_path Geodata is a .csv with column headers, with at least two columns: subpop and popnodes +#' @param popnodes is the name of a column in geodata that specifies the population of the subpop column +#' @param subpop is the name of a column in geodata that specifies the geo IDs of an area. This column must be unique. +#' @param mobility_file path to file relative to data_path. The mobility file is a .csv file (it has to contains .csv as extension) with long form comma separated values. Columns have to be named ori, dest, amount with amount being the amount of individual going from place ori to place dest. Unassigned relations are assumed to be zero. ori and dest should match exactly the subpop column in geodata.csv. It is also possible, but NOT RECOMMENDED to specify the mobility file as a .txt with space-separated values in the shape of a matrix. This matrix is symmetric and of size K x K, with K being the number of rows in geodata. #' @param state_level whether this is a state-level run #' #' @return @@ -599,23 +599,20 @@ print_header <- function ( #' print_spatial_setup <- function ( census_year = 2019, - sim_states, + modeled_states = NULL, geodata_file = "geodata.csv", mobility_file = "mobility.csv", - popnodes = "pop2019est", - nodenames = "geoid", state_level = TRUE) { cat( paste0("spatial_setup:\n", - " census_year: ", census_year, "\n", - " modeled_states:\n"), - paste0(" - ", sim_states, "\n"), + " census_year: ", census_year, "\n"), + ifelse(!is.null(modeled_states), + paste0(" modeled_states:\n", + " - ", modeled_states, "\n"),""), paste0("\n", " geodata: ", geodata_file, "\n", " mobility: ", mobility_file, "\n", - " popnodes: ", popnodes, "\n", - " nodenames: ", nodenames, "\n", " state_level: ", state_level, "\n", "\n") ) @@ -684,7 +681,7 @@ print_compartments <- function ( #' @param fix_added_seeding #' #' @details -#' ## The model performns inference on the seeding date and initial number of seeding infections in each geoid with the default settings +#' ## The model performns inference on the seeding date and initial number of seeding infections in each subpop with the default settings #' ## The method for determining the proposal distribution for the seeding amount is hard-coded in the inference package (R/pkgs/inference/R/functions/perturb_seeding.R). It is pertubed with a normal distribution where the mean of the distribution 10 times the number of confirmed cases on a given date and the standard deviation is 1. #' #' @return @@ -1019,7 +1016,7 @@ print_interventions <- function ( for (i in 1:nrow(dat)) { if (i > nrow(dat)) break - if (dat$template[i] == "MultiTimeReduce") { + if (dat$template[i] == "MultiPeriodModifier") { dat %>% dplyr::filter(name == dat$name[i]) %>% yaml_mtr_template(.) dat <- dat %>% dplyr::filter(name != dat$name[i] | dplyr::row_number() == i) } else { @@ -1036,7 +1033,7 @@ print_interventions <- function ( for (i in 1:nrow(outcome_dat)) { if (i > nrow(outcome_dat)) break - if (outcome_dat$template[i] == "MultiTimeReduce") { + if (outcome_dat$template[i] == "MultiPeriodModifier") { outcome_dat %>% dplyr::filter(name == outcome_dat$name[i]) %>% yaml_mtr_template(.) outcome_dat <- outcome_dat %>% @@ -1120,7 +1117,7 @@ print_interventions <- function ( print_outcomes <- function (resume_modifier = NULL, dat = NULL, ifr = NULL, outcomes_base_data = NULL, param_from_file = TRUE, - outcomes_parquet_file = "usa-geoid-params-output_statelevel.parquet", + outcomes_parquet_file = "usa-subpop-params-output_statelevel.parquet", incidH_prob_dist = "fixed", incidH_prob_value = 0.0175, incidH_delay_dist = "fixed", incidH_delay_value = 7, incidH_duration_dist = "fixed", incidH_duration_value = 7, incidD_prob_dist = "fixed", incidD_prob_value = 0.005, @@ -1401,7 +1398,7 @@ print_outcomes <- function (resume_modifier = NULL, cat(paste0(" interventions:\n", " settings:\n", " ", ifr, ":\n", - " template: Stacked\n", + " template: StackedModifier\n", " scenarios: [\"outcome_interventions\"]\n")) } @@ -1451,7 +1448,7 @@ print_outcomes <- function (resume_modifier = NULL, cat(paste0(" interventions:\n", " settings:\n", " ", ifr, ":\n", - " template: Stacked\n", + " template: StackedModifier\n", " scenarios: [\"", outcome_interventions, "\"]\n")) } } @@ -1989,3 +1986,50 @@ seir_chunk <- function(resume_modifier = NULL, return(tmp) } + + + +#' print_init_conditions +#' +#' @description Print initial conditions section of config +#' +#' @param method +#' @param proportional +#' @param perturbation if TRUE, will print perturbation section, requires other values below +#' @param pert_dist distribution of the perturbation +#' @param pert_mean mean of perturbation +#' @param pert_sd standard deviation of perturbation +#' @param pert_a minimum value of perturbation +#' @param pert_b maximum value of perturbation +#' +#' @details +#' Config helper to print initial conditions section +#' @export +#' +#' @examples +#' print_init_conditions() +#' +print_init_conditions <- function(method = "SetInitialConditionsFolderDraw", + proportional = "True", + perturbation = TRUE, + pert_dist = "truncnorm", + pert_mean = 0, + pert_sd = 0.02, + pert_a = -1, + pert_b = 1){ + + cat(paste0("initial_conditions: \n", + " method: ", method, "\n", + " proportional: ", proportional, "\n", + ifelse(perturbation, paste0(" perturbation: \n", + " distribution: ", pert_dist, "\n", + " mean: ", pert_mean, "\n", + " sd: ", pert_sd, "\n", + " a: ", pert_a, "\n", + " b: ", pert_b), + "\n") + )) + +} + + diff --git a/flepimop/R_packages/config.writer/tests/testthat/geodata.csv b/flepimop/R_packages/config.writer/tests/testthat/geodata.csv index 2f457db74..e1b497990 100644 --- a/flepimop/R_packages/config.writer/tests/testthat/geodata.csv +++ b/flepimop/R_packages/config.writer/tests/testthat/geodata.csv @@ -1,3 +1,3 @@ -"USPS","geoid","pop2019est" +"USPS","subpop","population" "DE","10000",957248 "KS","20000",2910652 diff --git a/flepimop/R_packages/config.writer/tests/testthat/outcome_adj.csv b/flepimop/R_packages/config.writer/tests/testthat/outcome_adj.csv index e377f4529..fb60b8264 100644 --- a/flepimop/R_packages/config.writer/tests/testthat/outcome_adj.csv +++ b/flepimop/R_packages/config.writer/tests/testthat/outcome_adj.csv @@ -1,4 +1,4 @@ -USPS,geoid,start_date,end_date,month,year,prob,prob_base,var,prob_redux,month_num,scenario +USPS,subpop,start_date,end_date,month,year,prob,prob_base,var,prob_redux,month_num,scenario DE,10000,2021-01-01,2021-01-31,Jan,2021,0.006144628617372787,0.00682136946726949,rr_death_inf,0.9008,1,2 DE,10000,2021-02-01,2021-02-28,Feb,2021,0.0056954013310463025,0.00682136946726949,rr_death_inf,0.8349,2,2 DE,10000,2021-03-01,2021-03-31,Mar,2021,0.00452485652648077,0.00682136946726949,rr_death_inf,0.6633,3,2 diff --git a/flepimop/R_packages/config.writer/tests/testthat/processed_intervention_data.csv b/flepimop/R_packages/config.writer/tests/testthat/processed_intervention_data.csv index 20fe42eba..9010c40a0 100644 --- a/flepimop/R_packages/config.writer/tests/testthat/processed_intervention_data.csv +++ b/flepimop/R_packages/config.writer/tests/testthat/processed_intervention_data.csv @@ -1,1491 +1,1491 @@ -USPS,geoid,start_date,end_date,name,template,type,category,parameter,baseline_scenario,value_dist,value_mean,value_sd,value_a,value_b,pert_dist,pert_mean,pert_sd,pert_a,pert_b -AL,01000,2020-04-04,2020-04-30,lockdown,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AL,01000,2020-05-01,2020-05-21,open_p1,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AL,01000,2020-05-22,2020-07-15,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AL,01000,2020-07-16,2021-03-03,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AL,01000,2021-03-04,2021-04-08,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AL,01000,2021-04-09,2021-05-30,open_p4,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AL,01000,2021-05-31,2021-08-07,open_p5,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AK,02000,2020-03-28,2020-04-23,lockdown,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AK,02000,2020-04-24,2020-05-07,open_p1,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AK,02000,2020-05-08,2020-05-21,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AK,02000,2020-05-22,2020-11-15,open_p4,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AK,02000,2020-11-16,2021-02-14,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AK,02000,2021-02-15,2021-08-07,open_p4,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AZ,04000,2020-03-31,2020-05-15,lockdown,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AZ,04000,2020-05-16,2020-06-28,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AZ,04000,2020-06-29,2020-10-01,open_p1,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AZ,04000,2020-10-02,2020-12-02,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AZ,04000,2020-12-03,2021-03-04,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AZ,04000,2021-03-05,2021-03-24,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AZ,04000,2021-03-25,2021-08-07,open_p4,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AR,05000,2020-03-20,2020-05-03,sd,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AR,05000,2020-05-04,2020-06-14,open_p1,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AR,05000,2020-06-15,2020-07-19,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AR,05000,2020-07-20,2020-11-18,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AR,05000,2020-11-19,2021-01-01,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AR,05000,2021-01-02,2021-02-25,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -AR,05000,2021-02-26,2021-03-30,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 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-WA,53000,2021-02-14,2021-03-21,open_p4,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WA,53000,2021-03-22,2021-05-12,open_p5,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WA,53000,2021-05-13,2021-05-17,open_p6,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WA,53000,2021-05-18,2021-08-07,open_p6,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2020-03-24,2020-05-03,lockdown,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2020-05-04,2020-05-20,open_p1,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2020-05-21,2020-06-04,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2020-06-05,2020-06-30,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2020-07-01,2020-07-13,open_p4,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2020-07-14,2020-10-12,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2020-10-13,2020-11-25,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2020-11-26,2021-02-13,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2021-02-14,2021-03-04,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2021-03-05,2021-04-19,open_p4,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2021-04-20,2021-05-13,open_p5,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2021-05-14,2021-06-07,open_p6,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2021-06-08,2021-06-19,open_p6,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WV,54000,2021-06-20,2021-08-07,open_p6,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WI,55000,2020-03-25,2020-05-13,lockdown,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WI,55000,2020-05-14,2020-06-12,open_p1,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WI,55000,2020-06-13,2020-07-31,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WI,55000,2020-08-01,2020-10-28,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WI,55000,2020-10-29,2021-01-12,open_p1,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WI,55000,2021-01-13,2021-02-08,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WI,55000,2021-02-09,2021-03-18,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WI,55000,2021-03-19,2021-03-30,open_p4,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WI,55000,2021-03-31,2021-05-31,open_p5,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WI,55000,2021-06-01,2021-08-07,open_p5,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2020-03-28,2020-04-30,sd,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2020-05-01,2020-05-14,open_p1,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2020-05-15,2020-06-14,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2020-06-15,2020-08-15,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2020-08-16,2020-11-23,open_p4,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2020-11-24,2020-12-08,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2020-12-09,2021-01-08,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2021-01-09,2021-01-25,open_p2,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2021-01-26,2021-02-14,open_p3,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2021-02-15,2021-02-28,open_p5,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2021-03-01,2021-03-15,open_p5,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2021-03-16,2021-05-20,open_p6,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -WY,56000,2021-05-21,2021-08-07,open_p6,MultiTimeReduce,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 -NA,all,2020-01-01,2020-01-31,Seas_jan,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,-0.2,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2021-01-01,2021-01-31,Seas_jan,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,-0.2,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2020-02-01,2020-02-29,Seas_feb,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,-0.133,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2021-02-01,2021-02-28,Seas_feb,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,-0.133,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2020-03-01,2020-03-31,Seas_mar,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,-0.067,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2021-03-01,2021-03-31,Seas_mar,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,-0.067,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2020-05-01,2020-05-31,Seas_may,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,0.067,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2021-05-01,2021-05-31,Seas_may,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,0.067,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2020-06-01,2020-06-30,Seas_jun,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,0.133,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2021-06-01,2021-06-30,Seas_jun,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,0.133,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2020-07-01,2020-07-31,Seas_jul,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,0.2,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2021-07-01,2021-07-31,Seas_jul,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,0.2,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2020-08-01,2020-08-31,Seas_aug,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,0.133,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2021-08-01,2021-08-07,Seas_aug,MultiTimeReduce,transmission,seasonal,R0,NA,truncnorm,0.133,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2020-09-01,2020-09-30,Seas_sep,Reduce,transmission,seasonal,R0,NA,truncnorm,0.067,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2020-10-01,2020-10-31,Seas_oct,Reduce,transmission,seasonal,R0,NA,truncnorm,0,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2020-11-01,2020-11-30,Seas_nov,Reduce,transmission,seasonal,R0,NA,truncnorm,-0.067,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2020-12-01,2020-12-31,Seas_dec,Reduce,transmission,seasonal,R0,NA,truncnorm,-0.133,0.05,-1,1,truncnorm,0,0.05,-1,1 -NA,all,2020-01-01,2021-08-07,local_variance,Reduce,transmission,local_variance,R0,NA,truncnorm,0,0.025,-1,1,truncnorm,0,0.05,-1,1 -AK,02000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001575,NA,NA,NA,NA,NA,NA,NA,NA -AK,02000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004632,NA,NA,NA,NA,NA,NA,NA,NA -AK,02000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005033,NA,NA,NA,NA,NA,NA,NA,NA -AK,02000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005206,NA,NA,NA,NA,NA,NA,NA,NA -AK,02000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003905,NA,NA,NA,NA,NA,NA,NA,NA -AK,02000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001637,NA,NA,NA,NA,NA,NA,NA,NA -AK,02000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003683,NA,NA,NA,NA,NA,NA,NA,NA -AK,02000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004457,NA,NA,NA,NA,NA,NA,NA,NA -AL,01000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,1.2e-4,NA,NA,NA,NA,NA,NA,NA,NA -AL,01000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00327,NA,NA,NA,NA,NA,NA,NA,NA -AL,01000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003378,NA,NA,NA,NA,NA,NA,NA,NA -AL,01000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005034,NA,NA,NA,NA,NA,NA,NA,NA -AL,01000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002462,NA,NA,NA,NA,NA,NA,NA,NA -AL,01000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001837,NA,NA,NA,NA,NA,NA,NA,NA -AL,01000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003138,NA,NA,NA,NA,NA,NA,NA,NA -AL,01000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003718,NA,NA,NA,NA,NA,NA,NA,NA -AR,05000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,2.5e-5,NA,NA,NA,NA,NA,NA,NA,NA -AR,05000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004047,NA,NA,NA,NA,NA,NA,NA,NA -AR,05000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003534,NA,NA,NA,NA,NA,NA,NA,NA -AR,05000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005765,NA,NA,NA,NA,NA,NA,NA,NA -AR,05000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002497,NA,NA,NA,NA,NA,NA,NA,NA -AR,05000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002908,NA,NA,NA,NA,NA,NA,NA,NA -AR,05000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004238,NA,NA,NA,NA,NA,NA,NA,NA -AR,05000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004355,NA,NA,NA,NA,NA,NA,NA,NA -AZ,04000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,9.1e-4,NA,NA,NA,NA,NA,NA,NA,NA -AZ,04000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003637,NA,NA,NA,NA,NA,NA,NA,NA -AZ,04000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004542,NA,NA,NA,NA,NA,NA,NA,NA -AZ,04000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006755,NA,NA,NA,NA,NA,NA,NA,NA -AZ,04000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004126,NA,NA,NA,NA,NA,NA,NA,NA -AZ,04000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003358,NA,NA,NA,NA,NA,NA,NA,NA -AZ,04000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003208,NA,NA,NA,NA,NA,NA,NA,NA -AZ,04000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003691,NA,NA,NA,NA,NA,NA,NA,NA -CA,06000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004032,NA,NA,NA,NA,NA,NA,NA,NA -CA,06000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004414,NA,NA,NA,NA,NA,NA,NA,NA -CA,06000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.009529,NA,NA,NA,NA,NA,NA,NA,NA -CA,06000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007473,NA,NA,NA,NA,NA,NA,NA,NA -CA,06000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005734,NA,NA,NA,NA,NA,NA,NA,NA -CA,06000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005427,NA,NA,NA,NA,NA,NA,NA,NA -CA,06000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005324,NA,NA,NA,NA,NA,NA,NA,NA -CO,08000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001223,NA,NA,NA,NA,NA,NA,NA,NA -CO,08000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00289,NA,NA,NA,NA,NA,NA,NA,NA -CO,08000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00442,NA,NA,NA,NA,NA,NA,NA,NA -CO,08000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.009366,NA,NA,NA,NA,NA,NA,NA,NA -CO,08000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006245,NA,NA,NA,NA,NA,NA,NA,NA -CO,08000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005531,NA,NA,NA,NA,NA,NA,NA,NA -CO,08000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005302,NA,NA,NA,NA,NA,NA,NA,NA -CO,08000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005107,NA,NA,NA,NA,NA,NA,NA,NA -CT,09000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001444,NA,NA,NA,NA,NA,NA,NA,NA -CT,09000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003284,NA,NA,NA,NA,NA,NA,NA,NA -CT,09000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006127,NA,NA,NA,NA,NA,NA,NA,NA -CT,09000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.010163,NA,NA,NA,NA,NA,NA,NA,NA -CT,09000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008513,NA,NA,NA,NA,NA,NA,NA,NA -CT,09000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007132,NA,NA,NA,NA,NA,NA,NA,NA -CT,09000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007648,NA,NA,NA,NA,NA,NA,NA,NA -CT,09000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.0073,NA,NA,NA,NA,NA,NA,NA,NA -DC,11000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004432,NA,NA,NA,NA,NA,NA,NA,NA -DC,11000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002789,NA,NA,NA,NA,NA,NA,NA,NA -DC,11000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.009738,NA,NA,NA,NA,NA,NA,NA,NA -DC,11000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.009489,NA,NA,NA,NA,NA,NA,NA,NA -DC,11000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005403,NA,NA,NA,NA,NA,NA,NA,NA -DC,11000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005846,NA,NA,NA,NA,NA,NA,NA,NA -DC,11000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007962,NA,NA,NA,NA,NA,NA,NA,NA -DE,10000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,4.24e-4,NA,NA,NA,NA,NA,NA,NA,NA -DE,10000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003744,NA,NA,NA,NA,NA,NA,NA,NA -DE,10000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004357,NA,NA,NA,NA,NA,NA,NA,NA -DE,10000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.009041,NA,NA,NA,NA,NA,NA,NA,NA -DE,10000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006471,NA,NA,NA,NA,NA,NA,NA,NA -DE,10000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005204,NA,NA,NA,NA,NA,NA,NA,NA -DE,10000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004372,NA,NA,NA,NA,NA,NA,NA,NA -DE,10000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004788,NA,NA,NA,NA,NA,NA,NA,NA -FL,12000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001333,NA,NA,NA,NA,NA,NA,NA,NA -FL,12000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002584,NA,NA,NA,NA,NA,NA,NA,NA -FL,12000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004256,NA,NA,NA,NA,NA,NA,NA,NA -FL,12000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007515,NA,NA,NA,NA,NA,NA,NA,NA -FL,12000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005339,NA,NA,NA,NA,NA,NA,NA,NA -FL,12000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004656,NA,NA,NA,NA,NA,NA,NA,NA -FL,12000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004632,NA,NA,NA,NA,NA,NA,NA,NA -FL,12000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004264,NA,NA,NA,NA,NA,NA,NA,NA -GA,13000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,2.95e-4,NA,NA,NA,NA,NA,NA,NA,NA -GA,13000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003166,NA,NA,NA,NA,NA,NA,NA,NA -GA,13000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002689,NA,NA,NA,NA,NA,NA,NA,NA -GA,13000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006914,NA,NA,NA,NA,NA,NA,NA,NA -GA,13000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003024,NA,NA,NA,NA,NA,NA,NA,NA -GA,13000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002945,NA,NA,NA,NA,NA,NA,NA,NA -GA,13000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002869,NA,NA,NA,NA,NA,NA,NA,NA -GA,13000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003331,NA,NA,NA,NA,NA,NA,NA,NA -GU,66000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001893,NA,NA,NA,NA,NA,NA,NA,NA -GU,66000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004754,NA,NA,NA,NA,NA,NA,NA,NA -GU,66000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002632,NA,NA,NA,NA,NA,NA,NA,NA -GU,66000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.009422,NA,NA,NA,NA,NA,NA,NA,NA -HI,15000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,2.05e-4,NA,NA,NA,NA,NA,NA,NA,NA -HI,15000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003911,NA,NA,NA,NA,NA,NA,NA,NA -HI,15000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005352,NA,NA,NA,NA,NA,NA,NA,NA -HI,15000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006736,NA,NA,NA,NA,NA,NA,NA,NA -HI,15000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.015824,NA,NA,NA,NA,NA,NA,NA,NA -HI,15000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007606,NA,NA,NA,NA,NA,NA,NA,NA -HI,15000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005033,NA,NA,NA,NA,NA,NA,NA,NA -HI,15000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005334,NA,NA,NA,NA,NA,NA,NA,NA -IA,19000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001032,NA,NA,NA,NA,NA,NA,NA,NA -IA,19000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002585,NA,NA,NA,NA,NA,NA,NA,NA -IA,19000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005662,NA,NA,NA,NA,NA,NA,NA,NA -IA,19000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007657,NA,NA,NA,NA,NA,NA,NA,NA -IA,19000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003995,NA,NA,NA,NA,NA,NA,NA,NA -IA,19000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003701,NA,NA,NA,NA,NA,NA,NA,NA -IA,19000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,5.72e-4,NA,NA,NA,NA,NA,NA,NA,NA -IA,19000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.009231,NA,NA,NA,NA,NA,NA,NA,NA -ID,16000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,7.05e-4,NA,NA,NA,NA,NA,NA,NA,NA -ID,16000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002855,NA,NA,NA,NA,NA,NA,NA,NA -ID,16000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004191,NA,NA,NA,NA,NA,NA,NA,NA -ID,16000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005559,NA,NA,NA,NA,NA,NA,NA,NA -ID,16000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002316,NA,NA,NA,NA,NA,NA,NA,NA -ID,16000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002436,NA,NA,NA,NA,NA,NA,NA,NA -ID,16000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003961,NA,NA,NA,NA,NA,NA,NA,NA -ID,16000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004795,NA,NA,NA,NA,NA,NA,NA,NA -IL,17000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,6.8e-4,NA,NA,NA,NA,NA,NA,NA,NA -IL,17000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003162,NA,NA,NA,NA,NA,NA,NA,NA -IL,17000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004809,NA,NA,NA,NA,NA,NA,NA,NA -IL,17000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008428,NA,NA,NA,NA,NA,NA,NA,NA -IL,17000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006174,NA,NA,NA,NA,NA,NA,NA,NA -IL,17000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005687,NA,NA,NA,NA,NA,NA,NA,NA -IL,17000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00567,NA,NA,NA,NA,NA,NA,NA,NA -IL,17000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005401,NA,NA,NA,NA,NA,NA,NA,NA -IN,18000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001109,NA,NA,NA,NA,NA,NA,NA,NA -IN,18000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003137,NA,NA,NA,NA,NA,NA,NA,NA -IN,18000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003365,NA,NA,NA,NA,NA,NA,NA,NA -IN,18000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005571,NA,NA,NA,NA,NA,NA,NA,NA -IN,18000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003615,NA,NA,NA,NA,NA,NA,NA,NA -IN,18000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003093,NA,NA,NA,NA,NA,NA,NA,NA -IN,18000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003615,NA,NA,NA,NA,NA,NA,NA,NA -IN,18000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003721,NA,NA,NA,NA,NA,NA,NA,NA -KS,20000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,7.55e-4,NA,NA,NA,NA,NA,NA,NA,NA -KS,20000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002627,NA,NA,NA,NA,NA,NA,NA,NA -KS,20000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005016,NA,NA,NA,NA,NA,NA,NA,NA -KS,20000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00819,NA,NA,NA,NA,NA,NA,NA,NA -KS,20000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003088,NA,NA,NA,NA,NA,NA,NA,NA -KS,20000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003067,NA,NA,NA,NA,NA,NA,NA,NA -KS,20000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004307,NA,NA,NA,NA,NA,NA,NA,NA -KS,20000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005069,NA,NA,NA,NA,NA,NA,NA,NA -KY,21000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,4.42e-4,NA,NA,NA,NA,NA,NA,NA,NA -KY,21000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003479,NA,NA,NA,NA,NA,NA,NA,NA -KY,21000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005304,NA,NA,NA,NA,NA,NA,NA,NA -KY,21000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006686,NA,NA,NA,NA,NA,NA,NA,NA -KY,21000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003199,NA,NA,NA,NA,NA,NA,NA,NA -KY,21000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003151,NA,NA,NA,NA,NA,NA,NA,NA -KY,21000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002638,NA,NA,NA,NA,NA,NA,NA,NA -KY,21000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003136,NA,NA,NA,NA,NA,NA,NA,NA -LA,22000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001033,NA,NA,NA,NA,NA,NA,NA,NA -LA,22000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002887,NA,NA,NA,NA,NA,NA,NA,NA -LA,22000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003833,NA,NA,NA,NA,NA,NA,NA,NA -LA,22000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004371,NA,NA,NA,NA,NA,NA,NA,NA -LA,22000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001721,NA,NA,NA,NA,NA,NA,NA,NA -LA,22000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.0018,NA,NA,NA,NA,NA,NA,NA,NA -LA,22000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001898,NA,NA,NA,NA,NA,NA,NA,NA -LA,22000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.0022,NA,NA,NA,NA,NA,NA,NA,NA -MA,25000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,7.7e-4,NA,NA,NA,NA,NA,NA,NA,NA -MA,25000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003447,NA,NA,NA,NA,NA,NA,NA,NA -MA,25000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00593,NA,NA,NA,NA,NA,NA,NA,NA -MA,25000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.010795,NA,NA,NA,NA,NA,NA,NA,NA -MA,25000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.011708,NA,NA,NA,NA,NA,NA,NA,NA -MA,25000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008408,NA,NA,NA,NA,NA,NA,NA,NA -MA,25000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00671,NA,NA,NA,NA,NA,NA,NA,NA -MA,25000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004521,NA,NA,NA,NA,NA,NA,NA,NA -MD,24000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001068,NA,NA,NA,NA,NA,NA,NA,NA -MD,24000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002659,NA,NA,NA,NA,NA,NA,NA,NA -MD,24000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005003,NA,NA,NA,NA,NA,NA,NA,NA -MD,24000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.009014,NA,NA,NA,NA,NA,NA,NA,NA -MD,24000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007697,NA,NA,NA,NA,NA,NA,NA,NA -MD,24000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006153,NA,NA,NA,NA,NA,NA,NA,NA -MD,24000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006499,NA,NA,NA,NA,NA,NA,NA,NA -MD,24000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00596,NA,NA,NA,NA,NA,NA,NA,NA -ME,23000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001276,NA,NA,NA,NA,NA,NA,NA,NA -ME,23000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00329,NA,NA,NA,NA,NA,NA,NA,NA -ME,23000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005684,NA,NA,NA,NA,NA,NA,NA,NA -ME,23000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.010999,NA,NA,NA,NA,NA,NA,NA,NA -ME,23000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008499,NA,NA,NA,NA,NA,NA,NA,NA -ME,23000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007334,NA,NA,NA,NA,NA,NA,NA,NA -ME,23000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008344,NA,NA,NA,NA,NA,NA,NA,NA -ME,23000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008117,NA,NA,NA,NA,NA,NA,NA,NA -MI,26000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001021,NA,NA,NA,NA,NA,NA,NA,NA -MI,26000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002897,NA,NA,NA,NA,NA,NA,NA,NA -MI,26000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004235,NA,NA,NA,NA,NA,NA,NA,NA -MI,26000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007429,NA,NA,NA,NA,NA,NA,NA,NA -MI,26000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004843,NA,NA,NA,NA,NA,NA,NA,NA -MI,26000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003954,NA,NA,NA,NA,NA,NA,NA,NA -MI,26000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004991,NA,NA,NA,NA,NA,NA,NA,NA -MI,26000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005088,NA,NA,NA,NA,NA,NA,NA,NA -MN,27000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,8.75e-4,NA,NA,NA,NA,NA,NA,NA,NA -MN,27000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003259,NA,NA,NA,NA,NA,NA,NA,NA -MN,27000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005394,NA,NA,NA,NA,NA,NA,NA,NA -MN,27000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008294,NA,NA,NA,NA,NA,NA,NA,NA -MN,27000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006285,NA,NA,NA,NA,NA,NA,NA,NA -MN,27000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005101,NA,NA,NA,NA,NA,NA,NA,NA -MN,27000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005772,NA,NA,NA,NA,NA,NA,NA,NA -MN,27000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005718,NA,NA,NA,NA,NA,NA,NA,NA -MO,29000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,8.54e-4,NA,NA,NA,NA,NA,NA,NA,NA -MO,29000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002774,NA,NA,NA,NA,NA,NA,NA,NA -MO,29000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003972,NA,NA,NA,NA,NA,NA,NA,NA -MO,29000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005893,NA,NA,NA,NA,NA,NA,NA,NA -MO,29000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003574,NA,NA,NA,NA,NA,NA,NA,NA -MO,29000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002749,NA,NA,NA,NA,NA,NA,NA,NA -MO,29000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003287,NA,NA,NA,NA,NA,NA,NA,NA -MO,29000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003573,NA,NA,NA,NA,NA,NA,NA,NA -MP,69000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00187,NA,NA,NA,NA,NA,NA,NA,NA -MP,69000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004072,NA,NA,NA,NA,NA,NA,NA,NA -MP,69000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003597,NA,NA,NA,NA,NA,NA,NA,NA -MP,69000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005874,NA,NA,NA,NA,NA,NA,NA,NA -MP,69000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004361,NA,NA,NA,NA,NA,NA,NA,NA -MP,69000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004769,NA,NA,NA,NA,NA,NA,NA,NA -MP,69000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00444,NA,NA,NA,NA,NA,NA,NA,NA -MP,69000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004518,NA,NA,NA,NA,NA,NA,NA,NA -MS,28000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,9.69e-4,NA,NA,NA,NA,NA,NA,NA,NA -MS,28000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002764,NA,NA,NA,NA,NA,NA,NA,NA -MS,28000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003859,NA,NA,NA,NA,NA,NA,NA,NA -MS,28000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003698,NA,NA,NA,NA,NA,NA,NA,NA -MS,28000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002123,NA,NA,NA,NA,NA,NA,NA,NA -MS,28000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001683,NA,NA,NA,NA,NA,NA,NA,NA -MS,28000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002325,NA,NA,NA,NA,NA,NA,NA,NA -MS,28000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003066,NA,NA,NA,NA,NA,NA,NA,NA -MT,30000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,5.83e-4,NA,NA,NA,NA,NA,NA,NA,NA -MT,30000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003727,NA,NA,NA,NA,NA,NA,NA,NA -MT,30000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005204,NA,NA,NA,NA,NA,NA,NA,NA -MT,30000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006569,NA,NA,NA,NA,NA,NA,NA,NA -MT,30000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003107,NA,NA,NA,NA,NA,NA,NA,NA -MT,30000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003141,NA,NA,NA,NA,NA,NA,NA,NA -MT,30000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003945,NA,NA,NA,NA,NA,NA,NA,NA -MT,30000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003914,NA,NA,NA,NA,NA,NA,NA,NA -NC,37000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,6.41e-4,NA,NA,NA,NA,NA,NA,NA,NA -NC,37000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003656,NA,NA,NA,NA,NA,NA,NA,NA -NC,37000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004385,NA,NA,NA,NA,NA,NA,NA,NA -NC,37000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006358,NA,NA,NA,NA,NA,NA,NA,NA -NC,37000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003274,NA,NA,NA,NA,NA,NA,NA,NA -NC,37000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002715,NA,NA,NA,NA,NA,NA,NA,NA -NC,37000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004056,NA,NA,NA,NA,NA,NA,NA,NA -NC,37000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005478,NA,NA,NA,NA,NA,NA,NA,NA -ND,38000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001679,NA,NA,NA,NA,NA,NA,NA,NA -ND,38000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002858,NA,NA,NA,NA,NA,NA,NA,NA -ND,38000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005731,NA,NA,NA,NA,NA,NA,NA,NA -ND,38000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005392,NA,NA,NA,NA,NA,NA,NA,NA -ND,38000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001724,NA,NA,NA,NA,NA,NA,NA,NA -ND,38000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002575,NA,NA,NA,NA,NA,NA,NA,NA -ND,38000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003916,NA,NA,NA,NA,NA,NA,NA,NA -ND,38000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003931,NA,NA,NA,NA,NA,NA,NA,NA -NE,31000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00123,NA,NA,NA,NA,NA,NA,NA,NA -NE,31000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002341,NA,NA,NA,NA,NA,NA,NA,NA -NE,31000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00543,NA,NA,NA,NA,NA,NA,NA,NA -NE,31000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007955,NA,NA,NA,NA,NA,NA,NA,NA -NE,31000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003575,NA,NA,NA,NA,NA,NA,NA,NA -NE,31000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00359,NA,NA,NA,NA,NA,NA,NA,NA -NE,31000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004064,NA,NA,NA,NA,NA,NA,NA,NA -NE,31000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003986,NA,NA,NA,NA,NA,NA,NA,NA -NH,33000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,1.96e-4,NA,NA,NA,NA,NA,NA,NA,NA -NH,33000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003713,NA,NA,NA,NA,NA,NA,NA,NA -NH,33000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006088,NA,NA,NA,NA,NA,NA,NA,NA -NH,33000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.016931,NA,NA,NA,NA,NA,NA,NA,NA -NH,33000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00496,NA,NA,NA,NA,NA,NA,NA,NA -NH,33000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004555,NA,NA,NA,NA,NA,NA,NA,NA -NH,33000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006668,NA,NA,NA,NA,NA,NA,NA,NA -NH,33000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00686,NA,NA,NA,NA,NA,NA,NA,NA -NJ,34000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,9.75e-4,NA,NA,NA,NA,NA,NA,NA,NA -NJ,34000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003007,NA,NA,NA,NA,NA,NA,NA,NA -NJ,34000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00573,NA,NA,NA,NA,NA,NA,NA,NA -NJ,34000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.009843,NA,NA,NA,NA,NA,NA,NA,NA -NJ,34000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007756,NA,NA,NA,NA,NA,NA,NA,NA -NJ,34000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006326,NA,NA,NA,NA,NA,NA,NA,NA -NJ,34000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005596,NA,NA,NA,NA,NA,NA,NA,NA -NJ,34000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005557,NA,NA,NA,NA,NA,NA,NA,NA -NM,35000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005124,NA,NA,NA,NA,NA,NA,NA,NA -NM,35000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007049,NA,NA,NA,NA,NA,NA,NA,NA -NM,35000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008913,NA,NA,NA,NA,NA,NA,NA,NA -NM,35000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005217,NA,NA,NA,NA,NA,NA,NA,NA -NM,35000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005211,NA,NA,NA,NA,NA,NA,NA,NA -NM,35000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006225,NA,NA,NA,NA,NA,NA,NA,NA -NM,35000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007262,NA,NA,NA,NA,NA,NA,NA,NA -NV,32000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,1.07e-4,NA,NA,NA,NA,NA,NA,NA,NA -NV,32000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00392,NA,NA,NA,NA,NA,NA,NA,NA -NV,32000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004457,NA,NA,NA,NA,NA,NA,NA,NA -NV,32000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006877,NA,NA,NA,NA,NA,NA,NA,NA -NV,32000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004096,NA,NA,NA,NA,NA,NA,NA,NA -NV,32000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003606,NA,NA,NA,NA,NA,NA,NA,NA -NV,32000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003599,NA,NA,NA,NA,NA,NA,NA,NA -NV,32000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00424,NA,NA,NA,NA,NA,NA,NA,NA -NY,36000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,4.63e-4,NA,NA,NA,NA,NA,NA,NA,NA -NY,36000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003562,NA,NA,NA,NA,NA,NA,NA,NA -NY,36000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004922,NA,NA,NA,NA,NA,NA,NA,NA -NY,36000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008693,NA,NA,NA,NA,NA,NA,NA,NA -NY,36000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006354,NA,NA,NA,NA,NA,NA,NA,NA -NY,36000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005819,NA,NA,NA,NA,NA,NA,NA,NA -NY,36000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005997,NA,NA,NA,NA,NA,NA,NA,NA -NY,36000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005131,NA,NA,NA,NA,NA,NA,NA,NA -OH,39000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001169,NA,NA,NA,NA,NA,NA,NA,NA -OH,39000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00267,NA,NA,NA,NA,NA,NA,NA,NA -OH,39000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004276,NA,NA,NA,NA,NA,NA,NA,NA -OH,39000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007267,NA,NA,NA,NA,NA,NA,NA,NA -OH,39000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.0034,NA,NA,NA,NA,NA,NA,NA,NA -OH,39000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003255,NA,NA,NA,NA,NA,NA,NA,NA -OH,39000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003161,NA,NA,NA,NA,NA,NA,NA,NA -OH,39000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003561,NA,NA,NA,NA,NA,NA,NA,NA -OK,40000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001114,NA,NA,NA,NA,NA,NA,NA,NA -OK,40000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003242,NA,NA,NA,NA,NA,NA,NA,NA -OK,40000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005228,NA,NA,NA,NA,NA,NA,NA,NA -OK,40000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005614,NA,NA,NA,NA,NA,NA,NA,NA -OK,40000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002007,NA,NA,NA,NA,NA,NA,NA,NA -OK,40000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002001,NA,NA,NA,NA,NA,NA,NA,NA -OK,40000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002067,NA,NA,NA,NA,NA,NA,NA,NA -OK,40000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002009,NA,NA,NA,NA,NA,NA,NA,NA -OR,41000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001191,NA,NA,NA,NA,NA,NA,NA,NA -OR,41000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002842,NA,NA,NA,NA,NA,NA,NA,NA -OR,41000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004293,NA,NA,NA,NA,NA,NA,NA,NA -OR,41000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007712,NA,NA,NA,NA,NA,NA,NA,NA -OR,41000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007987,NA,NA,NA,NA,NA,NA,NA,NA -OR,41000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006005,NA,NA,NA,NA,NA,NA,NA,NA -OR,41000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004637,NA,NA,NA,NA,NA,NA,NA,NA -OR,41000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003582,NA,NA,NA,NA,NA,NA,NA,NA -PA,42000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,7.98e-4,NA,NA,NA,NA,NA,NA,NA,NA -PA,42000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002889,NA,NA,NA,NA,NA,NA,NA,NA -PA,42000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005107,NA,NA,NA,NA,NA,NA,NA,NA -PA,42000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.009101,NA,NA,NA,NA,NA,NA,NA,NA -PA,42000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008397,NA,NA,NA,NA,NA,NA,NA,NA -PA,42000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005664,NA,NA,NA,NA,NA,NA,NA,NA -PA,42000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005252,NA,NA,NA,NA,NA,NA,NA,NA -PA,42000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00518,NA,NA,NA,NA,NA,NA,NA,NA -PR,72000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,1.2e-4,NA,NA,NA,NA,NA,NA,NA,NA -PR,72000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002806,NA,NA,NA,NA,NA,NA,NA,NA -PR,72000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002673,NA,NA,NA,NA,NA,NA,NA,NA -PR,72000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005806,NA,NA,NA,NA,NA,NA,NA,NA -PR,72000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007686,NA,NA,NA,NA,NA,NA,NA,NA -PR,72000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.010066,NA,NA,NA,NA,NA,NA,NA,NA -PR,72000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005251,NA,NA,NA,NA,NA,NA,NA,NA -PR,72000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003103,NA,NA,NA,NA,NA,NA,NA,NA -RI,44000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001005,NA,NA,NA,NA,NA,NA,NA,NA -RI,44000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002291,NA,NA,NA,NA,NA,NA,NA,NA -RI,44000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007043,NA,NA,NA,NA,NA,NA,NA,NA -RI,44000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008476,NA,NA,NA,NA,NA,NA,NA,NA -RI,44000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.009584,NA,NA,NA,NA,NA,NA,NA,NA -RI,44000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00624,NA,NA,NA,NA,NA,NA,NA,NA -RI,44000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005759,NA,NA,NA,NA,NA,NA,NA,NA -RI,44000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004904,NA,NA,NA,NA,NA,NA,NA,NA -SC,45000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,6.52e-4,NA,NA,NA,NA,NA,NA,NA,NA -SC,45000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003076,NA,NA,NA,NA,NA,NA,NA,NA -SC,45000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004293,NA,NA,NA,NA,NA,NA,NA,NA -SC,45000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006142,NA,NA,NA,NA,NA,NA,NA,NA -SC,45000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002733,NA,NA,NA,NA,NA,NA,NA,NA -SC,45000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002738,NA,NA,NA,NA,NA,NA,NA,NA -SC,45000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003436,NA,NA,NA,NA,NA,NA,NA,NA -SC,45000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003906,NA,NA,NA,NA,NA,NA,NA,NA -SD,46000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001286,NA,NA,NA,NA,NA,NA,NA,NA -SD,46000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003045,NA,NA,NA,NA,NA,NA,NA,NA -SD,46000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006832,NA,NA,NA,NA,NA,NA,NA,NA -SD,46000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007449,NA,NA,NA,NA,NA,NA,NA,NA -SD,46000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002513,NA,NA,NA,NA,NA,NA,NA,NA -SD,46000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003085,NA,NA,NA,NA,NA,NA,NA,NA -SD,46000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004862,NA,NA,NA,NA,NA,NA,NA,NA -SD,46000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005295,NA,NA,NA,NA,NA,NA,NA,NA -TN,47000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001256,NA,NA,NA,NA,NA,NA,NA,NA -TN,47000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002297,NA,NA,NA,NA,NA,NA,NA,NA -TN,47000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003566,NA,NA,NA,NA,NA,NA,NA,NA -TN,47000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005577,NA,NA,NA,NA,NA,NA,NA,NA -TN,47000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003139,NA,NA,NA,NA,NA,NA,NA,NA -TN,47000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002314,NA,NA,NA,NA,NA,NA,NA,NA -TN,47000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002869,NA,NA,NA,NA,NA,NA,NA,NA -TN,47000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003197,NA,NA,NA,NA,NA,NA,NA,NA -TX,48000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00104,NA,NA,NA,NA,NA,NA,NA,NA -TX,48000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002662,NA,NA,NA,NA,NA,NA,NA,NA -TX,48000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00386,NA,NA,NA,NA,NA,NA,NA,NA -TX,48000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006748,NA,NA,NA,NA,NA,NA,NA,NA -TX,48000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003813,NA,NA,NA,NA,NA,NA,NA,NA -TX,48000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003763,NA,NA,NA,NA,NA,NA,NA,NA -TX,48000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003366,NA,NA,NA,NA,NA,NA,NA,NA -TX,48000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003568,NA,NA,NA,NA,NA,NA,NA,NA -UT,49000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001195,NA,NA,NA,NA,NA,NA,NA,NA -UT,49000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002924,NA,NA,NA,NA,NA,NA,NA,NA -UT,49000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003472,NA,NA,NA,NA,NA,NA,NA,NA -UT,49000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007139,NA,NA,NA,NA,NA,NA,NA,NA -UT,49000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00447,NA,NA,NA,NA,NA,NA,NA,NA -UT,49000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003338,NA,NA,NA,NA,NA,NA,NA,NA -UT,49000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003455,NA,NA,NA,NA,NA,NA,NA,NA -UT,49000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004197,NA,NA,NA,NA,NA,NA,NA,NA -VA,51000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,9.2e-4,NA,NA,NA,NA,NA,NA,NA,NA -VA,51000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003394,NA,NA,NA,NA,NA,NA,NA,NA -VA,51000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004607,NA,NA,NA,NA,NA,NA,NA,NA -VA,51000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008845,NA,NA,NA,NA,NA,NA,NA,NA -VA,51000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006556,NA,NA,NA,NA,NA,NA,NA,NA -VA,51000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005956,NA,NA,NA,NA,NA,NA,NA,NA -VA,51000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007471,NA,NA,NA,NA,NA,NA,NA,NA -VA,51000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008116,NA,NA,NA,NA,NA,NA,NA,NA -VI,78000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,3.92e-4,NA,NA,NA,NA,NA,NA,NA,NA -VI,78000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002511,NA,NA,NA,NA,NA,NA,NA,NA -VI,78000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003722,NA,NA,NA,NA,NA,NA,NA,NA -VI,78000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.0047,NA,NA,NA,NA,NA,NA,NA,NA -VI,78000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002398,NA,NA,NA,NA,NA,NA,NA,NA -VI,78000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00255,NA,NA,NA,NA,NA,NA,NA,NA -VI,78000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003405,NA,NA,NA,NA,NA,NA,NA,NA -VI,78000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003368,NA,NA,NA,NA,NA,NA,NA,NA -VT,50000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001255,NA,NA,NA,NA,NA,NA,NA,NA -VT,50000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002859,NA,NA,NA,NA,NA,NA,NA,NA -VT,50000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005581,NA,NA,NA,NA,NA,NA,NA,NA -VT,50000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.010141,NA,NA,NA,NA,NA,NA,NA,NA -VT,50000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.014482,NA,NA,NA,NA,NA,NA,NA,NA -VT,50000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008818,NA,NA,NA,NA,NA,NA,NA,NA -VT,50000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003411,NA,NA,NA,NA,NA,NA,NA,NA -VT,50000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003076,NA,NA,NA,NA,NA,NA,NA,NA -WA,53000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,5.61e-4,NA,NA,NA,NA,NA,NA,NA,NA -WA,53000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003633,NA,NA,NA,NA,NA,NA,NA,NA -WA,53000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004755,NA,NA,NA,NA,NA,NA,NA,NA -WA,53000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008176,NA,NA,NA,NA,NA,NA,NA,NA -WA,53000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008216,NA,NA,NA,NA,NA,NA,NA,NA -WA,53000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.007167,NA,NA,NA,NA,NA,NA,NA,NA -WA,53000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.006675,NA,NA,NA,NA,NA,NA,NA,NA -WA,53000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005865,NA,NA,NA,NA,NA,NA,NA,NA -WI,55000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,8.73e-4,NA,NA,NA,NA,NA,NA,NA,NA -WI,55000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003428,NA,NA,NA,NA,NA,NA,NA,NA -WI,55000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004815,NA,NA,NA,NA,NA,NA,NA,NA -WI,55000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.008678,NA,NA,NA,NA,NA,NA,NA,NA -WI,55000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004268,NA,NA,NA,NA,NA,NA,NA,NA -WI,55000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004013,NA,NA,NA,NA,NA,NA,NA,NA -WI,55000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004666,NA,NA,NA,NA,NA,NA,NA,NA -WI,55000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005008,NA,NA,NA,NA,NA,NA,NA,NA -WV,54000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001851,NA,NA,NA,NA,NA,NA,NA,NA -WV,54000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.002902,NA,NA,NA,NA,NA,NA,NA,NA -WV,54000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004229,NA,NA,NA,NA,NA,NA,NA,NA -WV,54000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004682,NA,NA,NA,NA,NA,NA,NA,NA -WV,54000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003996,NA,NA,NA,NA,NA,NA,NA,NA -WV,54000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003008,NA,NA,NA,NA,NA,NA,NA,NA -WV,54000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003992,NA,NA,NA,NA,NA,NA,NA,NA -WV,54000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003925,NA,NA,NA,NA,NA,NA,NA,NA -WY,56000,2021-01-01,2021-01-31,Dose1_jan2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.001042,NA,NA,NA,NA,NA,NA,NA,NA -WY,56000,2021-02-01,2021-02-28,Dose1_feb2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00325,NA,NA,NA,NA,NA,NA,NA,NA -WY,56000,2021-03-01,2021-03-31,Dose1_mar2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.00427,NA,NA,NA,NA,NA,NA,NA,NA -WY,56000,2021-04-01,2021-04-30,Dose1_apr2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004258,NA,NA,NA,NA,NA,NA,NA,NA -WY,56000,2021-05-01,2021-05-31,Dose1_may2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.0017,NA,NA,NA,NA,NA,NA,NA,NA -WY,56000,2021-06-01,2021-06-30,Dose1_jun2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.003188,NA,NA,NA,NA,NA,NA,NA,NA -WY,56000,2021-07-01,2021-07-31,Dose1_jul2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.005629,NA,NA,NA,NA,NA,NA,NA,NA -WY,56000,2021-08-01,2021-08-07,Dose1_aug2021,Reduce,transmission,vaccination,transition_rate 0,NA,fixed,0.004926,NA,NA,NA,NA,NA,NA,NA,NA -NA,all,2021-01-10,2021-01-23,variantR0adj_Week2,Reduce,transmission,variant,R0,NA,truncnorm,-0.01,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-01-24,2021-01-30,variantR0adj_Week4,Reduce,transmission,variant,R0,NA,truncnorm,-0.02000000000000001,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-01-31,2021-02-06,variantR0adj_Week5,Reduce,transmission,variant,R0,NA,truncnorm,-0.03000000000000002,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-02-07,2021-02-13,variantR0adj_Week6,Reduce,transmission,variant,R0,NA,truncnorm,-0.05000000000000004,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-02-14,2021-02-20,variantR0adj_Week7,Reduce,transmission,variant,R0,NA,truncnorm,-0.07000000000000006,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-02-21,2021-02-27,variantR0adj_Week8,Reduce,transmission,variant,R0,NA,truncnorm,-0.1100000000000001,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-02-28,2021-03-06,variantR0adj_Week9,Reduce,transmission,variant,R0,NA,truncnorm,-0.15999999999999992,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-03-07,2021-03-13,variantR0adj_Week10,Reduce,transmission,variant,R0,NA,truncnorm,-0.21999999999999997,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-03-14,2021-03-20,variantR0adj_Week11,Reduce,transmission,variant,R0,NA,truncnorm,-0.29000000000000004,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-03-21,2021-03-27,variantR0adj_Week12,Reduce,transmission,variant,R0,NA,truncnorm,-0.3500000000000001,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-03-28,2021-04-03,variantR0adj_Week13,Reduce,transmission,variant,R0,NA,truncnorm,-0.3999999999999999,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-04-04,2021-04-10,variantR0adj_Week14,Reduce,transmission,variant,R0,NA,truncnorm,-0.43999999999999995,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-04-11,2021-04-17,variantR0adj_Week15,Reduce,transmission,variant,R0,NA,truncnorm,-0.47,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-04-18,2021-04-24,variantR0adj_Week16,Reduce,transmission,variant,R0,NA,truncnorm,-0.48,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-04-25,2021-05-01,variantR0adj_Week17,Reduce,transmission,variant,R0,NA,truncnorm,-0.49,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-05-02,2021-05-29,variantR0adj_Week18,Reduce,transmission,variant,R0,NA,truncnorm,-0.5,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-05-30,2021-06-05,variantR0adj_Week22,Reduce,transmission,variant,R0,NA,truncnorm,-0.55,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-06-06,2021-06-12,variantR0adj_Week23,Reduce,transmission,variant,R0,NA,truncnorm,-0.5900000000000001,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-06-13,2021-06-19,variantR0adj_Week24,Reduce,transmission,variant,R0,NA,truncnorm,-0.6499999999999999,0.01,-1.5,0,truncnorm,0,0.01,-1,1 -NA,all,2021-06-20,2021-06-26,variantR0adj_Week25,Reduce,transmission,variant,R0,NA,truncnorm,-0.74,0.01,-1.5,0,NA,NA,NA,NA,NA -NA,all,2021-06-27,2021-07-03,variantR0adj_Week26,Reduce,transmission,variant,R0,NA,truncnorm,-0.8600000000000001,0.01,-1.5,0,NA,NA,NA,NA,NA -NA,all,2021-07-04,2021-07-10,variantR0adj_Week27,Reduce,transmission,variant,R0,NA,truncnorm,-0.99,0.01,-1.5,0,NA,NA,NA,NA,NA -NA,all,2021-07-11,2021-07-17,variantR0adj_Week28,Reduce,transmission,variant,R0,NA,truncnorm,-1.12,0.01,-1.5,0,NA,NA,NA,NA,NA -NA,all,2021-07-18,2021-07-24,variantR0adj_Week29,Reduce,transmission,variant,R0,NA,truncnorm,-1.2200000000000002,0.01,-1.5,0,NA,NA,NA,NA,NA -NA,all,2021-07-25,2021-07-31,variantR0adj_Week30,Reduce,transmission,variant,R0,NA,truncnorm,-1.2999999999999998,0.01,-1.5,0,NA,NA,NA,NA,NA -NA,all,2021-08-01,2021-08-07,variantR0adj_Week31,Reduce,transmission,variant,R0,NA,truncnorm,-1.34,0.01,-1.5,0,NA,NA,NA,NA,NA -AK,02000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15790000000000004,0.01,0,1,NA,NA,NA,NA,NA -AK,02000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.20089999999999997,0.01,0,1,NA,NA,NA,NA,NA -AK,02000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.29890000000000005,0.01,0,1,NA,NA,NA,NA,NA -AK,02000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3893,0.01,0,1,NA,NA,NA,NA,NA -AK,02000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4679,0.01,0,1,NA,NA,NA,NA,NA -AK,02000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5273,0.01,0,1,NA,NA,NA,NA,NA -AK,02000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5662,0.01,0,1,NA,NA,NA,NA,NA -AK,02000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5748,0.01,0,1,NA,NA,NA,NA,NA -AL,01000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.10529999999999996,0.01,0,1,NA,NA,NA,NA,NA -AL,01000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1663,0.01,0,1,NA,NA,NA,NA,NA -AL,01000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3105,0.01,0,1,NA,NA,NA,NA,NA -AL,01000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4433,0.01,0,1,NA,NA,NA,NA,NA -AL,01000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5273,0.01,0,1,NA,NA,NA,NA,NA -AL,01000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5645,0.01,0,1,NA,NA,NA,NA,NA -AL,01000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5707,0.01,0,1,NA,NA,NA,NA,NA -AL,01000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5483,0.01,0,1,NA,NA,NA,NA,NA -AR,05000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16400000000000003,0.01,0,1,NA,NA,NA,NA,NA -AR,05000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2169,0.01,0,1,NA,NA,NA,NA,NA -AR,05000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.33330000000000004,0.01,0,1,NA,NA,NA,NA,NA -AR,05000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4466,0.01,0,1,NA,NA,NA,NA,NA -AR,05000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5433,0.01,0,1,NA,NA,NA,NA,NA -AR,05000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6123000000000001,0.01,0,1,NA,NA,NA,NA,NA -AR,05000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6528,0.01,0,1,NA,NA,NA,NA,NA -AR,05000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6596,0.01,0,1,NA,NA,NA,NA,NA -AZ,04000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.04400000000000004,0.01,0,1,NA,NA,NA,NA,NA -AZ,04000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.0918,0.01,0,1,NA,NA,NA,NA,NA -AZ,04000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2518,0.01,0,1,NA,NA,NA,NA,NA -AZ,04000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4718,0.01,0,1,NA,NA,NA,NA,NA -AZ,04000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6359,0.01,0,1,NA,NA,NA,NA,NA -AZ,04000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7090000000000001,0.01,0,1,NA,NA,NA,NA,NA -AZ,04000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.727,0.01,0,1,NA,NA,NA,NA,NA -AZ,04000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7141,0.01,0,1,NA,NA,NA,NA,NA -CA,06000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.12860000000000005,0.01,0,1,NA,NA,NA,NA,NA -CA,06000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1903,0.01,0,1,NA,NA,NA,NA,NA -CA,06000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.34240000000000004,0.01,0,1,NA,NA,NA,NA,NA -CA,06000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5013000000000001,0.01,0,1,NA,NA,NA,NA,NA -CA,06000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6295,0.01,0,1,NA,NA,NA,NA,NA -CA,06000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7123999999999999,0.01,0,1,NA,NA,NA,NA,NA -CA,06000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7548,0.01,0,1,NA,NA,NA,NA,NA -CA,06000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7613,0.01,0,1,NA,NA,NA,NA,NA -CO,08000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.12960000000000005,0.01,0,1,NA,NA,NA,NA,NA -CO,08000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1886,0.01,0,1,NA,NA,NA,NA,NA -CO,08000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3368,0.01,0,1,NA,NA,NA,NA,NA -CO,08000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4867,0.01,0,1,NA,NA,NA,NA,NA -CO,08000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.601,0.01,0,1,NA,NA,NA,NA,NA -CO,08000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6754,0.01,0,1,NA,NA,NA,NA,NA -CO,08000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7179,0.01,0,1,NA,NA,NA,NA,NA -CO,08000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7279,0.01,0,1,NA,NA,NA,NA,NA -CT,09000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.20299999999999996,0.01,0,1,NA,NA,NA,NA,NA -CT,09000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2573,0.01,0,1,NA,NA,NA,NA,NA -CT,09000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3803,0.01,0,1,NA,NA,NA,NA,NA -CT,09000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4933999999999999,0.01,0,1,NA,NA,NA,NA,NA -CT,09000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5928,0.01,0,1,NA,NA,NA,NA,NA -CT,09000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6737,0.01,0,1,NA,NA,NA,NA,NA -CT,09000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.739,0.01,0,1,NA,NA,NA,NA,NA -CT,09000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7805,0.01,0,1,NA,NA,NA,NA,NA -DC,11000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11070000000000002,0.01,0,1,NA,NA,NA,NA,NA -DC,11000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15080000000000005,0.01,0,1,NA,NA,NA,NA,NA -DC,11000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.24419999999999997,0.01,0,1,NA,NA,NA,NA,NA -DC,11000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3477,0.01,0,1,NA,NA,NA,NA,NA -DC,11000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4547,0.01,0,1,NA,NA,NA,NA,NA -DC,11000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5264,0.01,0,1,NA,NA,NA,NA,NA -DC,11000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5603,0.01,0,1,NA,NA,NA,NA,NA -DC,11000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5760000000000001,0.01,0,1,NA,NA,NA,NA,NA -DE,10000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09919999999999995,0.01,0,1,NA,NA,NA,NA,NA -DE,10000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16510000000000002,0.01,0,1,NA,NA,NA,NA,NA -DE,10000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3367,0.01,0,1,NA,NA,NA,NA,NA -DE,10000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5131,0.01,0,1,NA,NA,NA,NA,NA -DE,10000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6295,0.01,0,1,NA,NA,NA,NA,NA -DE,10000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6813,0.01,0,1,NA,NA,NA,NA,NA -DE,10000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6925,0.01,0,1,NA,NA,NA,NA,NA -DE,10000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6748000000000001,0.01,0,1,NA,NA,NA,NA,NA -FL,12000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.10799999999999998,0.01,0,1,NA,NA,NA,NA,NA -FL,12000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16800000000000004,0.01,0,1,NA,NA,NA,NA,NA -FL,12000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.32530000000000003,0.01,0,1,NA,NA,NA,NA,NA -FL,12000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4935000000000001,0.01,0,1,NA,NA,NA,NA,NA -FL,12000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.613,0.01,0,1,NA,NA,NA,NA,NA -FL,12000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6705,0.01,0,1,NA,NA,NA,NA,NA -FL,12000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6844,0.01,0,1,NA,NA,NA,NA,NA -FL,12000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6687000000000001,0.01,0,1,NA,NA,NA,NA,NA -GA,13000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09850000000000005,0.01,0,1,NA,NA,NA,NA,NA -GA,13000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16549999999999998,0.01,0,1,NA,NA,NA,NA,NA -GA,13000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.34119999999999995,0.01,0,1,NA,NA,NA,NA,NA -GA,13000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5245,0.01,0,1,NA,NA,NA,NA,NA -GA,13000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6466000000000001,0.01,0,1,NA,NA,NA,NA,NA -GA,13000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7026,0.01,0,1,NA,NA,NA,NA,NA -GA,13000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.718,0.01,0,1,NA,NA,NA,NA,NA -GA,13000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7072,0.01,0,1,NA,NA,NA,NA,NA -GU,66000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15390000000000004,0.01,0,1,NA,NA,NA,NA,NA -GU,66000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2108,0.01,0,1,NA,NA,NA,NA,NA -GU,66000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3448,0.01,0,1,NA,NA,NA,NA,NA -GU,66000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4714,0.01,0,1,NA,NA,NA,NA,NA -GU,66000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.563,0.01,0,1,NA,NA,NA,NA,NA -GU,66000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6122000000000001,0.01,0,1,NA,NA,NA,NA,NA -GU,66000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6285000000000001,0.01,0,1,NA,NA,NA,NA,NA -GU,66000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6134,0.01,0,1,NA,NA,NA,NA,NA -HI,15000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09230000000000003,0.01,0,1,NA,NA,NA,NA,NA -HI,15000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15059999999999996,0.01,0,1,NA,NA,NA,NA,NA -HI,15000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.29910000000000003,0.01,0,1,NA,NA,NA,NA,NA -HI,15000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4653000000000001,0.01,0,1,NA,NA,NA,NA,NA -HI,15000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6356999999999999,0.01,0,1,NA,NA,NA,NA,NA -HI,15000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7482,0.01,0,1,NA,NA,NA,NA,NA -HI,15000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7744,0.01,0,1,NA,NA,NA,NA,NA -HI,15000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7635000000000001,0.01,0,1,NA,NA,NA,NA,NA -IA,19000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.089400000000000035,0.01,0,1,NA,NA,NA,NA,NA -IA,19000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.12860000000000005,0.01,0,1,NA,NA,NA,NA,NA -IA,19000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2279,0.01,0,1,NA,NA,NA,NA,NA -IA,19000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3468,0.01,0,1,NA,NA,NA,NA,NA -IA,19000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4617,0.01,0,1,NA,NA,NA,NA,NA -IA,19000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5528,0.01,0,1,NA,NA,NA,NA,NA -IA,19000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6133,0.01,0,1,NA,NA,NA,NA,NA -IA,19000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6528,0.01,0,1,NA,NA,NA,NA,NA -ID,16000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11939999999999996,0.01,0,1,NA,NA,NA,NA,NA -ID,16000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16679999999999995,0.01,0,1,NA,NA,NA,NA,NA -ID,16000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2823,0.01,0,1,NA,NA,NA,NA,NA -ID,16000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4031,0.01,0,1,NA,NA,NA,NA,NA -ID,16000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.505,0.01,0,1,NA,NA,NA,NA,NA -ID,16000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.573,0.01,0,1,NA,NA,NA,NA,NA -ID,16000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6081,0.01,0,1,NA,NA,NA,NA,NA -ID,16000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6069,0.01,0,1,NA,NA,NA,NA,NA -IL,17000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09240000000000004,0.01,0,1,NA,NA,NA,NA,NA -IL,17000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13429999999999995,0.01,0,1,NA,NA,NA,NA,NA -IL,17000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.24170000000000005,0.01,0,1,NA,NA,NA,NA,NA -IL,17000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.365,0.01,0,1,NA,NA,NA,NA,NA -IL,17000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.47629999999999995,0.01,0,1,NA,NA,NA,NA,NA -IL,17000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5567,0.01,0,1,NA,NA,NA,NA,NA -IL,17000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6061000000000001,0.01,0,1,NA,NA,NA,NA,NA -IL,17000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.619,0.01,0,1,NA,NA,NA,NA,NA -IN,18000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09919999999999995,0.01,0,1,NA,NA,NA,NA,NA -IN,18000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1612,0.01,0,1,NA,NA,NA,NA,NA -IN,18000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.31899999999999995,0.01,0,1,NA,NA,NA,NA,NA -IN,18000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4754,0.01,0,1,NA,NA,NA,NA,NA -IN,18000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5694,0.01,0,1,NA,NA,NA,NA,NA -IN,18000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5992,0.01,0,1,NA,NA,NA,NA,NA -IN,18000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5912,0.01,0,1,NA,NA,NA,NA,NA -IN,18000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5566,0.01,0,1,NA,NA,NA,NA,NA -KS,20000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1211,0.01,0,1,NA,NA,NA,NA,NA -KS,20000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16549999999999998,0.01,0,1,NA,NA,NA,NA,NA -KS,20000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2751,0.01,0,1,NA,NA,NA,NA,NA -KS,20000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3922,0.01,0,1,NA,NA,NA,NA,NA -KS,20000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.495,0.01,0,1,NA,NA,NA,NA,NA -KS,20000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5687,0.01,0,1,NA,NA,NA,NA,NA -KS,20000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6135999999999999,0.01,0,1,NA,NA,NA,NA,NA -KS,20000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6252,0.01,0,1,NA,NA,NA,NA,NA -KY,21000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.0917,0.01,0,1,NA,NA,NA,NA,NA -KY,21000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.14839999999999998,0.01,0,1,NA,NA,NA,NA,NA -KY,21000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2891,0.01,0,1,NA,NA,NA,NA,NA -KY,21000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4278,0.01,0,1,NA,NA,NA,NA,NA -KY,21000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5147999999999999,0.01,0,1,NA,NA,NA,NA,NA -KY,21000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.546,0.01,0,1,NA,NA,NA,NA,NA -KY,21000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5446,0.01,0,1,NA,NA,NA,NA,NA -KY,21000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5175000000000001,0.01,0,1,NA,NA,NA,NA,NA -LA,22000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.10840000000000004,0.01,0,1,NA,NA,NA,NA,NA -LA,22000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1703,0.01,0,1,NA,NA,NA,NA,NA -LA,22000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3206,0.01,0,1,NA,NA,NA,NA,NA -LA,22000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4588,0.01,0,1,NA,NA,NA,NA,NA -LA,22000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5387,0.01,0,1,NA,NA,NA,NA,NA -LA,22000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5668,0.01,0,1,NA,NA,NA,NA,NA -LA,22000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5665,0.01,0,1,NA,NA,NA,NA,NA -LA,22000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5450999999999999,0.01,0,1,NA,NA,NA,NA,NA -MA,25000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.10440000000000003,0.01,0,1,NA,NA,NA,NA,NA -MA,25000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15949999999999998,0.01,0,1,NA,NA,NA,NA,NA -MA,25000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.30589999999999995,0.01,0,1,NA,NA,NA,NA,NA -MA,25000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4787,0.01,0,1,NA,NA,NA,NA,NA -MA,25000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6269,0.01,0,1,NA,NA,NA,NA,NA -MA,25000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7229,0.01,0,1,NA,NA,NA,NA,NA -MA,25000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7734,0.01,0,1,NA,NA,NA,NA,NA -MA,25000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7922,0.01,0,1,NA,NA,NA,NA,NA -MD,24000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09760000000000002,0.01,0,1,NA,NA,NA,NA,NA -MD,24000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13939999999999997,0.01,0,1,NA,NA,NA,NA,NA -MD,24000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.25039999999999996,0.01,0,1,NA,NA,NA,NA,NA -MD,24000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3842,0.01,0,1,NA,NA,NA,NA,NA -MD,24000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5105999999999999,0.01,0,1,NA,NA,NA,NA,NA -MD,24000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6079,0.01,0,1,NA,NA,NA,NA,NA -MD,24000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6719999999999999,0.01,0,1,NA,NA,NA,NA,NA -MD,24000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6997,0.01,0,1,NA,NA,NA,NA,NA -ME,23000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11950000000000004,0.01,0,1,NA,NA,NA,NA,NA -ME,23000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.17290000000000005,0.01,0,1,NA,NA,NA,NA,NA -ME,23000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3183,0.01,0,1,NA,NA,NA,NA,NA -ME,23000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4582000000000001,0.01,0,1,NA,NA,NA,NA,NA -ME,23000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5484,0.01,0,1,NA,NA,NA,NA,NA -ME,23000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6,0.01,0,1,NA,NA,NA,NA,NA -ME,23000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6276999999999999,0.01,0,1,NA,NA,NA,NA,NA -ME,23000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6306,0.01,0,1,NA,NA,NA,NA,NA -MI,26000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1231,0.01,0,1,NA,NA,NA,NA,NA -MI,26000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16700000000000004,0.01,0,1,NA,NA,NA,NA,NA -MI,26000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2724,0.01,0,1,NA,NA,NA,NA,NA -MI,26000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3822,0.01,0,1,NA,NA,NA,NA,NA -MI,26000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4778,0.01,0,1,NA,NA,NA,NA,NA -MI,26000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5438000000000001,0.01,0,1,NA,NA,NA,NA,NA -MI,26000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5784,0.01,0,1,NA,NA,NA,NA,NA -MI,26000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.577,0.01,0,1,NA,NA,NA,NA,NA -MN,27000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.08919999999999995,0.01,0,1,NA,NA,NA,NA,NA -MN,27000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13439999999999996,0.01,0,1,NA,NA,NA,NA,NA -MN,27000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2551,0.01,0,1,NA,NA,NA,NA,NA -MN,27000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4036,0.01,0,1,NA,NA,NA,NA,NA -MN,27000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5453,0.01,0,1,NA,NA,NA,NA,NA -MN,27000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6523,0.01,0,1,NA,NA,NA,NA,NA -MN,27000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7185,0.01,0,1,NA,NA,NA,NA,NA -MN,27000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7425999999999999,0.01,0,1,NA,NA,NA,NA,NA -MO,29000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.06320000000000003,0.01,0,1,NA,NA,NA,NA,NA -MO,29000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.10609999999999996,0.01,0,1,NA,NA,NA,NA,NA -MO,29000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.23429999999999995,0.01,0,1,NA,NA,NA,NA,NA -MO,29000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3934,0.01,0,1,NA,NA,NA,NA,NA -MO,29000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.509,0.01,0,1,NA,NA,NA,NA,NA -MO,29000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5606,0.01,0,1,NA,NA,NA,NA,NA -MO,29000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5703,0.01,0,1,NA,NA,NA,NA,NA -MO,29000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5483,0.01,0,1,NA,NA,NA,NA,NA -MP,69000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15380000000000005,0.01,0,1,NA,NA,NA,NA,NA -MP,69000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2107,0.01,0,1,NA,NA,NA,NA,NA -MP,69000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3449,0.01,0,1,NA,NA,NA,NA,NA -MP,69000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4714,0.01,0,1,NA,NA,NA,NA,NA -MP,69000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5630999999999999,0.01,0,1,NA,NA,NA,NA,NA -MP,69000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6122000000000001,0.01,0,1,NA,NA,NA,NA,NA -MP,69000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6286,0.01,0,1,NA,NA,NA,NA,NA -MP,69000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6134,0.01,0,1,NA,NA,NA,NA,NA -MS,28000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.08399999999999996,0.01,0,1,NA,NA,NA,NA,NA -MS,28000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13639999999999997,0.01,0,1,NA,NA,NA,NA,NA -MS,28000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.26849999999999996,0.01,0,1,NA,NA,NA,NA,NA -MS,28000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3996,0.01,0,1,NA,NA,NA,NA,NA -MS,28000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4829,0.01,0,1,NA,NA,NA,NA,NA -MS,28000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5163,0.01,0,1,NA,NA,NA,NA,NA -MS,28000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5183,0.01,0,1,NA,NA,NA,NA,NA -MS,28000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4919,0.01,0,1,NA,NA,NA,NA,NA -MT,30000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1382,0.01,0,1,NA,NA,NA,NA,NA -MT,30000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.19599999999999995,0.01,0,1,NA,NA,NA,NA,NA -MT,30000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.32789999999999997,0.01,0,1,NA,NA,NA,NA,NA -MT,30000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4583,0.01,0,1,NA,NA,NA,NA,NA -MT,30000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5552,0.01,0,1,NA,NA,NA,NA,NA -MT,30000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6073999999999999,0.01,0,1,NA,NA,NA,NA,NA -MT,30000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6241,0.01,0,1,NA,NA,NA,NA,NA -MT,30000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6087,0.01,0,1,NA,NA,NA,NA,NA -NC,37000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13890000000000002,0.01,0,1,NA,NA,NA,NA,NA -NC,37000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.17490000000000006,0.01,0,1,NA,NA,NA,NA,NA -NC,37000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.25849999999999995,0.01,0,1,NA,NA,NA,NA,NA -NC,37000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3415,0.01,0,1,NA,NA,NA,NA,NA -NC,37000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4227,0.01,0,1,NA,NA,NA,NA,NA -NC,37000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4932,0.01,0,1,NA,NA,NA,NA,NA -NC,37000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5472,0.01,0,1,NA,NA,NA,NA,NA -NC,37000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5731999999999999,0.01,0,1,NA,NA,NA,NA,NA -ND,38000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16300000000000003,0.01,0,1,NA,NA,NA,NA,NA -ND,38000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2208,0.01,0,1,NA,NA,NA,NA,NA -ND,38000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.35440000000000005,0.01,0,1,NA,NA,NA,NA,NA -ND,38000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.479,0.01,0,1,NA,NA,NA,NA,NA -ND,38000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5704,0.01,0,1,NA,NA,NA,NA,NA -ND,38000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6211,0.01,0,1,NA,NA,NA,NA,NA -ND,38000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6395,0.01,0,1,NA,NA,NA,NA,NA -ND,38000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6273,0.01,0,1,NA,NA,NA,NA,NA -NE,31000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11050000000000004,0.01,0,1,NA,NA,NA,NA,NA -NE,31000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.17159999999999995,0.01,0,1,NA,NA,NA,NA,NA -NE,31000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3278,0.01,0,1,NA,NA,NA,NA,NA -NE,31000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4867,0.01,0,1,NA,NA,NA,NA,NA -NE,31000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5892999999999999,0.01,0,1,NA,NA,NA,NA,NA -NE,31000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6302,0.01,0,1,NA,NA,NA,NA,NA -NE,31000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6328,0.01,0,1,NA,NA,NA,NA,NA -NE,31000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6068,0.01,0,1,NA,NA,NA,NA,NA -NH,33000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13649999999999995,0.01,0,1,NA,NA,NA,NA,NA -NH,33000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.19330000000000003,0.01,0,1,NA,NA,NA,NA,NA -NH,33000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3226,0.01,0,1,NA,NA,NA,NA,NA -NH,33000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4496,0.01,0,1,NA,NA,NA,NA,NA -NH,33000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5432,0.01,0,1,NA,NA,NA,NA,NA -NH,33000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5928,0.01,0,1,NA,NA,NA,NA,NA -NH,33000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6071,0.01,0,1,NA,NA,NA,NA,NA -NH,33000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5892,0.01,0,1,NA,NA,NA,NA,NA -NJ,34000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.20120000000000005,0.01,0,1,NA,NA,NA,NA,NA -NJ,34000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2228,0.01,0,1,NA,NA,NA,NA,NA -NJ,34000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3962,0.01,0,1,NA,NA,NA,NA,NA -NJ,34000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7029000000000001,0.01,0,1,NA,NA,NA,NA,NA -NJ,34000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8492999999999999,0.01,0,1,NA,NA,NA,NA,NA -NJ,34000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8857,0.01,0,1,NA,NA,NA,NA,NA -NJ,34000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8929,0.01,0,1,NA,NA,NA,NA,NA -NJ,34000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8858,0.01,0,1,NA,NA,NA,NA,NA -NM,35000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15800000000000003,0.01,0,1,NA,NA,NA,NA,NA -NM,35000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1926,0.01,0,1,NA,NA,NA,NA,NA -NM,35000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.272,0.01,0,1,NA,NA,NA,NA,NA -NM,35000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3476,0.01,0,1,NA,NA,NA,NA,NA -NM,35000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4248,0.01,0,1,NA,NA,NA,NA,NA -NM,35000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5001,0.01,0,1,NA,NA,NA,NA,NA -NM,35000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5687,0.01,0,1,NA,NA,NA,NA,NA -NM,35000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6122000000000001,0.01,0,1,NA,NA,NA,NA,NA -NV,32000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.12990000000000002,0.01,0,1,NA,NA,NA,NA,NA -NV,32000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.18010000000000004,0.01,0,1,NA,NA,NA,NA,NA -NV,32000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.29779999999999995,0.01,0,1,NA,NA,NA,NA,NA -NV,32000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4125,0.01,0,1,NA,NA,NA,NA,NA -NV,32000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5044,0.01,0,1,NA,NA,NA,NA,NA -NV,32000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5623,0.01,0,1,NA,NA,NA,NA,NA -NV,32000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.589,0.01,0,1,NA,NA,NA,NA,NA -NV,32000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5823,0.01,0,1,NA,NA,NA,NA,NA -NY,36000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.04279999999999995,0.01,0,1,NA,NA,NA,NA,NA -NY,36000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.06610000000000005,0.01,0,1,NA,NA,NA,NA,NA -NY,36000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1351,0.01,0,1,NA,NA,NA,NA,NA -NY,36000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.24119999999999997,0.01,0,1,NA,NA,NA,NA,NA -NY,36000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3751,0.01,0,1,NA,NA,NA,NA,NA -NY,36000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5085999999999999,0.01,0,1,NA,NA,NA,NA,NA -NY,36000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6121,0.01,0,1,NA,NA,NA,NA,NA -NY,36000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6639999999999999,0.01,0,1,NA,NA,NA,NA,NA -OH,39000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.08620000000000005,0.01,0,1,NA,NA,NA,NA,NA -OH,39000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.14470000000000005,0.01,0,1,NA,NA,NA,NA,NA -OH,39000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.29869999999999997,0.01,0,1,NA,NA,NA,NA,NA -OH,39000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4586,0.01,0,1,NA,NA,NA,NA,NA -OH,39000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5590999999999999,0.01,0,1,NA,NA,NA,NA,NA -OH,39000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5957,0.01,0,1,NA,NA,NA,NA,NA -OH,39000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5951,0.01,0,1,NA,NA,NA,NA,NA -OH,39000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5671999999999999,0.01,0,1,NA,NA,NA,NA,NA -OK,40000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1402,0.01,0,1,NA,NA,NA,NA,NA -OK,40000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.20230000000000004,0.01,0,1,NA,NA,NA,NA,NA -OK,40000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3415,0.01,0,1,NA,NA,NA,NA,NA -OK,40000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4738,0.01,0,1,NA,NA,NA,NA,NA -OK,40000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5677,0.01,0,1,NA,NA,NA,NA,NA -OK,40000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6189,0.01,0,1,NA,NA,NA,NA,NA -OK,40000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6404000000000001,0.01,0,1,NA,NA,NA,NA,NA -OK,40000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6377999999999999,0.01,0,1,NA,NA,NA,NA,NA -OR,41000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.07420000000000004,0.01,0,1,NA,NA,NA,NA,NA -OR,41000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1239,0.01,0,1,NA,NA,NA,NA,NA -OR,41000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.264,0.01,0,1,NA,NA,NA,NA,NA -OR,41000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4300000000000001,0.01,0,1,NA,NA,NA,NA,NA -OR,41000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5528,0.01,0,1,NA,NA,NA,NA,NA -OR,41000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6066,0.01,0,1,NA,NA,NA,NA,NA -OR,41000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.614,0.01,0,1,NA,NA,NA,NA,NA -OR,41000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5958,0.01,0,1,NA,NA,NA,NA,NA -PA,42000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.0504,0.01,0,1,NA,NA,NA,NA,NA -PA,42000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09030000000000005,0.01,0,1,NA,NA,NA,NA,NA -PA,42000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.22009999999999996,0.01,0,1,NA,NA,NA,NA,NA -PA,42000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4051,0.01,0,1,NA,NA,NA,NA,NA -PA,42000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5576,0.01,0,1,NA,NA,NA,NA,NA -PA,42000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6325000000000001,0.01,0,1,NA,NA,NA,NA,NA -PA,42000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6507000000000001,0.01,0,1,NA,NA,NA,NA,NA -PA,42000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6334,0.01,0,1,NA,NA,NA,NA,NA -PR,72000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0,0.01,0,1,NA,NA,NA,NA,NA -PR,72000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0,0.01,0,1,NA,NA,NA,NA,NA -PR,72000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0,0.01,0,1,NA,NA,NA,NA,NA -PR,72000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1734,0.01,0,1,NA,NA,NA,NA,NA -PR,72000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.24370000000000003,0.01,0,1,NA,NA,NA,NA,NA -PR,72000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3246,0.01,0,1,NA,NA,NA,NA,NA -PR,72000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.393,0.01,0,1,NA,NA,NA,NA,NA -PR,72000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4353,0.01,0,1,NA,NA,NA,NA,NA -RI,44000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09350000000000004,0.01,0,1,NA,NA,NA,NA,NA -RI,44000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.14149999999999996,0.01,0,1,NA,NA,NA,NA,NA -RI,44000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.27270000000000005,0.01,0,1,NA,NA,NA,NA,NA -RI,44000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4267,0.01,0,1,NA,NA,NA,NA,NA -RI,44000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5452,0.01,0,1,NA,NA,NA,NA,NA -RI,44000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6038,0.01,0,1,NA,NA,NA,NA,NA -RI,44000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6202,0.01,0,1,NA,NA,NA,NA,NA -RI,44000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6084,0.01,0,1,NA,NA,NA,NA,NA -SC,45000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1007,0.01,0,1,NA,NA,NA,NA,NA -SC,45000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1643,0.01,0,1,NA,NA,NA,NA,NA -SC,45000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3248,0.01,0,1,NA,NA,NA,NA,NA -SC,45000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4812,0.01,0,1,NA,NA,NA,NA,NA -SC,45000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5772999999999999,0.01,0,1,NA,NA,NA,NA,NA -SC,45000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6152,0.01,0,1,NA,NA,NA,NA,NA -SC,45000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6187,0.01,0,1,NA,NA,NA,NA,NA -SC,45000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.596,0.01,0,1,NA,NA,NA,NA,NA -SD,46000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13839999999999997,0.01,0,1,NA,NA,NA,NA,NA -SD,46000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.18889999999999996,0.01,0,1,NA,NA,NA,NA,NA -SD,46000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.30700000000000005,0.01,0,1,NA,NA,NA,NA,NA -SD,46000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4234,0.01,0,1,NA,NA,NA,NA,NA -SD,46000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5195000000000001,0.01,0,1,NA,NA,NA,NA,NA -SD,46000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5808,0.01,0,1,NA,NA,NA,NA,NA -SD,46000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6069,0.01,0,1,NA,NA,NA,NA,NA -SD,46000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5998,0.01,0,1,NA,NA,NA,NA,NA -TN,47000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.07989999999999997,0.01,0,1,NA,NA,NA,NA,NA -TN,47000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.136,0.01,0,1,NA,NA,NA,NA,NA -TN,47000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2845,0.01,0,1,NA,NA,NA,NA,NA -TN,47000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4399,0.01,0,1,NA,NA,NA,NA,NA -TN,47000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5391,0.01,0,1,NA,NA,NA,NA,NA -TN,47000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5763,0.01,0,1,NA,NA,NA,NA,NA -TN,47000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5761000000000001,0.01,0,1,NA,NA,NA,NA,NA -TN,47000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5488999999999999,0.01,0,1,NA,NA,NA,NA,NA -TX,48000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.07069999999999999,0.01,0,1,NA,NA,NA,NA,NA -TX,48000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11929999999999996,0.01,0,1,NA,NA,NA,NA,NA -TX,48000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2563,0.01,0,1,NA,NA,NA,NA,NA -TX,48000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4184,0.01,0,1,NA,NA,NA,NA,NA -TX,48000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5373,0.01,0,1,NA,NA,NA,NA,NA -TX,48000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5903,0.01,0,1,NA,NA,NA,NA,NA -TX,48000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5983,0.01,0,1,NA,NA,NA,NA,NA -TX,48000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5737,0.01,0,1,NA,NA,NA,NA,NA -UT,49000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1603,0.01,0,1,NA,NA,NA,NA,NA -UT,49000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.23409999999999995,0.01,0,1,NA,NA,NA,NA,NA -UT,49000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4054,0.01,0,1,NA,NA,NA,NA,NA -UT,49000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5633,0.01,0,1,NA,NA,NA,NA,NA -UT,49000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6803,0.01,0,1,NA,NA,NA,NA,NA -UT,49000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7534,0.01,0,1,NA,NA,NA,NA,NA -UT,49000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7885,0.01,0,1,NA,NA,NA,NA,NA -UT,49000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7911,0.01,0,1,NA,NA,NA,NA,NA -VA,51000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.12139999999999997,0.01,0,1,NA,NA,NA,NA,NA -VA,51000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16169999999999995,0.01,0,1,NA,NA,NA,NA,NA -VA,51000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.26070000000000004,0.01,0,1,NA,NA,NA,NA,NA -VA,51000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3712,0.01,0,1,NA,NA,NA,NA,NA -VA,51000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.482,0.01,0,1,NA,NA,NA,NA,NA -VA,51000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5764,0.01,0,1,NA,NA,NA,NA,NA -VA,51000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6455,0.01,0,1,NA,NA,NA,NA,NA -VA,51000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6806,0.01,0,1,NA,NA,NA,NA,NA -VI,78000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15380000000000005,0.01,0,1,NA,NA,NA,NA,NA -VI,78000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2108,0.01,0,1,NA,NA,NA,NA,NA -VI,78000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3447,0.01,0,1,NA,NA,NA,NA,NA -VI,78000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4716,0.01,0,1,NA,NA,NA,NA,NA -VI,78000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5629,0.01,0,1,NA,NA,NA,NA,NA -VI,78000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6121,0.01,0,1,NA,NA,NA,NA,NA -VI,78000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6285000000000001,0.01,0,1,NA,NA,NA,NA,NA -VI,78000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6135999999999999,0.01,0,1,NA,NA,NA,NA,NA -VT,50000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1623,0.01,0,1,NA,NA,NA,NA,NA -VT,50000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.235,0.01,0,1,NA,NA,NA,NA,NA -VT,50000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4272,0.01,0,1,NA,NA,NA,NA,NA -VT,50000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6154,0.01,0,1,NA,NA,NA,NA,NA -VT,50000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7498,0.01,0,1,NA,NA,NA,NA,NA -VT,50000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8316,0.01,0,1,NA,NA,NA,NA,NA -VT,50000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8601,0.01,0,1,NA,NA,NA,NA,NA -VT,50000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8618,0.01,0,1,NA,NA,NA,NA,NA -WA,53000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11240000000000006,0.01,0,1,NA,NA,NA,NA,NA -WA,53000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.17110000000000003,0.01,0,1,NA,NA,NA,NA,NA -WA,53000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3206,0.01,0,1,NA,NA,NA,NA,NA -WA,53000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4817,0.01,0,1,NA,NA,NA,NA,NA -WA,53000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6066,0.01,0,1,NA,NA,NA,NA,NA -WA,53000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6784,0.01,0,1,NA,NA,NA,NA,NA -WA,53000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7088,0.01,0,1,NA,NA,NA,NA,NA -WA,53000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7070000000000001,0.01,0,1,NA,NA,NA,NA,NA -WI,55000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.07730000000000004,0.01,0,1,NA,NA,NA,NA,NA -WI,55000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.133,0.01,0,1,NA,NA,NA,NA,NA -WI,55000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.28759999999999997,0.01,0,1,NA,NA,NA,NA,NA -WI,55000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.46419999999999995,0.01,0,1,NA,NA,NA,NA,NA -WI,55000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5921000000000001,0.01,0,1,NA,NA,NA,NA,NA -WI,55000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6541,0.01,0,1,NA,NA,NA,NA,NA -WI,55000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6707000000000001,0.01,0,1,NA,NA,NA,NA,NA -WI,55000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6529,0.01,0,1,NA,NA,NA,NA,NA -WV,54000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.18610000000000004,0.01,0,1,NA,NA,NA,NA,NA -WV,54000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.21809999999999996,0.01,0,1,NA,NA,NA,NA,NA -WV,54000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.28690000000000004,0.01,0,1,NA,NA,NA,NA,NA -WV,54000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3379,0.01,0,1,NA,NA,NA,NA,NA -WV,54000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3786000000000001,0.01,0,1,NA,NA,NA,NA,NA -WV,54000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4086,0.01,0,1,NA,NA,NA,NA,NA -WV,54000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4332,0.01,0,1,NA,NA,NA,NA,NA -WV,54000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4416,0.01,0,1,NA,NA,NA,NA,NA -WY,56000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.00860000000000005,0.01,0,1,NA,NA,NA,NA,NA -WY,56000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.01429999999999998,0.01,0,1,NA,NA,NA,NA,NA -WY,56000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.03420000000000001,0.01,0,1,NA,NA,NA,NA,NA -WY,56000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.07509999999999994,0.01,0,1,NA,NA,NA,NA,NA -WY,56000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.14790000000000003,0.01,0,1,NA,NA,NA,NA,NA -WY,56000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.25229999999999997,0.01,0,1,NA,NA,NA,NA,NA -WY,56000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3671,0.01,0,1,NA,NA,NA,NA,NA -WY,56000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,Reduce,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4439999999999999,0.01,0,1,NA,NA,NA,NA,NA +USPS,subpop,start_date,end_date,name,template,type,category,parameter,baseline_scenario,value_dist,value_mean,value_sd,value_a,value_b,pert_dist,pert_mean,pert_sd,pert_a,pert_b +AL,01000,2020-04-04,2020-04-30,lockdown,MultiPeriodModifier,transmission,NPI,R0,NA,truncnorm,0.6,0.05,0,0.9,truncnorm,0,0.05,-1,1 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+AR,05000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004355,NA,NA,NA,NA,NA,NA,NA,NA +AZ,04000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,9.1e-4,NA,NA,NA,NA,NA,NA,NA,NA +AZ,04000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003637,NA,NA,NA,NA,NA,NA,NA,NA +AZ,04000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004542,NA,NA,NA,NA,NA,NA,NA,NA +AZ,04000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006755,NA,NA,NA,NA,NA,NA,NA,NA +AZ,04000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004126,NA,NA,NA,NA,NA,NA,NA,NA +AZ,04000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003358,NA,NA,NA,NA,NA,NA,NA,NA +AZ,04000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003208,NA,NA,NA,NA,NA,NA,NA,NA +AZ,04000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003691,NA,NA,NA,NA,NA,NA,NA,NA +CA,06000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004032,NA,NA,NA,NA,NA,NA,NA,NA +CA,06000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004414,NA,NA,NA,NA,NA,NA,NA,NA +CA,06000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.009529,NA,NA,NA,NA,NA,NA,NA,NA +CA,06000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007473,NA,NA,NA,NA,NA,NA,NA,NA +CA,06000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005734,NA,NA,NA,NA,NA,NA,NA,NA +CA,06000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005427,NA,NA,NA,NA,NA,NA,NA,NA +CA,06000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005324,NA,NA,NA,NA,NA,NA,NA,NA +CO,08000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001223,NA,NA,NA,NA,NA,NA,NA,NA +CO,08000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00289,NA,NA,NA,NA,NA,NA,NA,NA +CO,08000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00442,NA,NA,NA,NA,NA,NA,NA,NA +CO,08000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.009366,NA,NA,NA,NA,NA,NA,NA,NA +CO,08000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006245,NA,NA,NA,NA,NA,NA,NA,NA +CO,08000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005531,NA,NA,NA,NA,NA,NA,NA,NA +CO,08000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005302,NA,NA,NA,NA,NA,NA,NA,NA +CO,08000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005107,NA,NA,NA,NA,NA,NA,NA,NA +CT,09000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001444,NA,NA,NA,NA,NA,NA,NA,NA +CT,09000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003284,NA,NA,NA,NA,NA,NA,NA,NA +CT,09000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006127,NA,NA,NA,NA,NA,NA,NA,NA +CT,09000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.010163,NA,NA,NA,NA,NA,NA,NA,NA +CT,09000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008513,NA,NA,NA,NA,NA,NA,NA,NA +CT,09000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007132,NA,NA,NA,NA,NA,NA,NA,NA +CT,09000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007648,NA,NA,NA,NA,NA,NA,NA,NA +CT,09000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.0073,NA,NA,NA,NA,NA,NA,NA,NA +DC,11000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004432,NA,NA,NA,NA,NA,NA,NA,NA +DC,11000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002789,NA,NA,NA,NA,NA,NA,NA,NA +DC,11000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.009738,NA,NA,NA,NA,NA,NA,NA,NA +DC,11000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.009489,NA,NA,NA,NA,NA,NA,NA,NA +DC,11000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005403,NA,NA,NA,NA,NA,NA,NA,NA +DC,11000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005846,NA,NA,NA,NA,NA,NA,NA,NA +DC,11000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007962,NA,NA,NA,NA,NA,NA,NA,NA +DE,10000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,4.24e-4,NA,NA,NA,NA,NA,NA,NA,NA +DE,10000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003744,NA,NA,NA,NA,NA,NA,NA,NA +DE,10000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004357,NA,NA,NA,NA,NA,NA,NA,NA +DE,10000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.009041,NA,NA,NA,NA,NA,NA,NA,NA +DE,10000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006471,NA,NA,NA,NA,NA,NA,NA,NA +DE,10000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005204,NA,NA,NA,NA,NA,NA,NA,NA +DE,10000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004372,NA,NA,NA,NA,NA,NA,NA,NA +DE,10000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004788,NA,NA,NA,NA,NA,NA,NA,NA +FL,12000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001333,NA,NA,NA,NA,NA,NA,NA,NA +FL,12000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002584,NA,NA,NA,NA,NA,NA,NA,NA +FL,12000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004256,NA,NA,NA,NA,NA,NA,NA,NA +FL,12000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007515,NA,NA,NA,NA,NA,NA,NA,NA +FL,12000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005339,NA,NA,NA,NA,NA,NA,NA,NA +FL,12000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004656,NA,NA,NA,NA,NA,NA,NA,NA +FL,12000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004632,NA,NA,NA,NA,NA,NA,NA,NA +FL,12000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004264,NA,NA,NA,NA,NA,NA,NA,NA +GA,13000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,2.95e-4,NA,NA,NA,NA,NA,NA,NA,NA +GA,13000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003166,NA,NA,NA,NA,NA,NA,NA,NA +GA,13000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002689,NA,NA,NA,NA,NA,NA,NA,NA +GA,13000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006914,NA,NA,NA,NA,NA,NA,NA,NA +GA,13000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003024,NA,NA,NA,NA,NA,NA,NA,NA +GA,13000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002945,NA,NA,NA,NA,NA,NA,NA,NA +GA,13000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002869,NA,NA,NA,NA,NA,NA,NA,NA +GA,13000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003331,NA,NA,NA,NA,NA,NA,NA,NA +GU,66000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001893,NA,NA,NA,NA,NA,NA,NA,NA +GU,66000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004754,NA,NA,NA,NA,NA,NA,NA,NA +GU,66000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002632,NA,NA,NA,NA,NA,NA,NA,NA +GU,66000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.009422,NA,NA,NA,NA,NA,NA,NA,NA +HI,15000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,2.05e-4,NA,NA,NA,NA,NA,NA,NA,NA +HI,15000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003911,NA,NA,NA,NA,NA,NA,NA,NA +HI,15000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005352,NA,NA,NA,NA,NA,NA,NA,NA +HI,15000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006736,NA,NA,NA,NA,NA,NA,NA,NA +HI,15000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.015824,NA,NA,NA,NA,NA,NA,NA,NA +HI,15000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007606,NA,NA,NA,NA,NA,NA,NA,NA +HI,15000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005033,NA,NA,NA,NA,NA,NA,NA,NA +HI,15000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005334,NA,NA,NA,NA,NA,NA,NA,NA +IA,19000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001032,NA,NA,NA,NA,NA,NA,NA,NA +IA,19000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002585,NA,NA,NA,NA,NA,NA,NA,NA +IA,19000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005662,NA,NA,NA,NA,NA,NA,NA,NA +IA,19000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007657,NA,NA,NA,NA,NA,NA,NA,NA +IA,19000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003995,NA,NA,NA,NA,NA,NA,NA,NA +IA,19000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003701,NA,NA,NA,NA,NA,NA,NA,NA +IA,19000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,5.72e-4,NA,NA,NA,NA,NA,NA,NA,NA +IA,19000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.009231,NA,NA,NA,NA,NA,NA,NA,NA +ID,16000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,7.05e-4,NA,NA,NA,NA,NA,NA,NA,NA +ID,16000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002855,NA,NA,NA,NA,NA,NA,NA,NA +ID,16000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004191,NA,NA,NA,NA,NA,NA,NA,NA +ID,16000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005559,NA,NA,NA,NA,NA,NA,NA,NA +ID,16000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002316,NA,NA,NA,NA,NA,NA,NA,NA +ID,16000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002436,NA,NA,NA,NA,NA,NA,NA,NA +ID,16000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003961,NA,NA,NA,NA,NA,NA,NA,NA +ID,16000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004795,NA,NA,NA,NA,NA,NA,NA,NA +IL,17000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,6.8e-4,NA,NA,NA,NA,NA,NA,NA,NA +IL,17000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003162,NA,NA,NA,NA,NA,NA,NA,NA +IL,17000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004809,NA,NA,NA,NA,NA,NA,NA,NA +IL,17000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008428,NA,NA,NA,NA,NA,NA,NA,NA +IL,17000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006174,NA,NA,NA,NA,NA,NA,NA,NA +IL,17000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005687,NA,NA,NA,NA,NA,NA,NA,NA +IL,17000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00567,NA,NA,NA,NA,NA,NA,NA,NA +IL,17000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005401,NA,NA,NA,NA,NA,NA,NA,NA +IN,18000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001109,NA,NA,NA,NA,NA,NA,NA,NA +IN,18000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003137,NA,NA,NA,NA,NA,NA,NA,NA +IN,18000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003365,NA,NA,NA,NA,NA,NA,NA,NA +IN,18000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005571,NA,NA,NA,NA,NA,NA,NA,NA +IN,18000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003615,NA,NA,NA,NA,NA,NA,NA,NA +IN,18000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003093,NA,NA,NA,NA,NA,NA,NA,NA +IN,18000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003615,NA,NA,NA,NA,NA,NA,NA,NA +IN,18000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003721,NA,NA,NA,NA,NA,NA,NA,NA +KS,20000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,7.55e-4,NA,NA,NA,NA,NA,NA,NA,NA +KS,20000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002627,NA,NA,NA,NA,NA,NA,NA,NA +KS,20000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005016,NA,NA,NA,NA,NA,NA,NA,NA +KS,20000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00819,NA,NA,NA,NA,NA,NA,NA,NA +KS,20000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003088,NA,NA,NA,NA,NA,NA,NA,NA +KS,20000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003067,NA,NA,NA,NA,NA,NA,NA,NA +KS,20000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004307,NA,NA,NA,NA,NA,NA,NA,NA +KS,20000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005069,NA,NA,NA,NA,NA,NA,NA,NA +KY,21000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,4.42e-4,NA,NA,NA,NA,NA,NA,NA,NA +KY,21000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003479,NA,NA,NA,NA,NA,NA,NA,NA +KY,21000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005304,NA,NA,NA,NA,NA,NA,NA,NA +KY,21000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006686,NA,NA,NA,NA,NA,NA,NA,NA +KY,21000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003199,NA,NA,NA,NA,NA,NA,NA,NA +KY,21000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003151,NA,NA,NA,NA,NA,NA,NA,NA +KY,21000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002638,NA,NA,NA,NA,NA,NA,NA,NA +KY,21000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003136,NA,NA,NA,NA,NA,NA,NA,NA +LA,22000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001033,NA,NA,NA,NA,NA,NA,NA,NA +LA,22000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002887,NA,NA,NA,NA,NA,NA,NA,NA +LA,22000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003833,NA,NA,NA,NA,NA,NA,NA,NA +LA,22000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004371,NA,NA,NA,NA,NA,NA,NA,NA +LA,22000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001721,NA,NA,NA,NA,NA,NA,NA,NA +LA,22000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.0018,NA,NA,NA,NA,NA,NA,NA,NA +LA,22000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001898,NA,NA,NA,NA,NA,NA,NA,NA +LA,22000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.0022,NA,NA,NA,NA,NA,NA,NA,NA +MA,25000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,7.7e-4,NA,NA,NA,NA,NA,NA,NA,NA +MA,25000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003447,NA,NA,NA,NA,NA,NA,NA,NA +MA,25000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00593,NA,NA,NA,NA,NA,NA,NA,NA +MA,25000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.010795,NA,NA,NA,NA,NA,NA,NA,NA +MA,25000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.011708,NA,NA,NA,NA,NA,NA,NA,NA +MA,25000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008408,NA,NA,NA,NA,NA,NA,NA,NA +MA,25000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00671,NA,NA,NA,NA,NA,NA,NA,NA +MA,25000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004521,NA,NA,NA,NA,NA,NA,NA,NA +MD,24000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001068,NA,NA,NA,NA,NA,NA,NA,NA +MD,24000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002659,NA,NA,NA,NA,NA,NA,NA,NA +MD,24000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005003,NA,NA,NA,NA,NA,NA,NA,NA +MD,24000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.009014,NA,NA,NA,NA,NA,NA,NA,NA +MD,24000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007697,NA,NA,NA,NA,NA,NA,NA,NA +MD,24000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006153,NA,NA,NA,NA,NA,NA,NA,NA +MD,24000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006499,NA,NA,NA,NA,NA,NA,NA,NA +MD,24000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00596,NA,NA,NA,NA,NA,NA,NA,NA +ME,23000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001276,NA,NA,NA,NA,NA,NA,NA,NA +ME,23000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00329,NA,NA,NA,NA,NA,NA,NA,NA +ME,23000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005684,NA,NA,NA,NA,NA,NA,NA,NA +ME,23000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.010999,NA,NA,NA,NA,NA,NA,NA,NA +ME,23000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008499,NA,NA,NA,NA,NA,NA,NA,NA +ME,23000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007334,NA,NA,NA,NA,NA,NA,NA,NA +ME,23000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008344,NA,NA,NA,NA,NA,NA,NA,NA +ME,23000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008117,NA,NA,NA,NA,NA,NA,NA,NA +MI,26000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001021,NA,NA,NA,NA,NA,NA,NA,NA +MI,26000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002897,NA,NA,NA,NA,NA,NA,NA,NA +MI,26000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004235,NA,NA,NA,NA,NA,NA,NA,NA +MI,26000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007429,NA,NA,NA,NA,NA,NA,NA,NA +MI,26000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004843,NA,NA,NA,NA,NA,NA,NA,NA +MI,26000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003954,NA,NA,NA,NA,NA,NA,NA,NA +MI,26000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004991,NA,NA,NA,NA,NA,NA,NA,NA +MI,26000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005088,NA,NA,NA,NA,NA,NA,NA,NA +MN,27000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,8.75e-4,NA,NA,NA,NA,NA,NA,NA,NA +MN,27000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003259,NA,NA,NA,NA,NA,NA,NA,NA +MN,27000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005394,NA,NA,NA,NA,NA,NA,NA,NA +MN,27000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008294,NA,NA,NA,NA,NA,NA,NA,NA +MN,27000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006285,NA,NA,NA,NA,NA,NA,NA,NA +MN,27000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005101,NA,NA,NA,NA,NA,NA,NA,NA +MN,27000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005772,NA,NA,NA,NA,NA,NA,NA,NA +MN,27000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005718,NA,NA,NA,NA,NA,NA,NA,NA +MO,29000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,8.54e-4,NA,NA,NA,NA,NA,NA,NA,NA +MO,29000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002774,NA,NA,NA,NA,NA,NA,NA,NA +MO,29000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003972,NA,NA,NA,NA,NA,NA,NA,NA +MO,29000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005893,NA,NA,NA,NA,NA,NA,NA,NA +MO,29000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003574,NA,NA,NA,NA,NA,NA,NA,NA +MO,29000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002749,NA,NA,NA,NA,NA,NA,NA,NA +MO,29000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003287,NA,NA,NA,NA,NA,NA,NA,NA +MO,29000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003573,NA,NA,NA,NA,NA,NA,NA,NA +MP,69000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00187,NA,NA,NA,NA,NA,NA,NA,NA +MP,69000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004072,NA,NA,NA,NA,NA,NA,NA,NA +MP,69000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003597,NA,NA,NA,NA,NA,NA,NA,NA +MP,69000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005874,NA,NA,NA,NA,NA,NA,NA,NA +MP,69000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004361,NA,NA,NA,NA,NA,NA,NA,NA +MP,69000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004769,NA,NA,NA,NA,NA,NA,NA,NA +MP,69000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00444,NA,NA,NA,NA,NA,NA,NA,NA +MP,69000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004518,NA,NA,NA,NA,NA,NA,NA,NA +MS,28000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,9.69e-4,NA,NA,NA,NA,NA,NA,NA,NA +MS,28000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002764,NA,NA,NA,NA,NA,NA,NA,NA +MS,28000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003859,NA,NA,NA,NA,NA,NA,NA,NA +MS,28000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003698,NA,NA,NA,NA,NA,NA,NA,NA +MS,28000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002123,NA,NA,NA,NA,NA,NA,NA,NA +MS,28000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001683,NA,NA,NA,NA,NA,NA,NA,NA +MS,28000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002325,NA,NA,NA,NA,NA,NA,NA,NA +MS,28000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003066,NA,NA,NA,NA,NA,NA,NA,NA +MT,30000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,5.83e-4,NA,NA,NA,NA,NA,NA,NA,NA +MT,30000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003727,NA,NA,NA,NA,NA,NA,NA,NA +MT,30000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005204,NA,NA,NA,NA,NA,NA,NA,NA +MT,30000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006569,NA,NA,NA,NA,NA,NA,NA,NA +MT,30000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003107,NA,NA,NA,NA,NA,NA,NA,NA +MT,30000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003141,NA,NA,NA,NA,NA,NA,NA,NA +MT,30000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003945,NA,NA,NA,NA,NA,NA,NA,NA +MT,30000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003914,NA,NA,NA,NA,NA,NA,NA,NA +NC,37000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,6.41e-4,NA,NA,NA,NA,NA,NA,NA,NA +NC,37000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003656,NA,NA,NA,NA,NA,NA,NA,NA +NC,37000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004385,NA,NA,NA,NA,NA,NA,NA,NA +NC,37000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006358,NA,NA,NA,NA,NA,NA,NA,NA +NC,37000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003274,NA,NA,NA,NA,NA,NA,NA,NA +NC,37000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002715,NA,NA,NA,NA,NA,NA,NA,NA +NC,37000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004056,NA,NA,NA,NA,NA,NA,NA,NA +NC,37000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005478,NA,NA,NA,NA,NA,NA,NA,NA +ND,38000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001679,NA,NA,NA,NA,NA,NA,NA,NA +ND,38000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002858,NA,NA,NA,NA,NA,NA,NA,NA +ND,38000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005731,NA,NA,NA,NA,NA,NA,NA,NA +ND,38000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005392,NA,NA,NA,NA,NA,NA,NA,NA +ND,38000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001724,NA,NA,NA,NA,NA,NA,NA,NA +ND,38000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002575,NA,NA,NA,NA,NA,NA,NA,NA +ND,38000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003916,NA,NA,NA,NA,NA,NA,NA,NA +ND,38000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003931,NA,NA,NA,NA,NA,NA,NA,NA +NE,31000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00123,NA,NA,NA,NA,NA,NA,NA,NA +NE,31000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002341,NA,NA,NA,NA,NA,NA,NA,NA +NE,31000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00543,NA,NA,NA,NA,NA,NA,NA,NA +NE,31000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007955,NA,NA,NA,NA,NA,NA,NA,NA +NE,31000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003575,NA,NA,NA,NA,NA,NA,NA,NA +NE,31000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00359,NA,NA,NA,NA,NA,NA,NA,NA +NE,31000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004064,NA,NA,NA,NA,NA,NA,NA,NA +NE,31000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003986,NA,NA,NA,NA,NA,NA,NA,NA +NH,33000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,1.96e-4,NA,NA,NA,NA,NA,NA,NA,NA +NH,33000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003713,NA,NA,NA,NA,NA,NA,NA,NA +NH,33000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006088,NA,NA,NA,NA,NA,NA,NA,NA +NH,33000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.016931,NA,NA,NA,NA,NA,NA,NA,NA +NH,33000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00496,NA,NA,NA,NA,NA,NA,NA,NA +NH,33000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004555,NA,NA,NA,NA,NA,NA,NA,NA +NH,33000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006668,NA,NA,NA,NA,NA,NA,NA,NA +NH,33000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00686,NA,NA,NA,NA,NA,NA,NA,NA +NJ,34000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,9.75e-4,NA,NA,NA,NA,NA,NA,NA,NA +NJ,34000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003007,NA,NA,NA,NA,NA,NA,NA,NA +NJ,34000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00573,NA,NA,NA,NA,NA,NA,NA,NA +NJ,34000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.009843,NA,NA,NA,NA,NA,NA,NA,NA +NJ,34000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007756,NA,NA,NA,NA,NA,NA,NA,NA +NJ,34000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006326,NA,NA,NA,NA,NA,NA,NA,NA +NJ,34000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005596,NA,NA,NA,NA,NA,NA,NA,NA +NJ,34000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005557,NA,NA,NA,NA,NA,NA,NA,NA +NM,35000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005124,NA,NA,NA,NA,NA,NA,NA,NA +NM,35000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007049,NA,NA,NA,NA,NA,NA,NA,NA +NM,35000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008913,NA,NA,NA,NA,NA,NA,NA,NA +NM,35000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005217,NA,NA,NA,NA,NA,NA,NA,NA +NM,35000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005211,NA,NA,NA,NA,NA,NA,NA,NA +NM,35000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006225,NA,NA,NA,NA,NA,NA,NA,NA +NM,35000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007262,NA,NA,NA,NA,NA,NA,NA,NA +NV,32000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,1.07e-4,NA,NA,NA,NA,NA,NA,NA,NA +NV,32000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00392,NA,NA,NA,NA,NA,NA,NA,NA +NV,32000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004457,NA,NA,NA,NA,NA,NA,NA,NA +NV,32000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006877,NA,NA,NA,NA,NA,NA,NA,NA +NV,32000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004096,NA,NA,NA,NA,NA,NA,NA,NA +NV,32000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003606,NA,NA,NA,NA,NA,NA,NA,NA +NV,32000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003599,NA,NA,NA,NA,NA,NA,NA,NA +NV,32000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00424,NA,NA,NA,NA,NA,NA,NA,NA +NY,36000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,4.63e-4,NA,NA,NA,NA,NA,NA,NA,NA +NY,36000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003562,NA,NA,NA,NA,NA,NA,NA,NA +NY,36000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004922,NA,NA,NA,NA,NA,NA,NA,NA +NY,36000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008693,NA,NA,NA,NA,NA,NA,NA,NA +NY,36000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006354,NA,NA,NA,NA,NA,NA,NA,NA +NY,36000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005819,NA,NA,NA,NA,NA,NA,NA,NA +NY,36000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005997,NA,NA,NA,NA,NA,NA,NA,NA +NY,36000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005131,NA,NA,NA,NA,NA,NA,NA,NA +OH,39000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001169,NA,NA,NA,NA,NA,NA,NA,NA +OH,39000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00267,NA,NA,NA,NA,NA,NA,NA,NA +OH,39000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004276,NA,NA,NA,NA,NA,NA,NA,NA +OH,39000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007267,NA,NA,NA,NA,NA,NA,NA,NA +OH,39000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.0034,NA,NA,NA,NA,NA,NA,NA,NA +OH,39000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003255,NA,NA,NA,NA,NA,NA,NA,NA +OH,39000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003161,NA,NA,NA,NA,NA,NA,NA,NA +OH,39000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003561,NA,NA,NA,NA,NA,NA,NA,NA +OK,40000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001114,NA,NA,NA,NA,NA,NA,NA,NA +OK,40000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003242,NA,NA,NA,NA,NA,NA,NA,NA +OK,40000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005228,NA,NA,NA,NA,NA,NA,NA,NA +OK,40000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005614,NA,NA,NA,NA,NA,NA,NA,NA +OK,40000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002007,NA,NA,NA,NA,NA,NA,NA,NA +OK,40000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002001,NA,NA,NA,NA,NA,NA,NA,NA +OK,40000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002067,NA,NA,NA,NA,NA,NA,NA,NA +OK,40000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002009,NA,NA,NA,NA,NA,NA,NA,NA +OR,41000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001191,NA,NA,NA,NA,NA,NA,NA,NA +OR,41000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002842,NA,NA,NA,NA,NA,NA,NA,NA +OR,41000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004293,NA,NA,NA,NA,NA,NA,NA,NA +OR,41000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007712,NA,NA,NA,NA,NA,NA,NA,NA +OR,41000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007987,NA,NA,NA,NA,NA,NA,NA,NA +OR,41000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006005,NA,NA,NA,NA,NA,NA,NA,NA +OR,41000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004637,NA,NA,NA,NA,NA,NA,NA,NA +OR,41000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003582,NA,NA,NA,NA,NA,NA,NA,NA +PA,42000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,7.98e-4,NA,NA,NA,NA,NA,NA,NA,NA +PA,42000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002889,NA,NA,NA,NA,NA,NA,NA,NA +PA,42000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005107,NA,NA,NA,NA,NA,NA,NA,NA +PA,42000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.009101,NA,NA,NA,NA,NA,NA,NA,NA +PA,42000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008397,NA,NA,NA,NA,NA,NA,NA,NA +PA,42000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005664,NA,NA,NA,NA,NA,NA,NA,NA +PA,42000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005252,NA,NA,NA,NA,NA,NA,NA,NA +PA,42000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00518,NA,NA,NA,NA,NA,NA,NA,NA +PR,72000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,1.2e-4,NA,NA,NA,NA,NA,NA,NA,NA +PR,72000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002806,NA,NA,NA,NA,NA,NA,NA,NA +PR,72000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002673,NA,NA,NA,NA,NA,NA,NA,NA +PR,72000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005806,NA,NA,NA,NA,NA,NA,NA,NA +PR,72000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007686,NA,NA,NA,NA,NA,NA,NA,NA +PR,72000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.010066,NA,NA,NA,NA,NA,NA,NA,NA +PR,72000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005251,NA,NA,NA,NA,NA,NA,NA,NA +PR,72000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003103,NA,NA,NA,NA,NA,NA,NA,NA +RI,44000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001005,NA,NA,NA,NA,NA,NA,NA,NA +RI,44000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002291,NA,NA,NA,NA,NA,NA,NA,NA +RI,44000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007043,NA,NA,NA,NA,NA,NA,NA,NA +RI,44000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008476,NA,NA,NA,NA,NA,NA,NA,NA +RI,44000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.009584,NA,NA,NA,NA,NA,NA,NA,NA +RI,44000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00624,NA,NA,NA,NA,NA,NA,NA,NA +RI,44000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005759,NA,NA,NA,NA,NA,NA,NA,NA +RI,44000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004904,NA,NA,NA,NA,NA,NA,NA,NA +SC,45000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,6.52e-4,NA,NA,NA,NA,NA,NA,NA,NA +SC,45000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003076,NA,NA,NA,NA,NA,NA,NA,NA +SC,45000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004293,NA,NA,NA,NA,NA,NA,NA,NA +SC,45000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006142,NA,NA,NA,NA,NA,NA,NA,NA +SC,45000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002733,NA,NA,NA,NA,NA,NA,NA,NA +SC,45000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002738,NA,NA,NA,NA,NA,NA,NA,NA +SC,45000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003436,NA,NA,NA,NA,NA,NA,NA,NA +SC,45000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003906,NA,NA,NA,NA,NA,NA,NA,NA +SD,46000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001286,NA,NA,NA,NA,NA,NA,NA,NA +SD,46000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003045,NA,NA,NA,NA,NA,NA,NA,NA +SD,46000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006832,NA,NA,NA,NA,NA,NA,NA,NA +SD,46000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007449,NA,NA,NA,NA,NA,NA,NA,NA +SD,46000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002513,NA,NA,NA,NA,NA,NA,NA,NA +SD,46000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003085,NA,NA,NA,NA,NA,NA,NA,NA +SD,46000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004862,NA,NA,NA,NA,NA,NA,NA,NA +SD,46000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005295,NA,NA,NA,NA,NA,NA,NA,NA +TN,47000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001256,NA,NA,NA,NA,NA,NA,NA,NA +TN,47000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002297,NA,NA,NA,NA,NA,NA,NA,NA +TN,47000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003566,NA,NA,NA,NA,NA,NA,NA,NA +TN,47000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005577,NA,NA,NA,NA,NA,NA,NA,NA +TN,47000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003139,NA,NA,NA,NA,NA,NA,NA,NA +TN,47000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002314,NA,NA,NA,NA,NA,NA,NA,NA +TN,47000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002869,NA,NA,NA,NA,NA,NA,NA,NA +TN,47000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003197,NA,NA,NA,NA,NA,NA,NA,NA +TX,48000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00104,NA,NA,NA,NA,NA,NA,NA,NA +TX,48000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002662,NA,NA,NA,NA,NA,NA,NA,NA +TX,48000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00386,NA,NA,NA,NA,NA,NA,NA,NA +TX,48000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006748,NA,NA,NA,NA,NA,NA,NA,NA +TX,48000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003813,NA,NA,NA,NA,NA,NA,NA,NA +TX,48000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003763,NA,NA,NA,NA,NA,NA,NA,NA +TX,48000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003366,NA,NA,NA,NA,NA,NA,NA,NA +TX,48000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003568,NA,NA,NA,NA,NA,NA,NA,NA +UT,49000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001195,NA,NA,NA,NA,NA,NA,NA,NA +UT,49000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002924,NA,NA,NA,NA,NA,NA,NA,NA +UT,49000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003472,NA,NA,NA,NA,NA,NA,NA,NA +UT,49000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007139,NA,NA,NA,NA,NA,NA,NA,NA +UT,49000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00447,NA,NA,NA,NA,NA,NA,NA,NA +UT,49000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003338,NA,NA,NA,NA,NA,NA,NA,NA +UT,49000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003455,NA,NA,NA,NA,NA,NA,NA,NA +UT,49000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004197,NA,NA,NA,NA,NA,NA,NA,NA +VA,51000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,9.2e-4,NA,NA,NA,NA,NA,NA,NA,NA +VA,51000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003394,NA,NA,NA,NA,NA,NA,NA,NA +VA,51000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004607,NA,NA,NA,NA,NA,NA,NA,NA +VA,51000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008845,NA,NA,NA,NA,NA,NA,NA,NA +VA,51000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006556,NA,NA,NA,NA,NA,NA,NA,NA +VA,51000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005956,NA,NA,NA,NA,NA,NA,NA,NA +VA,51000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007471,NA,NA,NA,NA,NA,NA,NA,NA +VA,51000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008116,NA,NA,NA,NA,NA,NA,NA,NA +VI,78000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,3.92e-4,NA,NA,NA,NA,NA,NA,NA,NA +VI,78000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002511,NA,NA,NA,NA,NA,NA,NA,NA +VI,78000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003722,NA,NA,NA,NA,NA,NA,NA,NA +VI,78000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.0047,NA,NA,NA,NA,NA,NA,NA,NA +VI,78000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002398,NA,NA,NA,NA,NA,NA,NA,NA +VI,78000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00255,NA,NA,NA,NA,NA,NA,NA,NA +VI,78000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003405,NA,NA,NA,NA,NA,NA,NA,NA +VI,78000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003368,NA,NA,NA,NA,NA,NA,NA,NA +VT,50000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001255,NA,NA,NA,NA,NA,NA,NA,NA +VT,50000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002859,NA,NA,NA,NA,NA,NA,NA,NA +VT,50000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005581,NA,NA,NA,NA,NA,NA,NA,NA +VT,50000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.010141,NA,NA,NA,NA,NA,NA,NA,NA +VT,50000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.014482,NA,NA,NA,NA,NA,NA,NA,NA +VT,50000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008818,NA,NA,NA,NA,NA,NA,NA,NA +VT,50000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003411,NA,NA,NA,NA,NA,NA,NA,NA +VT,50000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003076,NA,NA,NA,NA,NA,NA,NA,NA +WA,53000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,5.61e-4,NA,NA,NA,NA,NA,NA,NA,NA +WA,53000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003633,NA,NA,NA,NA,NA,NA,NA,NA +WA,53000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004755,NA,NA,NA,NA,NA,NA,NA,NA +WA,53000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008176,NA,NA,NA,NA,NA,NA,NA,NA +WA,53000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008216,NA,NA,NA,NA,NA,NA,NA,NA +WA,53000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.007167,NA,NA,NA,NA,NA,NA,NA,NA +WA,53000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.006675,NA,NA,NA,NA,NA,NA,NA,NA +WA,53000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005865,NA,NA,NA,NA,NA,NA,NA,NA +WI,55000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,8.73e-4,NA,NA,NA,NA,NA,NA,NA,NA +WI,55000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003428,NA,NA,NA,NA,NA,NA,NA,NA +WI,55000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004815,NA,NA,NA,NA,NA,NA,NA,NA +WI,55000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.008678,NA,NA,NA,NA,NA,NA,NA,NA +WI,55000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004268,NA,NA,NA,NA,NA,NA,NA,NA +WI,55000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004013,NA,NA,NA,NA,NA,NA,NA,NA +WI,55000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004666,NA,NA,NA,NA,NA,NA,NA,NA +WI,55000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.005008,NA,NA,NA,NA,NA,NA,NA,NA +WV,54000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001851,NA,NA,NA,NA,NA,NA,NA,NA +WV,54000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.002902,NA,NA,NA,NA,NA,NA,NA,NA +WV,54000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004229,NA,NA,NA,NA,NA,NA,NA,NA +WV,54000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004682,NA,NA,NA,NA,NA,NA,NA,NA +WV,54000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003996,NA,NA,NA,NA,NA,NA,NA,NA +WV,54000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003008,NA,NA,NA,NA,NA,NA,NA,NA +WV,54000,2021-07-01,2021-07-31,Dose1_jul2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003992,NA,NA,NA,NA,NA,NA,NA,NA +WV,54000,2021-08-01,2021-08-07,Dose1_aug2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003925,NA,NA,NA,NA,NA,NA,NA,NA +WY,56000,2021-01-01,2021-01-31,Dose1_jan2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.001042,NA,NA,NA,NA,NA,NA,NA,NA +WY,56000,2021-02-01,2021-02-28,Dose1_feb2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00325,NA,NA,NA,NA,NA,NA,NA,NA +WY,56000,2021-03-01,2021-03-31,Dose1_mar2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.00427,NA,NA,NA,NA,NA,NA,NA,NA +WY,56000,2021-04-01,2021-04-30,Dose1_apr2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.004258,NA,NA,NA,NA,NA,NA,NA,NA +WY,56000,2021-05-01,2021-05-31,Dose1_may2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.0017,NA,NA,NA,NA,NA,NA,NA,NA +WY,56000,2021-06-01,2021-06-30,Dose1_jun2021,SinglePeriodModifier,transmission,vaccination,transition_rate 0,NA,fixed,0.003188,NA,NA,NA,NA,NA,NA,NA,NA 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+CT,09000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7805,0.01,0,1,NA,NA,NA,NA,NA +DC,11000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11070000000000002,0.01,0,1,NA,NA,NA,NA,NA +DC,11000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15080000000000005,0.01,0,1,NA,NA,NA,NA,NA +DC,11000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.24419999999999997,0.01,0,1,NA,NA,NA,NA,NA +DC,11000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3477,0.01,0,1,NA,NA,NA,NA,NA +DC,11000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4547,0.01,0,1,NA,NA,NA,NA,NA +DC,11000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5264,0.01,0,1,NA,NA,NA,NA,NA +DC,11000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5603,0.01,0,1,NA,NA,NA,NA,NA +DC,11000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5760000000000001,0.01,0,1,NA,NA,NA,NA,NA +DE,10000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09919999999999995,0.01,0,1,NA,NA,NA,NA,NA +DE,10000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16510000000000002,0.01,0,1,NA,NA,NA,NA,NA +DE,10000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3367,0.01,0,1,NA,NA,NA,NA,NA +DE,10000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5131,0.01,0,1,NA,NA,NA,NA,NA +DE,10000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6295,0.01,0,1,NA,NA,NA,NA,NA +DE,10000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6813,0.01,0,1,NA,NA,NA,NA,NA +DE,10000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6925,0.01,0,1,NA,NA,NA,NA,NA +DE,10000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6748000000000001,0.01,0,1,NA,NA,NA,NA,NA +FL,12000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.10799999999999998,0.01,0,1,NA,NA,NA,NA,NA +FL,12000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16800000000000004,0.01,0,1,NA,NA,NA,NA,NA +FL,12000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.32530000000000003,0.01,0,1,NA,NA,NA,NA,NA +FL,12000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4935000000000001,0.01,0,1,NA,NA,NA,NA,NA +FL,12000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.613,0.01,0,1,NA,NA,NA,NA,NA +FL,12000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6705,0.01,0,1,NA,NA,NA,NA,NA +FL,12000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6844,0.01,0,1,NA,NA,NA,NA,NA +FL,12000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6687000000000001,0.01,0,1,NA,NA,NA,NA,NA +GA,13000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09850000000000005,0.01,0,1,NA,NA,NA,NA,NA +GA,13000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16549999999999998,0.01,0,1,NA,NA,NA,NA,NA +GA,13000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.34119999999999995,0.01,0,1,NA,NA,NA,NA,NA +GA,13000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5245,0.01,0,1,NA,NA,NA,NA,NA +GA,13000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6466000000000001,0.01,0,1,NA,NA,NA,NA,NA +GA,13000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7026,0.01,0,1,NA,NA,NA,NA,NA +GA,13000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.718,0.01,0,1,NA,NA,NA,NA,NA +GA,13000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7072,0.01,0,1,NA,NA,NA,NA,NA +GU,66000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15390000000000004,0.01,0,1,NA,NA,NA,NA,NA +GU,66000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2108,0.01,0,1,NA,NA,NA,NA,NA +GU,66000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3448,0.01,0,1,NA,NA,NA,NA,NA +GU,66000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4714,0.01,0,1,NA,NA,NA,NA,NA +GU,66000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.563,0.01,0,1,NA,NA,NA,NA,NA +GU,66000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6122000000000001,0.01,0,1,NA,NA,NA,NA,NA +GU,66000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6285000000000001,0.01,0,1,NA,NA,NA,NA,NA +GU,66000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6134,0.01,0,1,NA,NA,NA,NA,NA +HI,15000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09230000000000003,0.01,0,1,NA,NA,NA,NA,NA +HI,15000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15059999999999996,0.01,0,1,NA,NA,NA,NA,NA +HI,15000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.29910000000000003,0.01,0,1,NA,NA,NA,NA,NA +HI,15000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4653000000000001,0.01,0,1,NA,NA,NA,NA,NA +HI,15000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6356999999999999,0.01,0,1,NA,NA,NA,NA,NA +HI,15000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7482,0.01,0,1,NA,NA,NA,NA,NA +HI,15000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7744,0.01,0,1,NA,NA,NA,NA,NA +HI,15000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7635000000000001,0.01,0,1,NA,NA,NA,NA,NA +IA,19000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.089400000000000035,0.01,0,1,NA,NA,NA,NA,NA +IA,19000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.12860000000000005,0.01,0,1,NA,NA,NA,NA,NA +IA,19000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2279,0.01,0,1,NA,NA,NA,NA,NA +IA,19000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3468,0.01,0,1,NA,NA,NA,NA,NA +IA,19000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4617,0.01,0,1,NA,NA,NA,NA,NA +IA,19000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5528,0.01,0,1,NA,NA,NA,NA,NA +IA,19000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6133,0.01,0,1,NA,NA,NA,NA,NA +IA,19000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6528,0.01,0,1,NA,NA,NA,NA,NA +ID,16000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11939999999999996,0.01,0,1,NA,NA,NA,NA,NA +ID,16000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16679999999999995,0.01,0,1,NA,NA,NA,NA,NA +ID,16000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2823,0.01,0,1,NA,NA,NA,NA,NA +ID,16000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4031,0.01,0,1,NA,NA,NA,NA,NA +ID,16000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.505,0.01,0,1,NA,NA,NA,NA,NA +ID,16000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.573,0.01,0,1,NA,NA,NA,NA,NA +ID,16000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6081,0.01,0,1,NA,NA,NA,NA,NA +ID,16000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6069,0.01,0,1,NA,NA,NA,NA,NA +IL,17000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09240000000000004,0.01,0,1,NA,NA,NA,NA,NA +IL,17000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13429999999999995,0.01,0,1,NA,NA,NA,NA,NA +IL,17000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.24170000000000005,0.01,0,1,NA,NA,NA,NA,NA +IL,17000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.365,0.01,0,1,NA,NA,NA,NA,NA +IL,17000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.47629999999999995,0.01,0,1,NA,NA,NA,NA,NA +IL,17000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5567,0.01,0,1,NA,NA,NA,NA,NA +IL,17000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6061000000000001,0.01,0,1,NA,NA,NA,NA,NA +IL,17000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.619,0.01,0,1,NA,NA,NA,NA,NA +IN,18000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09919999999999995,0.01,0,1,NA,NA,NA,NA,NA +IN,18000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1612,0.01,0,1,NA,NA,NA,NA,NA +IN,18000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.31899999999999995,0.01,0,1,NA,NA,NA,NA,NA +IN,18000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4754,0.01,0,1,NA,NA,NA,NA,NA +IN,18000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5694,0.01,0,1,NA,NA,NA,NA,NA +IN,18000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5992,0.01,0,1,NA,NA,NA,NA,NA +IN,18000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5912,0.01,0,1,NA,NA,NA,NA,NA +IN,18000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5566,0.01,0,1,NA,NA,NA,NA,NA +KS,20000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1211,0.01,0,1,NA,NA,NA,NA,NA +KS,20000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16549999999999998,0.01,0,1,NA,NA,NA,NA,NA +KS,20000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2751,0.01,0,1,NA,NA,NA,NA,NA +KS,20000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3922,0.01,0,1,NA,NA,NA,NA,NA +KS,20000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.495,0.01,0,1,NA,NA,NA,NA,NA +KS,20000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5687,0.01,0,1,NA,NA,NA,NA,NA +KS,20000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6135999999999999,0.01,0,1,NA,NA,NA,NA,NA +KS,20000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6252,0.01,0,1,NA,NA,NA,NA,NA +KY,21000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.0917,0.01,0,1,NA,NA,NA,NA,NA +KY,21000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.14839999999999998,0.01,0,1,NA,NA,NA,NA,NA +KY,21000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2891,0.01,0,1,NA,NA,NA,NA,NA +KY,21000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4278,0.01,0,1,NA,NA,NA,NA,NA +KY,21000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5147999999999999,0.01,0,1,NA,NA,NA,NA,NA +KY,21000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.546,0.01,0,1,NA,NA,NA,NA,NA +KY,21000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5446,0.01,0,1,NA,NA,NA,NA,NA +KY,21000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5175000000000001,0.01,0,1,NA,NA,NA,NA,NA +LA,22000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.10840000000000004,0.01,0,1,NA,NA,NA,NA,NA +LA,22000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1703,0.01,0,1,NA,NA,NA,NA,NA +LA,22000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3206,0.01,0,1,NA,NA,NA,NA,NA +LA,22000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4588,0.01,0,1,NA,NA,NA,NA,NA +LA,22000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5387,0.01,0,1,NA,NA,NA,NA,NA +LA,22000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5668,0.01,0,1,NA,NA,NA,NA,NA +LA,22000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5665,0.01,0,1,NA,NA,NA,NA,NA +LA,22000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5450999999999999,0.01,0,1,NA,NA,NA,NA,NA +MA,25000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.10440000000000003,0.01,0,1,NA,NA,NA,NA,NA +MA,25000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15949999999999998,0.01,0,1,NA,NA,NA,NA,NA +MA,25000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.30589999999999995,0.01,0,1,NA,NA,NA,NA,NA +MA,25000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4787,0.01,0,1,NA,NA,NA,NA,NA +MA,25000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6269,0.01,0,1,NA,NA,NA,NA,NA +MA,25000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7229,0.01,0,1,NA,NA,NA,NA,NA +MA,25000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7734,0.01,0,1,NA,NA,NA,NA,NA +MA,25000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7922,0.01,0,1,NA,NA,NA,NA,NA +MD,24000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09760000000000002,0.01,0,1,NA,NA,NA,NA,NA +MD,24000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13939999999999997,0.01,0,1,NA,NA,NA,NA,NA +MD,24000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.25039999999999996,0.01,0,1,NA,NA,NA,NA,NA +MD,24000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3842,0.01,0,1,NA,NA,NA,NA,NA +MD,24000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5105999999999999,0.01,0,1,NA,NA,NA,NA,NA +MD,24000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6079,0.01,0,1,NA,NA,NA,NA,NA +MD,24000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6719999999999999,0.01,0,1,NA,NA,NA,NA,NA +MD,24000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6997,0.01,0,1,NA,NA,NA,NA,NA +ME,23000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11950000000000004,0.01,0,1,NA,NA,NA,NA,NA +ME,23000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.17290000000000005,0.01,0,1,NA,NA,NA,NA,NA +ME,23000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3183,0.01,0,1,NA,NA,NA,NA,NA +ME,23000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4582000000000001,0.01,0,1,NA,NA,NA,NA,NA +ME,23000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5484,0.01,0,1,NA,NA,NA,NA,NA +ME,23000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6,0.01,0,1,NA,NA,NA,NA,NA +ME,23000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6276999999999999,0.01,0,1,NA,NA,NA,NA,NA +ME,23000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6306,0.01,0,1,NA,NA,NA,NA,NA +MI,26000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1231,0.01,0,1,NA,NA,NA,NA,NA +MI,26000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16700000000000004,0.01,0,1,NA,NA,NA,NA,NA +MI,26000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2724,0.01,0,1,NA,NA,NA,NA,NA +MI,26000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3822,0.01,0,1,NA,NA,NA,NA,NA +MI,26000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4778,0.01,0,1,NA,NA,NA,NA,NA +MI,26000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5438000000000001,0.01,0,1,NA,NA,NA,NA,NA +MI,26000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5784,0.01,0,1,NA,NA,NA,NA,NA +MI,26000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.577,0.01,0,1,NA,NA,NA,NA,NA +MN,27000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.08919999999999995,0.01,0,1,NA,NA,NA,NA,NA +MN,27000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13439999999999996,0.01,0,1,NA,NA,NA,NA,NA +MN,27000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2551,0.01,0,1,NA,NA,NA,NA,NA +MN,27000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4036,0.01,0,1,NA,NA,NA,NA,NA +MN,27000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5453,0.01,0,1,NA,NA,NA,NA,NA +MN,27000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6523,0.01,0,1,NA,NA,NA,NA,NA +MN,27000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7185,0.01,0,1,NA,NA,NA,NA,NA +MN,27000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7425999999999999,0.01,0,1,NA,NA,NA,NA,NA +MO,29000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.06320000000000003,0.01,0,1,NA,NA,NA,NA,NA +MO,29000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.10609999999999996,0.01,0,1,NA,NA,NA,NA,NA +MO,29000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.23429999999999995,0.01,0,1,NA,NA,NA,NA,NA +MO,29000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3934,0.01,0,1,NA,NA,NA,NA,NA +MO,29000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.509,0.01,0,1,NA,NA,NA,NA,NA +MO,29000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5606,0.01,0,1,NA,NA,NA,NA,NA +MO,29000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5703,0.01,0,1,NA,NA,NA,NA,NA +MO,29000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5483,0.01,0,1,NA,NA,NA,NA,NA +MP,69000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15380000000000005,0.01,0,1,NA,NA,NA,NA,NA +MP,69000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2107,0.01,0,1,NA,NA,NA,NA,NA +MP,69000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3449,0.01,0,1,NA,NA,NA,NA,NA +MP,69000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4714,0.01,0,1,NA,NA,NA,NA,NA +MP,69000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5630999999999999,0.01,0,1,NA,NA,NA,NA,NA +MP,69000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6122000000000001,0.01,0,1,NA,NA,NA,NA,NA +MP,69000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6286,0.01,0,1,NA,NA,NA,NA,NA +MP,69000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6134,0.01,0,1,NA,NA,NA,NA,NA +MS,28000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.08399999999999996,0.01,0,1,NA,NA,NA,NA,NA +MS,28000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13639999999999997,0.01,0,1,NA,NA,NA,NA,NA +MS,28000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.26849999999999996,0.01,0,1,NA,NA,NA,NA,NA +MS,28000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3996,0.01,0,1,NA,NA,NA,NA,NA +MS,28000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4829,0.01,0,1,NA,NA,NA,NA,NA +MS,28000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5163,0.01,0,1,NA,NA,NA,NA,NA +MS,28000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5183,0.01,0,1,NA,NA,NA,NA,NA +MS,28000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4919,0.01,0,1,NA,NA,NA,NA,NA +MT,30000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1382,0.01,0,1,NA,NA,NA,NA,NA +MT,30000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.19599999999999995,0.01,0,1,NA,NA,NA,NA,NA +MT,30000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.32789999999999997,0.01,0,1,NA,NA,NA,NA,NA +MT,30000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4583,0.01,0,1,NA,NA,NA,NA,NA +MT,30000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5552,0.01,0,1,NA,NA,NA,NA,NA +MT,30000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6073999999999999,0.01,0,1,NA,NA,NA,NA,NA +MT,30000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6241,0.01,0,1,NA,NA,NA,NA,NA +MT,30000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6087,0.01,0,1,NA,NA,NA,NA,NA +NC,37000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13890000000000002,0.01,0,1,NA,NA,NA,NA,NA +NC,37000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.17490000000000006,0.01,0,1,NA,NA,NA,NA,NA +NC,37000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.25849999999999995,0.01,0,1,NA,NA,NA,NA,NA +NC,37000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3415,0.01,0,1,NA,NA,NA,NA,NA +NC,37000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4227,0.01,0,1,NA,NA,NA,NA,NA +NC,37000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4932,0.01,0,1,NA,NA,NA,NA,NA +NC,37000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5472,0.01,0,1,NA,NA,NA,NA,NA +NC,37000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5731999999999999,0.01,0,1,NA,NA,NA,NA,NA +ND,38000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16300000000000003,0.01,0,1,NA,NA,NA,NA,NA +ND,38000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2208,0.01,0,1,NA,NA,NA,NA,NA +ND,38000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.35440000000000005,0.01,0,1,NA,NA,NA,NA,NA +ND,38000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.479,0.01,0,1,NA,NA,NA,NA,NA +ND,38000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5704,0.01,0,1,NA,NA,NA,NA,NA +ND,38000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6211,0.01,0,1,NA,NA,NA,NA,NA +ND,38000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6395,0.01,0,1,NA,NA,NA,NA,NA +ND,38000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6273,0.01,0,1,NA,NA,NA,NA,NA +NE,31000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11050000000000004,0.01,0,1,NA,NA,NA,NA,NA +NE,31000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.17159999999999995,0.01,0,1,NA,NA,NA,NA,NA +NE,31000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3278,0.01,0,1,NA,NA,NA,NA,NA +NE,31000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4867,0.01,0,1,NA,NA,NA,NA,NA +NE,31000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5892999999999999,0.01,0,1,NA,NA,NA,NA,NA +NE,31000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6302,0.01,0,1,NA,NA,NA,NA,NA +NE,31000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6328,0.01,0,1,NA,NA,NA,NA,NA +NE,31000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6068,0.01,0,1,NA,NA,NA,NA,NA +NH,33000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13649999999999995,0.01,0,1,NA,NA,NA,NA,NA +NH,33000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.19330000000000003,0.01,0,1,NA,NA,NA,NA,NA +NH,33000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3226,0.01,0,1,NA,NA,NA,NA,NA +NH,33000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4496,0.01,0,1,NA,NA,NA,NA,NA +NH,33000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5432,0.01,0,1,NA,NA,NA,NA,NA +NH,33000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5928,0.01,0,1,NA,NA,NA,NA,NA +NH,33000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6071,0.01,0,1,NA,NA,NA,NA,NA +NH,33000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5892,0.01,0,1,NA,NA,NA,NA,NA +NJ,34000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.20120000000000005,0.01,0,1,NA,NA,NA,NA,NA +NJ,34000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2228,0.01,0,1,NA,NA,NA,NA,NA +NJ,34000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3962,0.01,0,1,NA,NA,NA,NA,NA +NJ,34000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7029000000000001,0.01,0,1,NA,NA,NA,NA,NA +NJ,34000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8492999999999999,0.01,0,1,NA,NA,NA,NA,NA +NJ,34000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8857,0.01,0,1,NA,NA,NA,NA,NA +NJ,34000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8929,0.01,0,1,NA,NA,NA,NA,NA +NJ,34000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8858,0.01,0,1,NA,NA,NA,NA,NA +NM,35000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15800000000000003,0.01,0,1,NA,NA,NA,NA,NA +NM,35000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1926,0.01,0,1,NA,NA,NA,NA,NA +NM,35000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.272,0.01,0,1,NA,NA,NA,NA,NA +NM,35000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3476,0.01,0,1,NA,NA,NA,NA,NA +NM,35000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4248,0.01,0,1,NA,NA,NA,NA,NA +NM,35000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5001,0.01,0,1,NA,NA,NA,NA,NA +NM,35000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5687,0.01,0,1,NA,NA,NA,NA,NA +NM,35000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6122000000000001,0.01,0,1,NA,NA,NA,NA,NA +NV,32000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.12990000000000002,0.01,0,1,NA,NA,NA,NA,NA +NV,32000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.18010000000000004,0.01,0,1,NA,NA,NA,NA,NA +NV,32000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.29779999999999995,0.01,0,1,NA,NA,NA,NA,NA +NV,32000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4125,0.01,0,1,NA,NA,NA,NA,NA +NV,32000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5044,0.01,0,1,NA,NA,NA,NA,NA +NV,32000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5623,0.01,0,1,NA,NA,NA,NA,NA +NV,32000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.589,0.01,0,1,NA,NA,NA,NA,NA +NV,32000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5823,0.01,0,1,NA,NA,NA,NA,NA +NY,36000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.04279999999999995,0.01,0,1,NA,NA,NA,NA,NA +NY,36000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.06610000000000005,0.01,0,1,NA,NA,NA,NA,NA +NY,36000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1351,0.01,0,1,NA,NA,NA,NA,NA +NY,36000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.24119999999999997,0.01,0,1,NA,NA,NA,NA,NA +NY,36000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3751,0.01,0,1,NA,NA,NA,NA,NA +NY,36000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5085999999999999,0.01,0,1,NA,NA,NA,NA,NA +NY,36000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6121,0.01,0,1,NA,NA,NA,NA,NA +NY,36000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6639999999999999,0.01,0,1,NA,NA,NA,NA,NA +OH,39000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.08620000000000005,0.01,0,1,NA,NA,NA,NA,NA +OH,39000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.14470000000000005,0.01,0,1,NA,NA,NA,NA,NA +OH,39000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.29869999999999997,0.01,0,1,NA,NA,NA,NA,NA +OH,39000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4586,0.01,0,1,NA,NA,NA,NA,NA +OH,39000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5590999999999999,0.01,0,1,NA,NA,NA,NA,NA +OH,39000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5957,0.01,0,1,NA,NA,NA,NA,NA +OH,39000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5951,0.01,0,1,NA,NA,NA,NA,NA +OH,39000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5671999999999999,0.01,0,1,NA,NA,NA,NA,NA +OK,40000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1402,0.01,0,1,NA,NA,NA,NA,NA +OK,40000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.20230000000000004,0.01,0,1,NA,NA,NA,NA,NA +OK,40000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3415,0.01,0,1,NA,NA,NA,NA,NA +OK,40000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4738,0.01,0,1,NA,NA,NA,NA,NA +OK,40000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5677,0.01,0,1,NA,NA,NA,NA,NA +OK,40000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6189,0.01,0,1,NA,NA,NA,NA,NA +OK,40000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6404000000000001,0.01,0,1,NA,NA,NA,NA,NA +OK,40000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6377999999999999,0.01,0,1,NA,NA,NA,NA,NA +OR,41000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.07420000000000004,0.01,0,1,NA,NA,NA,NA,NA +OR,41000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1239,0.01,0,1,NA,NA,NA,NA,NA +OR,41000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.264,0.01,0,1,NA,NA,NA,NA,NA +OR,41000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4300000000000001,0.01,0,1,NA,NA,NA,NA,NA +OR,41000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5528,0.01,0,1,NA,NA,NA,NA,NA +OR,41000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6066,0.01,0,1,NA,NA,NA,NA,NA +OR,41000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.614,0.01,0,1,NA,NA,NA,NA,NA +OR,41000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5958,0.01,0,1,NA,NA,NA,NA,NA +PA,42000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.0504,0.01,0,1,NA,NA,NA,NA,NA +PA,42000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09030000000000005,0.01,0,1,NA,NA,NA,NA,NA +PA,42000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.22009999999999996,0.01,0,1,NA,NA,NA,NA,NA +PA,42000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4051,0.01,0,1,NA,NA,NA,NA,NA +PA,42000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5576,0.01,0,1,NA,NA,NA,NA,NA +PA,42000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6325000000000001,0.01,0,1,NA,NA,NA,NA,NA +PA,42000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6507000000000001,0.01,0,1,NA,NA,NA,NA,NA +PA,42000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6334,0.01,0,1,NA,NA,NA,NA,NA +PR,72000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0,0.01,0,1,NA,NA,NA,NA,NA +PR,72000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0,0.01,0,1,NA,NA,NA,NA,NA +PR,72000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0,0.01,0,1,NA,NA,NA,NA,NA +PR,72000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1734,0.01,0,1,NA,NA,NA,NA,NA +PR,72000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.24370000000000003,0.01,0,1,NA,NA,NA,NA,NA +PR,72000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3246,0.01,0,1,NA,NA,NA,NA,NA +PR,72000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.393,0.01,0,1,NA,NA,NA,NA,NA +PR,72000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4353,0.01,0,1,NA,NA,NA,NA,NA +RI,44000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.09350000000000004,0.01,0,1,NA,NA,NA,NA,NA +RI,44000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.14149999999999996,0.01,0,1,NA,NA,NA,NA,NA +RI,44000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.27270000000000005,0.01,0,1,NA,NA,NA,NA,NA +RI,44000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4267,0.01,0,1,NA,NA,NA,NA,NA +RI,44000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5452,0.01,0,1,NA,NA,NA,NA,NA +RI,44000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6038,0.01,0,1,NA,NA,NA,NA,NA +RI,44000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6202,0.01,0,1,NA,NA,NA,NA,NA +RI,44000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6084,0.01,0,1,NA,NA,NA,NA,NA +SC,45000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1007,0.01,0,1,NA,NA,NA,NA,NA +SC,45000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1643,0.01,0,1,NA,NA,NA,NA,NA +SC,45000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3248,0.01,0,1,NA,NA,NA,NA,NA +SC,45000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4812,0.01,0,1,NA,NA,NA,NA,NA +SC,45000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5772999999999999,0.01,0,1,NA,NA,NA,NA,NA +SC,45000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6152,0.01,0,1,NA,NA,NA,NA,NA +SC,45000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6187,0.01,0,1,NA,NA,NA,NA,NA +SC,45000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.596,0.01,0,1,NA,NA,NA,NA,NA +SD,46000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.13839999999999997,0.01,0,1,NA,NA,NA,NA,NA +SD,46000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.18889999999999996,0.01,0,1,NA,NA,NA,NA,NA +SD,46000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.30700000000000005,0.01,0,1,NA,NA,NA,NA,NA +SD,46000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4234,0.01,0,1,NA,NA,NA,NA,NA +SD,46000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5195000000000001,0.01,0,1,NA,NA,NA,NA,NA +SD,46000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5808,0.01,0,1,NA,NA,NA,NA,NA +SD,46000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6069,0.01,0,1,NA,NA,NA,NA,NA +SD,46000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5998,0.01,0,1,NA,NA,NA,NA,NA +TN,47000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.07989999999999997,0.01,0,1,NA,NA,NA,NA,NA +TN,47000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.136,0.01,0,1,NA,NA,NA,NA,NA +TN,47000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2845,0.01,0,1,NA,NA,NA,NA,NA +TN,47000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4399,0.01,0,1,NA,NA,NA,NA,NA +TN,47000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5391,0.01,0,1,NA,NA,NA,NA,NA +TN,47000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5763,0.01,0,1,NA,NA,NA,NA,NA +TN,47000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5761000000000001,0.01,0,1,NA,NA,NA,NA,NA +TN,47000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5488999999999999,0.01,0,1,NA,NA,NA,NA,NA +TX,48000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.07069999999999999,0.01,0,1,NA,NA,NA,NA,NA +TX,48000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11929999999999996,0.01,0,1,NA,NA,NA,NA,NA +TX,48000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2563,0.01,0,1,NA,NA,NA,NA,NA +TX,48000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4184,0.01,0,1,NA,NA,NA,NA,NA +TX,48000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5373,0.01,0,1,NA,NA,NA,NA,NA +TX,48000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5903,0.01,0,1,NA,NA,NA,NA,NA +TX,48000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5983,0.01,0,1,NA,NA,NA,NA,NA +TX,48000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5737,0.01,0,1,NA,NA,NA,NA,NA +UT,49000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1603,0.01,0,1,NA,NA,NA,NA,NA +UT,49000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.23409999999999995,0.01,0,1,NA,NA,NA,NA,NA +UT,49000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4054,0.01,0,1,NA,NA,NA,NA,NA +UT,49000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5633,0.01,0,1,NA,NA,NA,NA,NA +UT,49000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6803,0.01,0,1,NA,NA,NA,NA,NA +UT,49000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7534,0.01,0,1,NA,NA,NA,NA,NA +UT,49000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7885,0.01,0,1,NA,NA,NA,NA,NA +UT,49000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7911,0.01,0,1,NA,NA,NA,NA,NA +VA,51000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.12139999999999997,0.01,0,1,NA,NA,NA,NA,NA +VA,51000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.16169999999999995,0.01,0,1,NA,NA,NA,NA,NA +VA,51000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.26070000000000004,0.01,0,1,NA,NA,NA,NA,NA +VA,51000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3712,0.01,0,1,NA,NA,NA,NA,NA +VA,51000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.482,0.01,0,1,NA,NA,NA,NA,NA +VA,51000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5764,0.01,0,1,NA,NA,NA,NA,NA +VA,51000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6455,0.01,0,1,NA,NA,NA,NA,NA +VA,51000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6806,0.01,0,1,NA,NA,NA,NA,NA +VI,78000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.15380000000000005,0.01,0,1,NA,NA,NA,NA,NA +VI,78000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.2108,0.01,0,1,NA,NA,NA,NA,NA +VI,78000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3447,0.01,0,1,NA,NA,NA,NA,NA +VI,78000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4716,0.01,0,1,NA,NA,NA,NA,NA +VI,78000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5629,0.01,0,1,NA,NA,NA,NA,NA +VI,78000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6121,0.01,0,1,NA,NA,NA,NA,NA +VI,78000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6285000000000001,0.01,0,1,NA,NA,NA,NA,NA +VI,78000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6135999999999999,0.01,0,1,NA,NA,NA,NA,NA +VT,50000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.1623,0.01,0,1,NA,NA,NA,NA,NA +VT,50000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.235,0.01,0,1,NA,NA,NA,NA,NA +VT,50000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4272,0.01,0,1,NA,NA,NA,NA,NA +VT,50000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6154,0.01,0,1,NA,NA,NA,NA,NA +VT,50000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7498,0.01,0,1,NA,NA,NA,NA,NA +VT,50000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8316,0.01,0,1,NA,NA,NA,NA,NA +VT,50000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8601,0.01,0,1,NA,NA,NA,NA,NA +VT,50000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.8618,0.01,0,1,NA,NA,NA,NA,NA +WA,53000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.11240000000000006,0.01,0,1,NA,NA,NA,NA,NA +WA,53000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.17110000000000003,0.01,0,1,NA,NA,NA,NA,NA +WA,53000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3206,0.01,0,1,NA,NA,NA,NA,NA +WA,53000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4817,0.01,0,1,NA,NA,NA,NA,NA +WA,53000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6066,0.01,0,1,NA,NA,NA,NA,NA +WA,53000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6784,0.01,0,1,NA,NA,NA,NA,NA +WA,53000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7088,0.01,0,1,NA,NA,NA,NA,NA +WA,53000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.7070000000000001,0.01,0,1,NA,NA,NA,NA,NA +WI,55000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.07730000000000004,0.01,0,1,NA,NA,NA,NA,NA +WI,55000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.133,0.01,0,1,NA,NA,NA,NA,NA +WI,55000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.28759999999999997,0.01,0,1,NA,NA,NA,NA,NA +WI,55000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.46419999999999995,0.01,0,1,NA,NA,NA,NA,NA +WI,55000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.5921000000000001,0.01,0,1,NA,NA,NA,NA,NA +WI,55000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6541,0.01,0,1,NA,NA,NA,NA,NA +WI,55000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6707000000000001,0.01,0,1,NA,NA,NA,NA,NA +WI,55000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.6529,0.01,0,1,NA,NA,NA,NA,NA +WV,54000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.18610000000000004,0.01,0,1,NA,NA,NA,NA,NA +WV,54000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.21809999999999996,0.01,0,1,NA,NA,NA,NA,NA +WV,54000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.28690000000000004,0.01,0,1,NA,NA,NA,NA,NA +WV,54000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3379,0.01,0,1,NA,NA,NA,NA,NA +WV,54000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3786000000000001,0.01,0,1,NA,NA,NA,NA,NA +WV,54000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4086,0.01,0,1,NA,NA,NA,NA,NA +WV,54000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4332,0.01,0,1,NA,NA,NA,NA,NA +WV,54000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4416,0.01,0,1,NA,NA,NA,NA,NA +WY,56000,2021-01-01,2021-01-31,incidD_vaccadj_jan2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.00860000000000005,0.01,0,1,NA,NA,NA,NA,NA +WY,56000,2021-02-01,2021-02-28,incidD_vaccadj_feb2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.01429999999999998,0.01,0,1,NA,NA,NA,NA,NA +WY,56000,2021-03-01,2021-03-31,incidD_vaccadj_mar2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.03420000000000001,0.01,0,1,NA,NA,NA,NA,NA +WY,56000,2021-04-01,2021-04-30,incidD_vaccadj_apr2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.07509999999999994,0.01,0,1,NA,NA,NA,NA,NA +WY,56000,2021-05-01,2021-05-31,incidD_vaccadj_may2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.14790000000000003,0.01,0,1,NA,NA,NA,NA,NA +WY,56000,2021-06-01,2021-06-30,incidD_vaccadj_jun2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.25229999999999997,0.01,0,1,NA,NA,NA,NA,NA +WY,56000,2021-07-01,2021-07-31,incidD_vaccadj_jul2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.3671,0.01,0,1,NA,NA,NA,NA,NA +WY,56000,2021-08-01,2021-08-07,incidD_vaccadj_aug2021,SinglePeriodModifier,outcome,vacc_outcome,incidD::probability,NA,truncnorm,0.4439999999999999,0.01,0,1,NA,NA,NA,NA,NA diff --git a/flepimop/R_packages/config.writer/tests/testthat/sample_config.yml b/flepimop/R_packages/config.writer/tests/testthat/sample_config.yml index 59233c1db..5cc7782d3 100644 --- a/flepimop/R_packages/config.writer/tests/testthat/sample_config.yml +++ b/flepimop/R_packages/config.writer/tests/testthat/sample_config.yml @@ -67,8 +67,6 @@ spatial_setup: geodata: geodata_territories_2019_statelevel.csv mobility: mobility_territories_2011-2015_statelevel.csv - popnodes: pop2019est - nodenames: geoid include_in_report: include_in_report state_level: TRUE @@ -131,9 +129,9 @@ interventions: - inference settings: local_variance: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2020-01-01 period_end_date: 2021-08-07 value: @@ -149,22 +147,22 @@ interventions: a: -1 b: 1 lockdown: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: ["01000"] + - subpop: ["01000"] periods: - start_date: 2020-04-04 end_date: 2020-04-30 - - affected_geoids: ["02000"] + - subpop: ["02000"] periods: - start_date: 2020-03-28 end_date: 2020-04-23 - - affected_geoids: ["04000"] + - subpop: ["04000"] periods: - start_date: 2020-03-31 end_date: 2020-05-15 - - affected_geoids: ["06000"] + - subpop: ["06000"] periods: - start_date: 2020-03-19 end_date: 2020-05-07 @@ -172,185 +170,185 @@ interventions: end_date: 2021-01-11 - start_date: 2021-01-12 end_date: 2021-01-24 - - affected_geoids: ["08000"] + - subpop: ["08000"] periods: - start_date: 2020-03-26 end_date: 2020-04-26 - start_date: 2020-11-20 end_date: 2021-01-03 - - affected_geoids: ["09000"] + - subpop: ["09000"] periods: - start_date: 2020-03-23 end_date: 2020-05-20 - - affected_geoids: ["10000"] + - subpop: ["10000"] periods: - start_date: 2020-03-24 end_date: 2020-05-31 - - affected_geoids: ["11000"] + - subpop: ["11000"] periods: - start_date: 2020-04-01 end_date: 2020-05-29 - - affected_geoids: ["12000"] + - subpop: ["12000"] periods: - start_date: 2020-04-03 end_date: 2020-05-04 - - affected_geoids: ["13000"] + - subpop: ["13000"] periods: - start_date: 2020-04-03 end_date: 2020-04-27 - - affected_geoids: ["15000"] + - subpop: ["15000"] periods: - start_date: 2020-03-25 end_date: 2020-05-06 - - affected_geoids: ["16000"] + - subpop: ["16000"] periods: - start_date: 2020-03-25 end_date: 2020-04-30 - - affected_geoids: ["17000"] + - subpop: ["17000"] periods: - start_date: 2020-03-21 end_date: 2020-05-29 - - affected_geoids: ["18000"] + - subpop: ["18000"] periods: - start_date: 2020-03-24 end_date: 2020-05-03 - - affected_geoids: ["20000"] + - subpop: ["20000"] periods: - start_date: 2020-03-30 end_date: 2020-05-04 - - affected_geoids: ["21000"] + - subpop: ["21000"] periods: - start_date: 2020-03-26 end_date: 2020-05-10 - - affected_geoids: ["22000"] + - subpop: ["22000"] periods: - start_date: 2020-03-23 end_date: 2020-05-14 - - affected_geoids: ["23000"] + - subpop: ["23000"] periods: - start_date: 2020-04-02 end_date: 2020-04-30 - - affected_geoids: ["24000"] + - subpop: ["24000"] periods: - start_date: 2020-03-30 end_date: 2020-05-14 - - affected_geoids: ["25000"] + - subpop: ["25000"] periods: - start_date: 2020-03-24 end_date: 2020-05-18 - - affected_geoids: ["26000"] + - subpop: ["26000"] periods: - start_date: 2020-03-24 end_date: 2020-05-31 - - affected_geoids: ["27000"] + - subpop: ["27000"] periods: - start_date: 2020-03-27 end_date: 2020-05-17 - - affected_geoids: ["28000"] + - subpop: ["28000"] periods: - start_date: 2020-04-03 end_date: 2020-04-27 - - affected_geoids: ["29000"] + - subpop: ["29000"] periods: - start_date: 2020-04-06 end_date: 2020-05-03 - - affected_geoids: ["30000"] + - subpop: ["30000"] periods: - start_date: 2020-03-28 end_date: 2020-04-26 - - affected_geoids: ["32000"] + - subpop: ["32000"] periods: - start_date: 2020-04-01 end_date: 2020-05-08 - - affected_geoids: ["33000"] + - subpop: ["33000"] periods: - start_date: 2020-03-27 end_date: 2020-05-10 - - affected_geoids: ["34000"] + - subpop: ["34000"] periods: - start_date: 2020-03-21 end_date: 2020-05-18 - - affected_geoids: ["35000"] + - subpop: ["35000"] periods: - start_date: 2020-03-24 end_date: 2020-05-31 - start_date: 2020-11-16 end_date: 2020-12-01 - - affected_geoids: ["36000"] + - subpop: ["36000"] periods: - start_date: 2020-03-22 end_date: 2020-06-07 - - affected_geoids: ["37000"] + - subpop: ["37000"] periods: - start_date: 2020-03-30 end_date: 2020-05-07 - - affected_geoids: ["39000"] + - subpop: ["39000"] periods: - start_date: 2020-03-23 end_date: 2020-05-03 - - affected_geoids: ["41000"] + - subpop: ["41000"] periods: - start_date: 2020-03-23 end_date: 2020-05-14 - - affected_geoids: ["42000"] + - subpop: ["42000"] periods: - start_date: 2020-03-28 end_date: 2020-05-07 - - affected_geoids: ["44000"] + - subpop: ["44000"] periods: - start_date: 2020-03-28 end_date: 2020-05-08 - - affected_geoids: ["45000"] + - subpop: ["45000"] periods: - start_date: 2020-04-07 end_date: 2020-04-20 - - affected_geoids: ["47000"] + - subpop: ["47000"] periods: - start_date: 2020-04-02 end_date: 2020-04-30 - - affected_geoids: ["48000"] + - subpop: ["48000"] periods: - start_date: 2020-03-31 end_date: 2020-04-30 - - affected_geoids: ["49000"] + - subpop: ["49000"] periods: - start_date: 2020-03-27 end_date: 2020-05-01 - - affected_geoids: ["50000"] + - subpop: ["50000"] periods: - start_date: 2020-03-25 end_date: 2020-05-15 - - affected_geoids: ["51000"] + - subpop: ["51000"] periods: - start_date: 2020-03-30 end_date: 2020-05-14 - - affected_geoids: ["53000"] + - subpop: ["53000"] periods: - start_date: 2020-03-23 end_date: 2020-05-04 - - affected_geoids: ["54000"] + - subpop: ["54000"] periods: - start_date: 2020-03-24 end_date: 2020-05-03 - - affected_geoids: ["55000"] + - subpop: ["55000"] periods: - start_date: 2020-03-25 end_date: 2020-05-13 - - affected_geoids: ["66000"] + - subpop: ["66000"] periods: - start_date: 2020-03-20 end_date: 2020-05-10 - start_date: 2020-08-16 end_date: 2020-09-24 - - affected_geoids: ["69000"] + - subpop: ["69000"] periods: - start_date: 2020-03-30 end_date: 2020-05-02 - - affected_geoids: ["72000"] + - subpop: ["72000"] periods: - start_date: 2020-03-30 end_date: 2020-05-24 - - affected_geoids: ["78000"] + - subpop: ["78000"] periods: - start_date: 2020-03-25 end_date: 2020-05-03 @@ -369,26 +367,26 @@ interventions: a: -1 b: 1 open_p1: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: ["01000"] + - subpop: ["01000"] periods: - start_date: 2020-05-01 end_date: 2020-05-21 - - affected_geoids: ["02000"] + - subpop: ["02000"] periods: - start_date: 2020-04-24 end_date: 2020-05-07 - - affected_geoids: ["04000"] + - subpop: ["04000"] periods: - start_date: 2020-06-29 end_date: 2020-10-01 - - affected_geoids: ["05000"] + - subpop: ["05000"] periods: - start_date: 2020-05-04 end_date: 2020-06-14 - - affected_geoids: ["06000"] + - subpop: ["06000"] periods: - start_date: 2020-07-06 end_date: 2020-11-20 @@ -396,7 +394,7 @@ interventions: end_date: 2020-12-05 - start_date: 2021-01-25 end_date: 2021-02-26 - - affected_geoids: ["08000"] + - subpop: ["08000"] periods: - start_date: 2020-07-01 end_date: 2020-09-28 @@ -404,11 +402,11 @@ interventions: end_date: 2020-11-19 - start_date: 2021-01-04 end_date: 2021-02-05 - - affected_geoids: ["09000"] + - subpop: ["09000"] periods: - start_date: 2020-05-21 end_date: 2020-06-16 - - affected_geoids: ["10000"] + - subpop: ["10000"] periods: - start_date: 2020-06-01 end_date: 2020-06-14 @@ -418,23 +416,23 @@ interventions: end_date: 2021-01-07 - start_date: 2021-01-08 end_date: 2021-02-11 - - affected_geoids: ["11000"] + - subpop: ["11000"] periods: - start_date: 2020-05-30 end_date: 2020-06-21 - start_date: 2020-12-23 end_date: 2021-01-21 - - affected_geoids: ["12000"] + - subpop: ["12000"] periods: - start_date: 2020-05-05 end_date: 2020-06-04 - start_date: 2020-06-26 end_date: 2020-09-13 - - affected_geoids: ["13000"] + - subpop: ["13000"] periods: - start_date: 2020-04-28 end_date: 2020-05-31 - - affected_geoids: ["15000"] + - subpop: ["15000"] periods: - start_date: 2020-05-07 end_date: 2020-05-31 @@ -442,109 +440,109 @@ interventions: end_date: 2020-09-23 - start_date: 2020-10-27 end_date: 2020-11-10 - - affected_geoids: ["16000"] + - subpop: ["16000"] periods: - start_date: 2020-05-01 end_date: 2020-05-15 - - affected_geoids: ["18000"] + - subpop: ["18000"] periods: - start_date: 2020-05-04 end_date: 2020-05-21 - start_date: 2021-01-11 end_date: 2021-01-31 - - affected_geoids: ["19000"] + - subpop: ["19000"] periods: - start_date: 2020-05-15 end_date: 2020-05-27 - - affected_geoids: ["20000"] + - subpop: ["20000"] periods: - start_date: 2020-05-05 end_date: 2020-05-21 - - affected_geoids: ["21000"] + - subpop: ["21000"] periods: - start_date: 2020-05-11 end_date: 2020-05-21 - - affected_geoids: ["22000"] + - subpop: ["22000"] periods: - start_date: 2020-05-15 end_date: 2020-06-04 - - affected_geoids: ["23000"] + - subpop: ["23000"] periods: - start_date: 2020-05-01 end_date: 2020-05-31 - - affected_geoids: ["24000"] + - subpop: ["24000"] periods: - start_date: 2020-05-15 end_date: 2020-06-04 - - affected_geoids: ["25000"] + - subpop: ["25000"] periods: - start_date: 2020-05-19 end_date: 2020-06-07 - - affected_geoids: ["26000"] + - subpop: ["26000"] periods: - start_date: 2020-07-01 end_date: 2020-09-08 - start_date: 2020-11-18 end_date: 2020-12-20 - - affected_geoids: ["27000"] + - subpop: ["27000"] periods: - start_date: 2020-05-18 end_date: 2020-05-31 - start_date: 2020-11-13 end_date: 2020-12-17 - - affected_geoids: ["28000"] + - subpop: ["28000"] periods: - start_date: 2020-04-28 end_date: 2020-05-06 - - affected_geoids: ["30000"] + - subpop: ["30000"] periods: - start_date: 2020-04-27 end_date: 2020-05-31 - - affected_geoids: ["31000"] + - subpop: ["31000"] periods: - start_date: 2020-05-04 end_date: 2020-05-31 - - affected_geoids: ["32000"] + - subpop: ["32000"] periods: - start_date: 2020-05-09 end_date: 2020-05-28 - - affected_geoids: ["33000"] + - subpop: ["33000"] periods: - start_date: 2020-05-11 end_date: 2020-06-14 - - affected_geoids: ["34000"] + - subpop: ["34000"] periods: - start_date: 2020-05-19 end_date: 2020-06-14 - - affected_geoids: ["35000"] + - subpop: ["35000"] periods: - start_date: 2020-07-13 end_date: 2020-08-28 - start_date: 2020-12-02 end_date: 2021-02-09 - - affected_geoids: ["36000"] + - subpop: ["36000"] periods: - start_date: 2020-06-08 end_date: 2020-06-21 - start_date: 2020-06-22 end_date: 2020-07-05 - - affected_geoids: ["37000"] + - subpop: ["37000"] periods: - start_date: 2020-05-08 end_date: 2020-05-21 - - affected_geoids: ["38000"] + - subpop: ["38000"] periods: - start_date: 2020-05-01 end_date: 2020-05-28 - - affected_geoids: ["39000"] + - subpop: ["39000"] periods: - start_date: 2020-05-04 end_date: 2020-05-20 - - affected_geoids: ["40000"] + - subpop: ["40000"] periods: - start_date: 2020-04-24 end_date: 2020-05-14 - - affected_geoids: ["41000"] + - subpop: ["41000"] periods: - start_date: 2020-05-15 end_date: 2020-06-04 @@ -552,65 +550,65 @@ interventions: end_date: 2020-12-02 - start_date: 2020-12-03 end_date: 2021-02-11 - - affected_geoids: ["42000"] + - subpop: ["42000"] periods: - start_date: 2020-05-08 end_date: 2020-05-28 - start_date: 2020-12-12 end_date: 2021-01-03 - - affected_geoids: ["44000"] + - subpop: ["44000"] periods: - start_date: 2020-05-09 end_date: 2020-05-31 - start_date: 2020-11-30 end_date: 2020-12-20 - - affected_geoids: ["45000"] + - subpop: ["45000"] periods: - start_date: 2020-04-21 end_date: 2020-05-10 - - affected_geoids: ["47000"] + - subpop: ["47000"] periods: - start_date: 2020-05-01 end_date: 2020-05-24 - - affected_geoids: ["48000"] + - subpop: ["48000"] periods: - start_date: 2020-05-01 end_date: 2020-05-17 - start_date: 2020-06-26 end_date: 2020-09-20 - - affected_geoids: ["49000"] + - subpop: ["49000"] periods: - start_date: 2020-05-02 end_date: 2020-05-15 - - affected_geoids: ["50000"] + - subpop: ["50000"] periods: - start_date: 2020-05-16 end_date: 2020-05-31 - - affected_geoids: ["51000"] + - subpop: ["51000"] periods: - start_date: 2020-05-15 end_date: 2020-06-04 - - affected_geoids: ["53000"] + - subpop: ["53000"] periods: - start_date: 2020-05-05 end_date: 2020-05-28 - start_date: 2020-11-16 end_date: 2021-01-10 - - affected_geoids: ["54000"] + - subpop: ["54000"] periods: - start_date: 2020-05-04 end_date: 2020-05-20 - - affected_geoids: ["55000"] + - subpop: ["55000"] periods: - start_date: 2020-05-14 end_date: 2020-06-12 - start_date: 2020-10-29 end_date: 2021-01-12 - - affected_geoids: ["56000"] + - subpop: ["56000"] periods: - start_date: 2020-05-01 end_date: 2020-05-14 - - affected_geoids: ["66000"] + - subpop: ["66000"] periods: - start_date: 2020-05-11 end_date: 2020-07-19 @@ -620,17 +618,17 @@ interventions: end_date: 2020-12-25 - start_date: 2020-12-26 end_date: 2021-01-17 - - affected_geoids: ["69000"] + - subpop: ["69000"] periods: - start_date: 2020-05-03 end_date: 2020-05-24 - - affected_geoids: ["72000"] + - subpop: ["72000"] periods: - start_date: 2020-07-16 end_date: 2020-09-11 - start_date: 2020-12-07 end_date: 2021-01-07 - - affected_geoids: ["78000"] + - subpop: ["78000"] periods: - start_date: 2020-05-04 end_date: 2020-05-31 @@ -647,20 +645,20 @@ interventions: a: -1 b: 1 open_p2: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: ["01000"] + - subpop: ["01000"] periods: - start_date: 2020-05-22 end_date: 2020-07-15 - start_date: 2020-07-16 end_date: 2021-03-03 - - affected_geoids: ["02000"] + - subpop: ["02000"] periods: - start_date: 2020-05-08 end_date: 2020-05-21 - - affected_geoids: ["04000"] + - subpop: ["04000"] periods: - start_date: 2020-05-16 end_date: 2020-06-28 @@ -668,7 +666,7 @@ interventions: end_date: 2020-12-02 - start_date: 2020-12-03 end_date: 2021-03-04 - - affected_geoids: ["05000"] + - subpop: ["05000"] periods: - start_date: 2020-06-15 end_date: 2020-07-19 @@ -678,7 +676,7 @@ interventions: end_date: 2021-01-01 - start_date: 2021-01-02 end_date: 2021-02-25 - - affected_geoids: ["06000"] + - subpop: ["06000"] periods: - start_date: 2020-05-08 end_date: 2020-06-11 @@ -686,13 +684,13 @@ interventions: end_date: 2020-07-05 - start_date: 2021-02-27 end_date: 2021-04-06 - - affected_geoids: ["08000"] + - subpop: ["08000"] periods: - start_date: 2020-04-27 end_date: 2020-06-30 - start_date: 2020-09-29 end_date: 2020-11-04 - - affected_geoids: ["09000"] + - subpop: ["09000"] periods: - start_date: 2020-06-17 end_date: 2020-10-07 @@ -700,7 +698,7 @@ interventions: end_date: 2021-01-18 - start_date: 2021-01-19 end_date: 2021-03-18 - - affected_geoids: ["10000"] + - subpop: ["10000"] periods: - start_date: 2020-06-15 end_date: 2020-11-22 @@ -710,7 +708,7 @@ interventions: end_date: 2021-03-31 - start_date: 2021-04-01 end_date: 2021-05-20 - - affected_geoids: ["11000"] + - subpop: ["11000"] periods: - start_date: 2020-06-22 end_date: 2020-11-24 @@ -722,17 +720,17 @@ interventions: end_date: 2021-03-21 - start_date: 2021-03-22 end_date: 2021-04-30 - - affected_geoids: ["12000"] + - subpop: ["12000"] periods: - start_date: 2020-06-05 end_date: 2020-06-25 - start_date: 2020-09-14 end_date: 2020-09-24 - - affected_geoids: ["13000"] + - subpop: ["13000"] periods: - start_date: 2020-06-01 end_date: 2020-06-30 - - affected_geoids: ["15000"] + - subpop: ["15000"] periods: - start_date: 2020-06-01 end_date: 2020-08-07 @@ -742,7 +740,7 @@ interventions: end_date: 2021-01-18 - start_date: 2021-01-19 end_date: 2021-02-24 - - affected_geoids: ["16000"] + - subpop: ["16000"] periods: - start_date: 2020-05-16 end_date: 2020-05-29 @@ -750,13 +748,13 @@ interventions: end_date: 2020-12-29 - start_date: 2020-12-30 end_date: 2021-02-01 - - affected_geoids: ["17000"] + - subpop: ["17000"] periods: - start_date: 2020-10-30 end_date: 2020-11-19 - start_date: 2020-11-20 end_date: 2021-01-17 - - affected_geoids: ["18000"] + - subpop: ["18000"] periods: - start_date: 2020-05-22 end_date: 2020-06-11 @@ -764,17 +762,17 @@ interventions: end_date: 2021-01-10 - start_date: 2021-02-01 end_date: 2021-02-14 - - affected_geoids: ["19000"] + - subpop: ["19000"] periods: - start_date: 2020-05-28 end_date: 2020-06-11 - start_date: 2020-08-27 end_date: 2020-10-03 - - affected_geoids: ["20000"] + - subpop: ["20000"] periods: - start_date: 2020-05-22 end_date: 2020-06-07 - - affected_geoids: ["21000"] + - subpop: ["21000"] periods: - start_date: 2020-05-22 end_date: 2020-06-28 @@ -782,7 +780,7 @@ interventions: end_date: 2020-08-10 - start_date: 2020-11-20 end_date: 2020-12-13 - - affected_geoids: ["22000"] + - subpop: ["22000"] periods: - start_date: 2020-06-05 end_date: 2020-07-12 @@ -790,11 +788,11 @@ interventions: end_date: 2020-09-10 - start_date: 2020-11-25 end_date: 2021-03-02 - - affected_geoids: ["23000"] + - subpop: ["23000"] periods: - start_date: 2020-06-01 end_date: 2020-06-30 - - affected_geoids: ["24000"] + - subpop: ["24000"] periods: - start_date: 2020-06-05 end_date: 2020-09-03 @@ -804,7 +802,7 @@ interventions: end_date: 2021-01-31 - start_date: 2021-02-01 end_date: 2021-03-11 - - affected_geoids: ["25000"] + - subpop: ["25000"] periods: - start_date: 2020-06-08 end_date: 2020-07-05 @@ -812,7 +810,7 @@ interventions: end_date: 2021-01-24 - start_date: 2021-01-25 end_date: 2021-02-07 - - affected_geoids: ["26000"] + - subpop: ["26000"] periods: - start_date: 2020-06-01 end_date: 2020-06-30 @@ -826,23 +824,23 @@ interventions: end_date: 2021-01-31 - start_date: 2021-02-01 end_date: 2021-03-04 - - affected_geoids: ["27000"] + - subpop: ["27000"] periods: - start_date: 2020-06-01 end_date: 2020-06-09 - start_date: 2020-12-18 end_date: 2021-01-10 - - affected_geoids: ["28000"] + - subpop: ["28000"] periods: - start_date: 2020-05-07 end_date: 2020-05-31 - - affected_geoids: ["30000"] + - subpop: ["30000"] periods: - start_date: 2020-06-01 end_date: 2020-11-19 - start_date: 2020-11-20 end_date: 2021-01-14 - - affected_geoids: ["31000"] + - subpop: ["31000"] periods: - start_date: 2020-06-01 end_date: 2020-06-21 @@ -852,17 +850,17 @@ interventions: end_date: 2020-12-11 - start_date: 2020-12-12 end_date: 2020-12-23 - - affected_geoids: ["32000"] + - subpop: ["32000"] periods: - start_date: 2020-07-10 end_date: 2020-09-19 - start_date: 2020-11-24 end_date: 2021-02-14 - - affected_geoids: ["33000"] + - subpop: ["33000"] periods: - start_date: 2020-06-15 end_date: 2020-06-28 - - affected_geoids: ["34000"] + - subpop: ["34000"] periods: - start_date: 2020-06-15 end_date: 2020-09-03 @@ -872,7 +870,7 @@ interventions: end_date: 2021-01-01 - start_date: 2021-01-02 end_date: 2021-02-04 - - affected_geoids: ["35000"] + - subpop: ["35000"] periods: - start_date: 2020-06-01 end_date: 2020-07-12 @@ -882,7 +880,7 @@ interventions: end_date: 2020-11-15 - start_date: 2021-02-10 end_date: 2021-02-23 - - affected_geoids: ["36000"] + - subpop: ["36000"] periods: - start_date: 2020-07-06 end_date: 2020-07-19 @@ -892,7 +890,7 @@ interventions: end_date: 2020-12-13 - start_date: 2020-12-14 end_date: 2021-01-26 - - affected_geoids: ["37000"] + - subpop: ["37000"] periods: - start_date: 2020-05-22 end_date: 2020-09-03 @@ -900,7 +898,7 @@ interventions: end_date: 2020-10-01 - start_date: 2020-12-11 end_date: 2021-02-25 - - affected_geoids: ["38000"] + - subpop: ["38000"] periods: - start_date: 2020-10-16 end_date: 2020-11-15 @@ -910,13 +908,13 @@ interventions: end_date: 2021-01-07 - start_date: 2021-01-08 end_date: 2021-01-17 - - affected_geoids: ["39000"] + - subpop: ["39000"] periods: - start_date: 2020-05-21 end_date: 2020-06-18 - start_date: 2020-11-19 end_date: 2021-02-10 - - affected_geoids: ["40000"] + - subpop: ["40000"] periods: - start_date: 2020-05-15 end_date: 2020-05-31 @@ -924,7 +922,7 @@ interventions: end_date: 2021-01-13 - start_date: 2021-01-14 end_date: 2021-03-11 - - affected_geoids: ["41000"] + - subpop: ["41000"] periods: - start_date: 2020-06-05 end_date: 2020-06-30 @@ -936,13 +934,13 @@ interventions: end_date: 2021-02-25 - start_date: 2021-04-30 end_date: 2021-06-08 - - affected_geoids: ["42000"] + - subpop: ["42000"] periods: - start_date: 2020-05-29 end_date: 2020-07-15 - start_date: 2020-07-16 end_date: 2020-09-13 - - affected_geoids: ["44000"] + - subpop: ["44000"] periods: - start_date: 2020-06-01 end_date: 2020-06-29 @@ -952,11 +950,11 @@ interventions: end_date: 2021-01-19 - start_date: 2021-01-20 end_date: 2021-02-11 - - affected_geoids: ["45000"] + - subpop: ["45000"] periods: - start_date: 2020-05-11 end_date: 2020-08-02 - - affected_geoids: ["47000"] + - subpop: ["47000"] periods: - start_date: 2020-05-25 end_date: 2020-09-28 @@ -964,7 +962,7 @@ interventions: end_date: 2020-12-19 - start_date: 2020-12-20 end_date: 2021-01-19 - - affected_geoids: ["48000"] + - subpop: ["48000"] periods: - start_date: 2020-05-18 end_date: 2020-06-02 @@ -972,13 +970,13 @@ interventions: end_date: 2020-06-25 - start_date: 2020-09-21 end_date: 2020-10-13 - - affected_geoids: ["49000"] + - subpop: ["49000"] periods: - start_date: 2020-05-16 end_date: 2020-06-18 - start_date: 2020-11-09 end_date: 2020-11-23 - - affected_geoids: ["50000"] + - subpop: ["50000"] periods: - start_date: 2020-06-01 end_date: 2020-06-25 @@ -986,7 +984,7 @@ interventions: end_date: 2021-02-11 - start_date: 2021-02-12 end_date: 2021-03-23 - - affected_geoids: ["51000"] + - subpop: ["51000"] periods: - start_date: 2020-06-05 end_date: 2020-06-30 @@ -994,7 +992,7 @@ interventions: end_date: 2020-09-09 - start_date: 2020-12-14 end_date: 2021-02-28 - - affected_geoids: ["53000"] + - subpop: ["53000"] periods: - start_date: 2020-05-29 end_date: 2020-07-01 @@ -1004,19 +1002,19 @@ interventions: end_date: 2020-11-15 - start_date: 2021-01-11 end_date: 2021-01-31 - - affected_geoids: ["54000"] + - subpop: ["54000"] periods: - start_date: 2020-05-21 end_date: 2020-06-04 - start_date: 2020-07-14 end_date: 2020-10-12 - - affected_geoids: ["55000"] + - subpop: ["55000"] periods: - start_date: 2020-06-13 end_date: 2020-07-31 - start_date: 2020-08-01 end_date: 2020-10-28 - - affected_geoids: ["56000"] + - subpop: ["56000"] periods: - start_date: 2020-05-15 end_date: 2020-06-14 @@ -1024,19 +1022,19 @@ interventions: end_date: 2021-01-08 - start_date: 2021-01-09 end_date: 2021-01-25 - - affected_geoids: ["66000"] + - subpop: ["66000"] periods: - start_date: 2020-07-20 end_date: 2020-08-15 - start_date: 2021-01-18 end_date: 2021-02-21 - - affected_geoids: ["69000"] + - subpop: ["69000"] periods: - start_date: 2020-05-25 end_date: 2020-06-15 - start_date: 2020-08-24 end_date: 2020-09-06 - - affected_geoids: ["72000"] + - subpop: ["72000"] periods: - start_date: 2020-05-25 end_date: 2020-06-15 @@ -1044,7 +1042,7 @@ interventions: end_date: 2020-12-06 - start_date: 2021-04-17 end_date: 2021-05-23 - - affected_geoids: ["78000"] + - subpop: ["78000"] periods: - start_date: 2020-06-01 end_date: 2020-08-16 @@ -1067,54 +1065,54 @@ interventions: a: -1 b: 1 open_p3: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: ["01000"] + - subpop: ["01000"] periods: - start_date: 2021-03-04 end_date: 2021-04-08 - - affected_geoids: ["02000"] + - subpop: ["02000"] periods: - start_date: 2020-11-16 end_date: 2021-02-14 - - affected_geoids: ["04000"] + - subpop: ["04000"] periods: - start_date: 2021-03-05 end_date: 2021-03-24 - - affected_geoids: ["05000"] + - subpop: ["05000"] periods: - start_date: 2021-02-26 end_date: 2021-03-30 - - affected_geoids: ["06000"] + - subpop: ["06000"] periods: - start_date: 2021-04-07 end_date: 2021-06-14 - - affected_geoids: ["08000"] + - subpop: ["08000"] periods: - start_date: 2021-02-06 end_date: 2021-03-14 - start_date: 2021-03-15 end_date: 2021-03-23 - - affected_geoids: ["09000"] + - subpop: ["09000"] periods: - start_date: 2020-10-08 end_date: 2020-11-05 - - affected_geoids: ["10000"] + - subpop: ["10000"] periods: - start_date: 2021-05-21 end_date: 2021-08-07 - - affected_geoids: ["11000"] + - subpop: ["11000"] periods: - start_date: 2021-05-01 end_date: 2021-05-16 - - affected_geoids: ["12000"] + - subpop: ["12000"] periods: - start_date: 2020-09-25 end_date: 2021-05-02 - start_date: 2021-05-03 end_date: 2021-08-07 - - affected_geoids: ["13000"] + - subpop: ["13000"] periods: - start_date: 2020-07-01 end_date: 2020-09-10 @@ -1122,7 +1120,7 @@ interventions: end_date: 2020-12-14 - start_date: 2020-12-15 end_date: 2021-04-07 - - affected_geoids: ["15000"] + - subpop: ["15000"] periods: - start_date: 2021-02-25 end_date: 2021-03-10 @@ -1132,7 +1130,7 @@ interventions: end_date: 2021-05-24 - start_date: 2021-05-25 end_date: 2021-06-10 - - affected_geoids: ["16000"] + - subpop: ["16000"] periods: - start_date: 2020-05-30 end_date: 2020-06-12 @@ -1140,7 +1138,7 @@ interventions: end_date: 2020-11-12 - start_date: 2021-02-02 end_date: 2021-05-10 - - affected_geoids: ["17000"] + - subpop: ["17000"] periods: - start_date: 2020-05-30 end_date: 2020-06-25 @@ -1150,13 +1148,13 @@ interventions: end_date: 2020-10-29 - start_date: 2021-01-18 end_date: 2021-01-31 - - affected_geoids: ["18000"] + - subpop: ["18000"] periods: - start_date: 2020-06-12 end_date: 2020-07-03 - start_date: 2021-02-15 end_date: 2021-03-01 - - affected_geoids: ["19000"] + - subpop: ["19000"] periods: - start_date: 2020-06-12 end_date: 2020-08-26 @@ -1168,13 +1166,13 @@ interventions: end_date: 2021-01-07 - start_date: 2021-01-08 end_date: 2021-02-06 - - affected_geoids: ["20000"] + - subpop: ["20000"] periods: - start_date: 2020-06-08 end_date: 2020-07-02 - start_date: 2020-07-03 end_date: 2021-03-30 - - affected_geoids: ["21000"] + - subpop: ["21000"] periods: - start_date: 2020-06-29 end_date: 2020-07-27 @@ -1184,7 +1182,7 @@ interventions: end_date: 2021-03-04 - start_date: 2021-03-05 end_date: 2021-05-15 - - affected_geoids: ["22000"] + - subpop: ["22000"] periods: - start_date: 2020-09-11 end_date: 2020-11-24 @@ -1192,17 +1190,17 @@ interventions: end_date: 2021-03-10 - start_date: 2021-03-11 end_date: 2021-03-30 - - affected_geoids: ["23000"] + - subpop: ["23000"] periods: - start_date: 2020-07-01 end_date: 2020-10-12 - start_date: 2020-11-20 end_date: 2021-01-31 - - affected_geoids: ["24000"] + - subpop: ["24000"] periods: - start_date: 2020-09-04 end_date: 2020-11-10 - - affected_geoids: ["25000"] + - subpop: ["25000"] periods: - start_date: 2020-07-06 end_date: 2020-10-04 @@ -1214,13 +1212,13 @@ interventions: end_date: 2020-12-25 - start_date: 2021-02-08 end_date: 2021-02-28 - - affected_geoids: ["26000"] + - subpop: ["26000"] periods: - start_date: 2021-03-05 end_date: 2021-03-21 - start_date: 2021-03-22 end_date: 2021-05-14 - - affected_geoids: ["27000"] + - subpop: ["27000"] periods: - start_date: 2020-06-10 end_date: 2020-07-24 @@ -1230,7 +1228,7 @@ interventions: end_date: 2021-02-12 - start_date: 2021-02-13 end_date: 2021-03-14 - - affected_geoids: ["28000"] + - subpop: ["28000"] periods: - start_date: 2020-06-01 end_date: 2020-09-13 @@ -1238,21 +1236,21 @@ interventions: end_date: 2020-12-10 - start_date: 2020-12-11 end_date: 2021-03-02 - - affected_geoids: ["29000"] + - subpop: ["29000"] periods: - start_date: 2020-05-04 end_date: 2020-06-15 - - affected_geoids: ["30000"] + - subpop: ["30000"] periods: - start_date: 2021-01-15 end_date: 2021-02-11 - - affected_geoids: ["31000"] + - subpop: ["31000"] periods: - start_date: 2020-06-22 end_date: 2020-09-13 - start_date: 2020-12-24 end_date: 2021-01-29 - - affected_geoids: ["32000"] + - subpop: ["32000"] periods: - start_date: 2020-05-29 end_date: 2020-07-09 @@ -1260,7 +1258,7 @@ interventions: end_date: 2020-11-23 - start_date: 2021-02-15 end_date: 2021-03-14 - - affected_geoids: ["33000"] + - subpop: ["33000"] periods: - start_date: 2020-06-29 end_date: 2020-10-14 @@ -1272,7 +1270,7 @@ interventions: end_date: 2021-03-10 - start_date: 2021-03-11 end_date: 2021-04-16 - - affected_geoids: ["34000"] + - subpop: ["34000"] periods: - start_date: 2020-09-04 end_date: 2020-11-11 @@ -1280,13 +1278,13 @@ interventions: end_date: 2021-02-21 - start_date: 2021-02-22 end_date: 2021-03-18 - - affected_geoids: ["35000"] + - subpop: ["35000"] periods: - start_date: 2021-02-24 end_date: 2021-03-09 - start_date: 2021-03-10 end_date: 2021-03-23 - - affected_geoids: ["36000"] + - subpop: ["36000"] periods: - start_date: 2020-07-20 end_date: 2020-09-29 @@ -1296,15 +1294,15 @@ interventions: end_date: 2021-02-11 - start_date: 2021-02-12 end_date: 2021-03-18 - - affected_geoids: ["37000"] + - subpop: ["37000"] periods: - start_date: 2020-10-02 end_date: 2020-12-10 - - affected_geoids: ["38000"] + - subpop: ["38000"] periods: - start_date: 2020-05-29 end_date: 2020-10-15 - - affected_geoids: ["39000"] + - subpop: ["39000"] periods: - start_date: 2020-06-19 end_date: 2020-09-20 @@ -1312,17 +1310,17 @@ interventions: end_date: 2020-11-18 - start_date: 2021-02-11 end_date: 2021-03-01 - - affected_geoids: ["40000"] + - subpop: ["40000"] periods: - start_date: 2020-06-01 end_date: 2020-11-15 - start_date: 2020-11-16 end_date: 2020-12-13 - - affected_geoids: ["41000"] + - subpop: ["41000"] periods: - start_date: 2021-02-26 end_date: 2021-03-28 - - affected_geoids: ["42000"] + - subpop: ["42000"] periods: - start_date: 2020-09-14 end_date: 2020-10-05 @@ -1330,29 +1328,29 @@ interventions: end_date: 2020-12-11 - start_date: 2021-01-04 end_date: 2021-02-28 - - affected_geoids: ["44000"] + - subpop: ["44000"] periods: - start_date: 2020-06-30 end_date: 2020-11-07 - start_date: 2021-02-12 end_date: 2021-03-18 - - affected_geoids: ["45000"] + - subpop: ["45000"] periods: - start_date: 2020-08-03 end_date: 2020-10-01 - start_date: 2020-10-02 end_date: 2021-02-28 - - affected_geoids: ["47000"] + - subpop: ["47000"] periods: - start_date: 2021-01-20 end_date: 2021-02-27 - start_date: 2021-02-28 end_date: 2021-04-27 - - affected_geoids: ["48000"] + - subpop: ["48000"] periods: - start_date: 2020-10-14 end_date: 2021-03-09 - - affected_geoids: ["49000"] + - subpop: ["49000"] periods: - start_date: 2020-06-19 end_date: 2020-10-14 @@ -1360,13 +1358,13 @@ interventions: end_date: 2020-11-08 - start_date: 2020-11-24 end_date: 2021-03-04 - - affected_geoids: ["50000"] + - subpop: ["50000"] periods: - start_date: 2020-06-26 end_date: 2020-07-31 - start_date: 2020-08-01 end_date: 2020-11-13 - - affected_geoids: ["51000"] + - subpop: ["51000"] periods: - start_date: 2020-07-01 end_date: 2020-07-30 @@ -1374,11 +1372,11 @@ interventions: end_date: 2020-11-14 - start_date: 2020-11-15 end_date: 2020-12-13 - - affected_geoids: ["53000"] + - subpop: ["53000"] periods: - start_date: 2021-02-01 end_date: 2021-02-13 - - affected_geoids: ["54000"] + - subpop: ["54000"] periods: - start_date: 2020-06-05 end_date: 2020-06-30 @@ -1388,13 +1386,13 @@ interventions: end_date: 2021-02-13 - start_date: 2021-02-14 end_date: 2021-03-04 - - affected_geoids: ["55000"] + - subpop: ["55000"] periods: - start_date: 2021-01-13 end_date: 2021-02-08 - start_date: 2021-02-09 end_date: 2021-03-18 - - affected_geoids: ["56000"] + - subpop: ["56000"] periods: - start_date: 2020-06-15 end_date: 2020-08-15 @@ -1402,19 +1400,19 @@ interventions: end_date: 2020-12-08 - start_date: 2021-01-26 end_date: 2021-02-14 - - affected_geoids: ["66000"] + - subpop: ["66000"] periods: - start_date: 2021-02-22 end_date: 2021-05-14 - start_date: 2021-05-15 end_date: 2021-08-07 - - affected_geoids: ["69000"] + - subpop: ["69000"] periods: - start_date: 2020-06-16 end_date: 2020-08-23 - start_date: 2020-09-07 end_date: 2021-08-07 - - affected_geoids: ["72000"] + - subpop: ["72000"] periods: - start_date: 2020-06-16 end_date: 2020-06-30 @@ -1432,7 +1430,7 @@ interventions: end_date: 2021-04-16 - start_date: 2021-05-24 end_date: 2021-06-06 - - affected_geoids: ["78000"] + - subpop: ["78000"] periods: - start_date: 2020-11-09 end_date: 2020-12-16 @@ -1455,74 +1453,74 @@ interventions: a: -1 b: 1 open_p4: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: ["01000"] + - subpop: ["01000"] periods: - start_date: 2021-04-09 end_date: 2021-05-30 - - affected_geoids: ["02000"] + - subpop: ["02000"] periods: - start_date: 2020-05-22 end_date: 2020-11-15 - start_date: 2021-02-15 end_date: 2021-08-07 - - affected_geoids: ["04000"] + - subpop: ["04000"] periods: - start_date: 2021-03-25 end_date: 2021-08-07 - - affected_geoids: ["05000"] + - subpop: ["05000"] periods: - start_date: 2021-03-31 end_date: 2021-08-07 - - affected_geoids: ["06000"] + - subpop: ["06000"] periods: - start_date: 2021-06-15 end_date: 2021-08-07 - - affected_geoids: ["08000"] + - subpop: ["08000"] periods: - start_date: 2021-03-24 end_date: 2021-04-15 - - affected_geoids: ["09000"] + - subpop: ["09000"] periods: - start_date: 2021-03-19 end_date: 2021-04-01 - - affected_geoids: ["11000"] + - subpop: ["11000"] periods: - start_date: 2021-05-17 end_date: 2021-05-20 - - affected_geoids: ["13000"] + - subpop: ["13000"] periods: - start_date: 2021-04-08 end_date: 2021-04-30 - - affected_geoids: ["15000"] + - subpop: ["15000"] periods: - start_date: 2021-06-11 end_date: 2021-08-07 - - affected_geoids: ["16000"] + - subpop: ["16000"] periods: - start_date: 2020-06-13 end_date: 2020-10-26 - start_date: 2021-05-11 end_date: 2021-08-07 - - affected_geoids: ["17000"] + - subpop: ["17000"] periods: - start_date: 2020-06-26 end_date: 2020-07-23 - start_date: 2021-02-01 end_date: 2021-05-16 - - affected_geoids: ["18000"] + - subpop: ["18000"] periods: - start_date: 2020-07-04 end_date: 2020-09-25 - start_date: 2021-03-02 end_date: 2021-04-05 - - affected_geoids: ["19000"] + - subpop: ["19000"] periods: - start_date: 2021-02-07 end_date: 2021-08-07 - - affected_geoids: ["20000"] + - subpop: ["20000"] periods: - start_date: 2021-03-31 end_date: 2021-04-05 @@ -1530,17 +1528,17 @@ interventions: end_date: 2021-05-13 - start_date: 2021-05-14 end_date: 2021-08-07 - - affected_geoids: ["21000"] + - subpop: ["21000"] periods: - start_date: 2021-05-16 end_date: 2021-05-27 - start_date: 2021-05-28 end_date: 2021-06-10 - - affected_geoids: ["22000"] + - subpop: ["22000"] periods: - start_date: 2021-03-31 end_date: 2021-04-27 - - affected_geoids: ["23000"] + - subpop: ["23000"] periods: - start_date: 2020-10-13 end_date: 2020-11-19 @@ -1548,19 +1546,19 @@ interventions: end_date: 2021-02-11 - start_date: 2021-02-12 end_date: 2021-03-25 - - affected_geoids: ["24000"] + - subpop: ["24000"] periods: - start_date: 2021-03-12 end_date: 2021-05-14 - - affected_geoids: ["25000"] + - subpop: ["25000"] periods: - start_date: 2021-03-01 end_date: 2021-03-21 - - affected_geoids: ["26000"] + - subpop: ["26000"] periods: - start_date: 2021-05-15 end_date: 2021-05-31 - - affected_geoids: ["27000"] + - subpop: ["27000"] periods: - start_date: 2021-03-15 end_date: 2021-03-31 @@ -1568,19 +1566,19 @@ interventions: end_date: 2021-05-06 - start_date: 2021-05-07 end_date: 2021-05-13 - - affected_geoids: ["28000"] + - subpop: ["28000"] periods: - start_date: 2020-09-14 end_date: 2020-11-24 - - affected_geoids: ["29000"] + - subpop: ["29000"] periods: - start_date: 2020-06-16 end_date: 2021-05-16 - - affected_geoids: ["30000"] + - subpop: ["30000"] periods: - start_date: 2021-02-12 end_date: 2021-08-07 - - affected_geoids: ["31000"] + - subpop: ["31000"] periods: - start_date: 2020-09-14 end_date: 2020-10-20 @@ -1588,117 +1586,117 @@ interventions: end_date: 2021-05-23 - start_date: 2021-05-24 end_date: 2021-08-07 - - affected_geoids: ["32000"] + - subpop: ["32000"] periods: - start_date: 2021-03-15 end_date: 2021-03-29 - start_date: 2021-03-30 end_date: 2021-04-30 - - affected_geoids: ["33000"] + - subpop: ["33000"] periods: - start_date: 2021-04-17 end_date: 2021-05-07 - start_date: 2021-05-08 end_date: 2021-08-07 - - affected_geoids: ["34000"] + - subpop: ["34000"] periods: - start_date: 2021-03-19 end_date: 2021-04-01 - - affected_geoids: ["35000"] + - subpop: ["35000"] periods: - start_date: 2021-03-24 end_date: 2021-04-06 - start_date: 2021-04-21 end_date: 2021-05-04 - - affected_geoids: ["36000"] + - subpop: ["36000"] periods: - start_date: 2021-03-19 end_date: 2021-03-31 - - affected_geoids: ["37000"] + - subpop: ["37000"] periods: - start_date: 2021-02-26 end_date: 2021-03-25 - - affected_geoids: ["38000"] + - subpop: ["38000"] periods: - start_date: 2021-01-18 end_date: 2021-08-07 - - affected_geoids: ["39000"] + - subpop: ["39000"] periods: - start_date: 2021-03-02 end_date: 2021-04-04 - start_date: 2021-04-05 end_date: 2021-04-26 - - affected_geoids: ["40000"] + - subpop: ["40000"] periods: - start_date: 2021-03-12 end_date: 2021-08-07 - - affected_geoids: ["41000"] + - subpop: ["41000"] periods: - start_date: 2021-03-29 end_date: 2021-04-18 - start_date: 2021-04-19 end_date: 2021-04-29 - - affected_geoids: ["42000"] + - subpop: ["42000"] periods: - start_date: 2021-03-01 end_date: 2021-04-03 - - affected_geoids: ["44000"] + - subpop: ["44000"] periods: - start_date: 2021-03-19 end_date: 2021-05-17 - - affected_geoids: ["45000"] + - subpop: ["45000"] periods: - start_date: 2021-03-01 end_date: 2021-03-18 - start_date: 2021-03-19 end_date: 2021-05-10 - - affected_geoids: ["46000"] + - subpop: ["46000"] periods: - start_date: 2020-04-28 end_date: 2021-08-07 - - affected_geoids: ["47000"] + - subpop: ["47000"] periods: - start_date: 2021-04-28 end_date: 2021-08-07 - - affected_geoids: ["48000"] + - subpop: ["48000"] periods: - start_date: 2021-03-10 end_date: 2021-08-07 - - affected_geoids: ["49000"] + - subpop: ["49000"] periods: - start_date: 2021-03-05 end_date: 2021-04-01 - start_date: 2021-04-02 end_date: 2021-04-09 - - affected_geoids: ["50000"] + - subpop: ["50000"] periods: - start_date: 2021-03-24 end_date: 2021-05-14 - - affected_geoids: ["51000"] + - subpop: ["51000"] periods: - start_date: 2021-03-01 end_date: 2021-03-31 - start_date: 2021-04-01 end_date: 2021-05-13 - - affected_geoids: ["53000"] + - subpop: ["53000"] periods: - start_date: 2021-02-14 end_date: 2021-03-21 - - affected_geoids: ["54000"] + - subpop: ["54000"] periods: - start_date: 2020-07-01 end_date: 2020-07-13 - start_date: 2021-03-05 end_date: 2021-04-19 - - affected_geoids: ["55000"] + - subpop: ["55000"] periods: - start_date: 2021-03-19 end_date: 2021-03-30 - - affected_geoids: ["56000"] + - subpop: ["56000"] periods: - start_date: 2020-08-16 end_date: 2020-11-23 - - affected_geoids: ["72000"] + - subpop: ["72000"] periods: - start_date: 2020-07-01 end_date: 2020-07-15 @@ -1717,78 +1715,78 @@ interventions: a: -1 b: 1 open_p5: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: ["01000"] + - subpop: ["01000"] periods: - start_date: 2021-05-31 end_date: 2021-08-07 - - affected_geoids: ["08000"] + - subpop: ["08000"] periods: - start_date: 2021-04-16 end_date: 2021-05-13 - - affected_geoids: ["09000"] + - subpop: ["09000"] periods: - start_date: 2021-04-02 end_date: 2021-04-30 - start_date: 2021-05-01 end_date: 2021-05-18 - - affected_geoids: ["11000"] + - subpop: ["11000"] periods: - start_date: 2021-05-21 end_date: 2021-06-10 - - affected_geoids: ["13000"] + - subpop: ["13000"] periods: - start_date: 2021-05-01 end_date: 2021-05-30 - start_date: 2021-05-31 end_date: 2021-08-07 - - affected_geoids: ["17000"] + - subpop: ["17000"] periods: - start_date: 2021-05-17 end_date: 2021-06-10 - - affected_geoids: ["18000"] + - subpop: ["18000"] periods: - start_date: 2020-09-26 end_date: 2020-11-10 - start_date: 2021-04-06 end_date: 2021-08-07 - - affected_geoids: ["21000"] + - subpop: ["21000"] periods: - start_date: 2021-06-11 end_date: 2021-08-07 - - affected_geoids: ["22000"] + - subpop: ["22000"] periods: - start_date: 2021-04-28 end_date: 2021-05-25 - start_date: 2021-05-26 end_date: 2021-08-07 - - affected_geoids: ["23000"] + - subpop: ["23000"] periods: - start_date: 2021-03-26 end_date: 2021-05-23 - - affected_geoids: ["24000"] + - subpop: ["24000"] periods: - start_date: 2021-05-15 end_date: 2021-08-07 - - affected_geoids: ["25000"] + - subpop: ["25000"] periods: - start_date: 2021-03-22 end_date: 2021-04-29 - start_date: 2021-04-30 end_date: 2021-05-28 - - affected_geoids: ["26000"] + - subpop: ["26000"] periods: - start_date: 2021-06-01 end_date: 2021-06-21 - - affected_geoids: ["27000"] + - subpop: ["27000"] periods: - start_date: 2021-05-14 end_date: 2021-05-27 - start_date: 2021-05-28 end_date: 2021-08-07 - - affected_geoids: ["28000"] + - subpop: ["28000"] periods: - start_date: 2021-03-03 end_date: 2021-03-30 @@ -1796,89 +1794,89 @@ interventions: end_date: 2021-04-29 - start_date: 2021-04-30 end_date: 2021-08-07 - - affected_geoids: ["29000"] + - subpop: ["29000"] periods: - start_date: 2021-05-17 end_date: 2021-08-07 - - affected_geoids: ["32000"] + - subpop: ["32000"] periods: - start_date: 2021-05-01 end_date: 2021-05-02 - start_date: 2021-05-03 end_date: 2021-05-31 - - affected_geoids: ["34000"] + - subpop: ["34000"] periods: - start_date: 2021-04-02 end_date: 2021-05-27 - - affected_geoids: ["35000"] + - subpop: ["35000"] periods: - start_date: 2021-04-07 end_date: 2021-04-20 - - affected_geoids: ["36000"] + - subpop: ["36000"] periods: - start_date: 2021-04-01 end_date: 2021-05-18 - - affected_geoids: ["37000"] + - subpop: ["37000"] periods: - start_date: 2021-03-26 end_date: 2021-04-29 - start_date: 2021-04-30 end_date: 2021-05-13 - - affected_geoids: ["39000"] + - subpop: ["39000"] periods: - start_date: 2021-04-27 end_date: 2021-05-16 - start_date: 2021-05-17 end_date: 2021-06-01 - - affected_geoids: ["41000"] + - subpop: ["41000"] periods: - start_date: 2021-06-09 end_date: 2021-08-07 - - affected_geoids: ["42000"] + - subpop: ["42000"] periods: - start_date: 2021-04-04 end_date: 2021-05-12 - - affected_geoids: ["44000"] + - subpop: ["44000"] periods: - start_date: 2021-05-18 end_date: 2021-05-20 - - affected_geoids: ["45000"] + - subpop: ["45000"] periods: - start_date: 2021-05-11 end_date: 2021-06-05 - start_date: 2021-06-06 end_date: 2021-08-07 - - affected_geoids: ["49000"] + - subpop: ["49000"] periods: - start_date: 2021-04-10 end_date: 2021-05-04 - start_date: 2021-05-05 end_date: 2021-08-07 - - affected_geoids: ["50000"] + - subpop: ["50000"] periods: - start_date: 2021-05-15 end_date: 2021-08-07 - - affected_geoids: ["51000"] + - subpop: ["51000"] periods: - start_date: 2021-05-14 end_date: 2021-05-27 - start_date: 2021-05-28 end_date: 2021-08-07 - - affected_geoids: ["53000"] + - subpop: ["53000"] periods: - start_date: 2021-03-22 end_date: 2021-05-12 - - affected_geoids: ["54000"] + - subpop: ["54000"] periods: - start_date: 2021-04-20 end_date: 2021-05-13 - - affected_geoids: ["55000"] + - subpop: ["55000"] periods: - start_date: 2021-03-31 end_date: 2021-05-31 - start_date: 2021-06-01 end_date: 2021-08-07 - - affected_geoids: ["56000"] + - subpop: ["56000"] periods: - start_date: 2021-02-15 end_date: 2021-02-28 @@ -1897,34 +1895,34 @@ interventions: a: -1 b: 1 sd: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: ["05000"] + - subpop: ["05000"] periods: - start_date: 2020-03-20 end_date: 2020-05-03 - - affected_geoids: ["19000"] + - subpop: ["19000"] periods: - start_date: 2020-04-02 end_date: 2020-05-14 - - affected_geoids: ["31000"] + - subpop: ["31000"] periods: - start_date: 2020-03-16 end_date: 2020-05-03 - - affected_geoids: ["38000"] + - subpop: ["38000"] periods: - start_date: 2020-03-19 end_date: 2020-04-30 - - affected_geoids: ["40000"] + - subpop: ["40000"] periods: - start_date: 2020-03-24 end_date: 2020-04-23 - - affected_geoids: ["46000"] + - subpop: ["46000"] periods: - start_date: 2020-03-16 end_date: 2020-04-27 - - affected_geoids: ["56000"] + - subpop: ["56000"] periods: - start_date: 2020-03-28 end_date: 2020-04-30 @@ -1941,46 +1939,46 @@ interventions: a: -1 b: 1 open_p6: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: ["08000"] + - subpop: ["08000"] periods: - start_date: 2021-05-14 end_date: 2021-05-31 - - affected_geoids: ["09000"] + - subpop: ["09000"] periods: - start_date: 2021-05-19 end_date: 2021-08-07 - - affected_geoids: ["11000"] + - subpop: ["11000"] periods: - start_date: 2021-06-11 end_date: 2021-08-07 - - affected_geoids: ["17000"] + - subpop: ["17000"] periods: - start_date: 2021-06-11 end_date: 2021-08-07 - - affected_geoids: ["23000"] + - subpop: ["23000"] periods: - start_date: 2021-05-24 end_date: 2021-08-07 - - affected_geoids: ["25000"] + - subpop: ["25000"] periods: - start_date: 2021-05-29 end_date: 2021-08-07 - - affected_geoids: ["26000"] + - subpop: ["26000"] periods: - start_date: 2021-06-22 end_date: 2021-08-07 - - affected_geoids: ["32000"] + - subpop: ["32000"] periods: - start_date: 2021-06-01 end_date: 2021-08-07 - - affected_geoids: ["34000"] + - subpop: ["34000"] periods: - start_date: 2021-05-28 end_date: 2021-06-03 - - affected_geoids: ["35000"] + - subpop: ["35000"] periods: - start_date: 2021-05-05 end_date: 2021-05-13 @@ -1988,37 +1986,37 @@ interventions: end_date: 2021-06-01 - start_date: 2021-06-02 end_date: 2021-08-07 - - affected_geoids: ["36000"] + - subpop: ["36000"] periods: - start_date: 2021-05-19 end_date: 2021-08-07 - - affected_geoids: ["37000"] + - subpop: ["37000"] periods: - start_date: 2021-05-14 end_date: 2021-08-07 - - affected_geoids: ["39000"] + - subpop: ["39000"] periods: - start_date: 2021-06-02 end_date: 2021-06-18 - start_date: 2021-06-19 end_date: 2021-08-07 - - affected_geoids: ["42000"] + - subpop: ["42000"] periods: - start_date: 2021-05-13 end_date: 2021-05-16 - start_date: 2021-05-17 end_date: 2021-05-30 - - affected_geoids: ["44000"] + - subpop: ["44000"] periods: - start_date: 2021-05-21 end_date: 2021-08-07 - - affected_geoids: ["53000"] + - subpop: ["53000"] periods: - start_date: 2021-05-13 end_date: 2021-05-17 - start_date: 2021-05-18 end_date: 2021-08-07 - - affected_geoids: ["54000"] + - subpop: ["54000"] periods: - start_date: 2021-05-14 end_date: 2021-06-07 @@ -2026,7 +2024,7 @@ interventions: end_date: 2021-06-19 - start_date: 2021-06-20 end_date: 2021-08-07 - - affected_geoids: ["56000"] + - subpop: ["56000"] periods: - start_date: 2021-03-16 end_date: 2021-05-20 @@ -2045,18 +2043,18 @@ interventions: a: -1 b: 1 open_p7: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: ["08000"] + - subpop: ["08000"] periods: - start_date: 2021-06-01 end_date: 2021-08-07 - - affected_geoids: ["34000"] + - subpop: ["34000"] periods: - start_date: 2021-06-04 end_date: 2021-08-07 - - affected_geoids: ["42000"] + - subpop: ["42000"] periods: - start_date: 2021-05-31 end_date: 2021-08-07 @@ -2073,10 +2071,10 @@ interventions: a: -1 b: 1 Seas_jan: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-01-01 end_date: 2020-01-31 @@ -2095,10 +2093,10 @@ interventions: a: -1 b: 1 Seas_feb: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-02-01 end_date: 2020-02-29 @@ -2117,10 +2115,10 @@ interventions: a: -1 b: 1 Seas_mar: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-03-01 end_date: 2020-03-31 @@ -2139,10 +2137,10 @@ interventions: a: -1 b: 1 Seas_may: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-05-01 end_date: 2020-05-31 @@ -2161,10 +2159,10 @@ interventions: a: -1 b: 1 Seas_jun: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-06-01 end_date: 2020-06-30 @@ -2183,10 +2181,10 @@ interventions: a: -1 b: 1 Seas_jul: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-07-01 end_date: 2020-07-31 @@ -2205,10 +2203,10 @@ interventions: a: -1 b: 1 Seas_aug: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: R0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-08-01 end_date: 2020-08-31 @@ -2227,9 +2225,9 @@ interventions: a: -1 b: 1 Seas_sep: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2020-09-01 period_end_date: 2020-09-30 value: @@ -2245,9 +2243,9 @@ interventions: a: -1 b: 1 Seas_oct: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2020-10-01 period_end_date: 2020-10-31 value: @@ -2263,9 +2261,9 @@ interventions: a: -1 b: 1 Seas_nov: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2020-11-01 period_end_date: 2020-11-30 value: @@ -2281,9 +2279,9 @@ interventions: a: -1 b: 1 Seas_dec: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2020-12-01 period_end_date: 2020-12-31 value: @@ -2299,3906 +2297,3906 @@ interventions: a: -1 b: 1 AL_Dose1_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00012 AL_Dose1_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00327 AL_Dose1_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.003378 AL_Dose1_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.005034 AL_Dose1_may2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.002462 AL_Dose1_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.001837 AL_Dose1_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.003138 AL_Dose1_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: distribution: fixed value: 0.003718 AK_Dose1_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.001575 AK_Dose1_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.004632 AK_Dose1_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.005033 AK_Dose1_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.005206 AK_Dose1_may2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.003905 AK_Dose1_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.001637 AK_Dose1_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.003683 AK_Dose1_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: distribution: fixed value: 0.004457 AZ_Dose1_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00091 AZ_Dose1_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.003637 AZ_Dose1_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.004542 AZ_Dose1_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.006755 AZ_Dose1_may2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.004126 AZ_Dose1_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.003358 AZ_Dose1_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.003208 AZ_Dose1_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 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2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.004229 WV_Dose1_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.004682 WV_Dose1_may2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.003996 WV_Dose1_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.003008 WV_Dose1_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.003992 WV_Dose1_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: distribution: fixed value: 0.003925 WI_Dose1_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.000873 WI_Dose1_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.003428 WI_Dose1_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.004815 WI_Dose1_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.008678 WI_Dose1_may2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.004268 WI_Dose1_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.004013 WI_Dose1_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.004666 WI_Dose1_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: distribution: fixed value: 0.005008 WY_Dose1_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.001042 WY_Dose1_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00325 WY_Dose1_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.00427 WY_Dose1_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.004258 WY_Dose1_may2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.0017 WY_Dose1_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.003188 WY_Dose1_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.005629 WY_Dose1_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: distribution: fixed value: 0.004926 GU_Dose1_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.001893 GU_Dose1_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.004754 GU_Dose1_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.002632 GU_Dose1_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.009422 MP_Dose1_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00187 MP_Dose1_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.004072 MP_Dose1_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.003597 MP_Dose1_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.005874 MP_Dose1_may2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.004361 MP_Dose1_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.004769 MP_Dose1_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00444 MP_Dose1_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: distribution: fixed value: 0.004518 PR_Dose1_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00012 PR_Dose1_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.002806 PR_Dose1_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.002673 PR_Dose1_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.005806 PR_Dose1_may2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.007686 PR_Dose1_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.010066 PR_Dose1_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.005251 PR_Dose1_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: distribution: fixed value: 0.003103 VI_Dose1_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.000392 VI_Dose1_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.002511 VI_Dose1_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.003722 VI_Dose1_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.0047 VI_Dose1_may2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.002398 VI_Dose1_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00255 VI_Dose1_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.003405 VI_Dose1_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: transition_rate 0 - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: distribution: fixed value: 0.003368 variantR0adj_Week2: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-01-10 period_end_date: 2021-01-23 value: @@ -6214,9 +6212,9 @@ interventions: a: -1 b: 1 variantR0adj_Week4: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-01-24 period_end_date: 2021-01-30 value: @@ -6232,9 +6230,9 @@ interventions: a: -1 b: 1 variantR0adj_Week5: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-01-31 period_end_date: 2021-02-06 value: @@ -6250,9 +6248,9 @@ interventions: a: -1 b: 1 variantR0adj_Week6: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-02-07 period_end_date: 2021-02-13 value: @@ -6268,9 +6266,9 @@ interventions: a: -1 b: 1 variantR0adj_Week7: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-02-14 period_end_date: 2021-02-20 value: @@ -6286,9 +6284,9 @@ interventions: a: -1 b: 1 variantR0adj_Week8: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-02-21 period_end_date: 2021-02-27 value: @@ -6304,9 +6302,9 @@ interventions: a: -1 b: 1 variantR0adj_Week9: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-02-28 period_end_date: 2021-03-06 value: @@ -6322,9 +6320,9 @@ interventions: a: -1 b: 1 variantR0adj_Week10: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-03-07 period_end_date: 2021-03-13 value: @@ -6340,9 +6338,9 @@ interventions: a: -1 b: 1 variantR0adj_Week11: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-03-14 period_end_date: 2021-03-20 value: @@ -6358,9 +6356,9 @@ interventions: a: -1 b: 1 variantR0adj_Week12: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-03-21 period_end_date: 2021-03-27 value: @@ -6376,9 +6374,9 @@ interventions: a: -1 b: 1 variantR0adj_Week13: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-03-28 period_end_date: 2021-04-03 value: @@ -6394,9 +6392,9 @@ interventions: a: -1 b: 1 variantR0adj_Week14: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-04-04 period_end_date: 2021-04-10 value: @@ -6412,9 +6410,9 @@ interventions: a: -1 b: 1 variantR0adj_Week15: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-04-11 period_end_date: 2021-04-17 value: @@ -6430,9 +6428,9 @@ interventions: a: -1 b: 1 variantR0adj_Week16: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-04-18 period_end_date: 2021-04-24 value: @@ -6448,9 +6446,9 @@ interventions: a: -1 b: 1 variantR0adj_Week17: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-04-25 period_end_date: 2021-05-01 value: @@ -6466,9 +6464,9 @@ interventions: a: -1 b: 1 variantR0adj_Week18: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-05-02 period_end_date: 2021-05-29 value: @@ -6484,9 +6482,9 @@ interventions: a: -1 b: 1 variantR0adj_Week22: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-05-30 period_end_date: 2021-06-05 value: @@ -6502,9 +6500,9 @@ interventions: a: -1 b: 1 variantR0adj_Week23: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-06-06 period_end_date: 2021-06-12 value: @@ -6520,9 +6518,9 @@ interventions: a: -1 b: 1 variantR0adj_Week24: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-06-13 period_end_date: 2021-06-19 value: @@ -6538,9 +6536,9 @@ interventions: a: -1 b: 1 variantR0adj_Week25: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-06-20 period_end_date: 2021-06-26 value: @@ -6550,9 +6548,9 @@ interventions: a: -1.5 b: 0 variantR0adj_Week26: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-06-27 period_end_date: 2021-07-03 value: @@ -6562,9 +6560,9 @@ interventions: a: -1.5 b: 0 variantR0adj_Week27: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-07-04 period_end_date: 2021-07-10 value: @@ -6574,9 +6572,9 @@ interventions: a: -1.5 b: 0 variantR0adj_Week28: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-07-11 period_end_date: 2021-07-17 value: @@ -6586,9 +6584,9 @@ interventions: a: -1.5 b: 0 variantR0adj_Week29: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-07-18 period_end_date: 2021-07-24 value: @@ -6598,9 +6596,9 @@ interventions: a: -1.5 b: 0 variantR0adj_Week30: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-07-25 period_end_date: 2021-07-31 value: @@ -6610,9 +6608,9 @@ interventions: a: -1.5 b: 0 variantR0adj_Week31: - template: Reduce + template: SinglePeriodModifier parameter: R0 - affected_geoids: "all" + subpop: "all" period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -6622,25 +6620,25 @@ interventions: a: -1.5 b: 0 NPI: - template: Stacked + template: StackedModifier scenarios: ["lockdown", "open_p1", "open_p2", "open_p3", "open_p4", "open_p5", "sd", "open_p6", "open_p7"] seasonal: - template: Stacked + template: StackedModifier scenarios: ["Seas_jan", "Seas_feb", "Seas_mar", "Seas_may", "Seas_jun", "Seas_jul", "Seas_aug", "Seas_sep", "Seas_oct", "Seas_nov", "Seas_dec"] vaccination: - template: Stacked + template: StackedModifier scenarios: ["AL_Dose1_jan2021", "AL_Dose1_feb2021", "AL_Dose1_mar2021", "AL_Dose1_apr2021", "AL_Dose1_may2021", "AL_Dose1_jun2021", "AL_Dose1_jul2021", "AL_Dose1_aug2021", "AK_Dose1_jan2021", "AK_Dose1_feb2021", "AK_Dose1_mar2021", "AK_Dose1_apr2021", "AK_Dose1_may2021", "AK_Dose1_jun2021", "AK_Dose1_jul2021", "AK_Dose1_aug2021", "AZ_Dose1_jan2021", "AZ_Dose1_feb2021", "AZ_Dose1_mar2021", "AZ_Dose1_apr2021", "AZ_Dose1_may2021", "AZ_Dose1_jun2021", "AZ_Dose1_jul2021", "AZ_Dose1_aug2021", "AR_Dose1_jan2021", "AR_Dose1_feb2021", "AR_Dose1_mar2021", "AR_Dose1_apr2021", "AR_Dose1_may2021", "AR_Dose1_jun2021", "AR_Dose1_jul2021", "AR_Dose1_aug2021", "CA_Dose1_feb2021", "CA_Dose1_mar2021", "CA_Dose1_apr2021", "CA_Dose1_may2021", "CA_Dose1_jun2021", "CA_Dose1_jul2021", "CA_Dose1_aug2021", "CO_Dose1_jan2021", "CO_Dose1_feb2021", "CO_Dose1_mar2021", "CO_Dose1_apr2021", "CO_Dose1_may2021", "CO_Dose1_jun2021", "CO_Dose1_jul2021", "CO_Dose1_aug2021", "CT_Dose1_jan2021", "CT_Dose1_feb2021", "CT_Dose1_mar2021", "CT_Dose1_apr2021", "CT_Dose1_may2021", "CT_Dose1_jun2021", "CT_Dose1_jul2021", "CT_Dose1_aug2021", "DE_Dose1_jan2021", "DE_Dose1_feb2021", "DE_Dose1_mar2021", "DE_Dose1_apr2021", "DE_Dose1_may2021", "DE_Dose1_jun2021", "DE_Dose1_jul2021", "DE_Dose1_aug2021", "DC_Dose1_feb2021", "DC_Dose1_mar2021", "DC_Dose1_apr2021", "DC_Dose1_may2021", "DC_Dose1_jun2021", "DC_Dose1_jul2021", "DC_Dose1_aug2021", "FL_Dose1_jan2021", "FL_Dose1_feb2021", "FL_Dose1_mar2021", "FL_Dose1_apr2021", "FL_Dose1_may2021", "FL_Dose1_jun2021", "FL_Dose1_jul2021", "FL_Dose1_aug2021", "GA_Dose1_jan2021", "GA_Dose1_feb2021", "GA_Dose1_mar2021", "GA_Dose1_apr2021", "GA_Dose1_may2021", "GA_Dose1_jun2021", "GA_Dose1_jul2021", "GA_Dose1_aug2021", "HI_Dose1_jan2021", "HI_Dose1_feb2021", "HI_Dose1_mar2021", "HI_Dose1_apr2021", "HI_Dose1_may2021", "HI_Dose1_jun2021", "HI_Dose1_jul2021", "HI_Dose1_aug2021", "ID_Dose1_jan2021", "ID_Dose1_feb2021", "ID_Dose1_mar2021", "ID_Dose1_apr2021", "ID_Dose1_may2021", "ID_Dose1_jun2021", "ID_Dose1_jul2021", "ID_Dose1_aug2021", "IL_Dose1_jan2021", "IL_Dose1_feb2021", "IL_Dose1_mar2021", "IL_Dose1_apr2021", "IL_Dose1_may2021", "IL_Dose1_jun2021", "IL_Dose1_jul2021", "IL_Dose1_aug2021", "IN_Dose1_jan2021", "IN_Dose1_feb2021", "IN_Dose1_mar2021", "IN_Dose1_apr2021", "IN_Dose1_may2021", "IN_Dose1_jun2021", "IN_Dose1_jul2021", "IN_Dose1_aug2021", "IA_Dose1_jan2021", "IA_Dose1_feb2021", "IA_Dose1_mar2021", "IA_Dose1_apr2021", "IA_Dose1_may2021", "IA_Dose1_jun2021", "IA_Dose1_jul2021", "IA_Dose1_aug2021", "KS_Dose1_jan2021", "KS_Dose1_feb2021", "KS_Dose1_mar2021", "KS_Dose1_apr2021", "KS_Dose1_may2021", "KS_Dose1_jun2021", "KS_Dose1_jul2021", "KS_Dose1_aug2021", "KY_Dose1_jan2021", "KY_Dose1_feb2021", "KY_Dose1_mar2021", "KY_Dose1_apr2021", "KY_Dose1_may2021", "KY_Dose1_jun2021", "KY_Dose1_jul2021", "KY_Dose1_aug2021", "LA_Dose1_jan2021", "LA_Dose1_feb2021", "LA_Dose1_mar2021", "LA_Dose1_apr2021", "LA_Dose1_may2021", "LA_Dose1_jun2021", "LA_Dose1_jul2021", "LA_Dose1_aug2021", "ME_Dose1_jan2021", "ME_Dose1_feb2021", "ME_Dose1_mar2021", "ME_Dose1_apr2021", "ME_Dose1_may2021", "ME_Dose1_jun2021", "ME_Dose1_jul2021", "ME_Dose1_aug2021", "MD_Dose1_jan2021", "MD_Dose1_feb2021", "MD_Dose1_mar2021", "MD_Dose1_apr2021", "MD_Dose1_may2021", "MD_Dose1_jun2021", "MD_Dose1_jul2021", "MD_Dose1_aug2021", "MA_Dose1_jan2021", "MA_Dose1_feb2021", "MA_Dose1_mar2021", "MA_Dose1_apr2021", "MA_Dose1_may2021", "MA_Dose1_jun2021", "MA_Dose1_jul2021", "MA_Dose1_aug2021", "MI_Dose1_jan2021", "MI_Dose1_feb2021", "MI_Dose1_mar2021", "MI_Dose1_apr2021", "MI_Dose1_may2021", "MI_Dose1_jun2021", "MI_Dose1_jul2021", "MI_Dose1_aug2021", "MN_Dose1_jan2021", "MN_Dose1_feb2021", "MN_Dose1_mar2021", "MN_Dose1_apr2021", "MN_Dose1_may2021", "MN_Dose1_jun2021", "MN_Dose1_jul2021", "MN_Dose1_aug2021", "MS_Dose1_jan2021", "MS_Dose1_feb2021", "MS_Dose1_mar2021", "MS_Dose1_apr2021", "MS_Dose1_may2021", "MS_Dose1_jun2021", "MS_Dose1_jul2021", "MS_Dose1_aug2021", "MO_Dose1_jan2021", "MO_Dose1_feb2021", "MO_Dose1_mar2021", "MO_Dose1_apr2021", "MO_Dose1_may2021", "MO_Dose1_jun2021", "MO_Dose1_jul2021", "MO_Dose1_aug2021", "MT_Dose1_jan2021", "MT_Dose1_feb2021", "MT_Dose1_mar2021", "MT_Dose1_apr2021", "MT_Dose1_may2021", "MT_Dose1_jun2021", "MT_Dose1_jul2021", "MT_Dose1_aug2021", "NE_Dose1_jan2021", "NE_Dose1_feb2021", "NE_Dose1_mar2021", "NE_Dose1_apr2021", "NE_Dose1_may2021", "NE_Dose1_jun2021", "NE_Dose1_jul2021", "NE_Dose1_aug2021", "NV_Dose1_jan2021", "NV_Dose1_feb2021", "NV_Dose1_mar2021", "NV_Dose1_apr2021", "NV_Dose1_may2021", "NV_Dose1_jun2021", "NV_Dose1_jul2021", "NV_Dose1_aug2021", "NH_Dose1_jan2021", "NH_Dose1_feb2021", "NH_Dose1_mar2021", "NH_Dose1_apr2021", "NH_Dose1_may2021", "NH_Dose1_jun2021", "NH_Dose1_jul2021", "NH_Dose1_aug2021", "NJ_Dose1_jan2021", "NJ_Dose1_feb2021", "NJ_Dose1_mar2021", "NJ_Dose1_apr2021", "NJ_Dose1_may2021", "NJ_Dose1_jun2021", "NJ_Dose1_jul2021", "NJ_Dose1_aug2021", "NM_Dose1_feb2021", "NM_Dose1_mar2021", "NM_Dose1_apr2021", "NM_Dose1_may2021", "NM_Dose1_jun2021", "NM_Dose1_jul2021", "NM_Dose1_aug2021", "NY_Dose1_jan2021", "NY_Dose1_feb2021", "NY_Dose1_mar2021", "NY_Dose1_apr2021", "NY_Dose1_may2021", "NY_Dose1_jun2021", "NY_Dose1_jul2021", "NY_Dose1_aug2021", "NC_Dose1_jan2021", "NC_Dose1_feb2021", "NC_Dose1_mar2021", "NC_Dose1_apr2021", "NC_Dose1_may2021", "NC_Dose1_jun2021", "NC_Dose1_jul2021", "NC_Dose1_aug2021", "ND_Dose1_jan2021", "ND_Dose1_feb2021", "ND_Dose1_mar2021", "ND_Dose1_apr2021", "ND_Dose1_may2021", "ND_Dose1_jun2021", "ND_Dose1_jul2021", "ND_Dose1_aug2021", "OH_Dose1_jan2021", "OH_Dose1_feb2021", "OH_Dose1_mar2021", "OH_Dose1_apr2021", "OH_Dose1_may2021", "OH_Dose1_jun2021", "OH_Dose1_jul2021", "OH_Dose1_aug2021", "OK_Dose1_jan2021", "OK_Dose1_feb2021", "OK_Dose1_mar2021", "OK_Dose1_apr2021", "OK_Dose1_may2021", "OK_Dose1_jun2021", "OK_Dose1_jul2021", "OK_Dose1_aug2021", "OR_Dose1_jan2021", "OR_Dose1_feb2021", "OR_Dose1_mar2021", "OR_Dose1_apr2021", "OR_Dose1_may2021", "OR_Dose1_jun2021", "OR_Dose1_jul2021", "OR_Dose1_aug2021", "PA_Dose1_jan2021", "PA_Dose1_feb2021", "PA_Dose1_mar2021", "PA_Dose1_apr2021", "PA_Dose1_may2021", "PA_Dose1_jun2021", "PA_Dose1_jul2021", "PA_Dose1_aug2021", "RI_Dose1_jan2021", "RI_Dose1_feb2021", "RI_Dose1_mar2021", "RI_Dose1_apr2021", "RI_Dose1_may2021", "RI_Dose1_jun2021", "RI_Dose1_jul2021", "RI_Dose1_aug2021", "SC_Dose1_jan2021", "SC_Dose1_feb2021", "SC_Dose1_mar2021", "SC_Dose1_apr2021", "SC_Dose1_may2021", "SC_Dose1_jun2021", "SC_Dose1_jul2021", "SC_Dose1_aug2021", "SD_Dose1_jan2021", "SD_Dose1_feb2021", "SD_Dose1_mar2021", "SD_Dose1_apr2021", "SD_Dose1_may2021", "SD_Dose1_jun2021", "SD_Dose1_jul2021", "SD_Dose1_aug2021", "TN_Dose1_jan2021", "TN_Dose1_feb2021", "TN_Dose1_mar2021", "TN_Dose1_apr2021", "TN_Dose1_may2021", "TN_Dose1_jun2021", "TN_Dose1_jul2021", "TN_Dose1_aug2021", "TX_Dose1_jan2021", "TX_Dose1_feb2021", "TX_Dose1_mar2021", "TX_Dose1_apr2021", "TX_Dose1_may2021", "TX_Dose1_jun2021", "TX_Dose1_jul2021", "TX_Dose1_aug2021", "UT_Dose1_jan2021", "UT_Dose1_feb2021", "UT_Dose1_mar2021", "UT_Dose1_apr2021", "UT_Dose1_may2021", "UT_Dose1_jun2021", "UT_Dose1_jul2021", "UT_Dose1_aug2021", "VT_Dose1_jan2021", "VT_Dose1_feb2021", "VT_Dose1_mar2021", "VT_Dose1_apr2021", "VT_Dose1_may2021", "VT_Dose1_jun2021", "VT_Dose1_jul2021", "VT_Dose1_aug2021", "VA_Dose1_jan2021", "VA_Dose1_feb2021", "VA_Dose1_mar2021", "VA_Dose1_apr2021", "VA_Dose1_may2021", "VA_Dose1_jun2021", "VA_Dose1_jul2021", "VA_Dose1_aug2021", "WA_Dose1_jan2021", "WA_Dose1_feb2021", "WA_Dose1_mar2021", "WA_Dose1_apr2021", "WA_Dose1_may2021", "WA_Dose1_jun2021", "WA_Dose1_jul2021", "WA_Dose1_aug2021", "WV_Dose1_jan2021", "WV_Dose1_feb2021", "WV_Dose1_mar2021", "WV_Dose1_apr2021", "WV_Dose1_may2021", "WV_Dose1_jun2021", "WV_Dose1_jul2021", "WV_Dose1_aug2021", "WI_Dose1_jan2021", "WI_Dose1_feb2021", "WI_Dose1_mar2021", "WI_Dose1_apr2021", "WI_Dose1_may2021", "WI_Dose1_jun2021", "WI_Dose1_jul2021", "WI_Dose1_aug2021", "WY_Dose1_jan2021", "WY_Dose1_feb2021", "WY_Dose1_mar2021", "WY_Dose1_apr2021", "WY_Dose1_may2021", "WY_Dose1_jun2021", "WY_Dose1_jul2021", "WY_Dose1_aug2021", "GU_Dose1_jan2021", "GU_Dose1_feb2021", "GU_Dose1_mar2021", "GU_Dose1_apr2021", "MP_Dose1_jan2021", "MP_Dose1_feb2021", "MP_Dose1_mar2021", "MP_Dose1_apr2021", "MP_Dose1_may2021", "MP_Dose1_jun2021", "MP_Dose1_jul2021", "MP_Dose1_aug2021", "PR_Dose1_jan2021", "PR_Dose1_feb2021", "PR_Dose1_mar2021", "PR_Dose1_apr2021", "PR_Dose1_may2021", "PR_Dose1_jun2021", "PR_Dose1_jul2021", "PR_Dose1_aug2021", "VI_Dose1_jan2021", "VI_Dose1_feb2021", "VI_Dose1_mar2021", "VI_Dose1_apr2021", "VI_Dose1_may2021", "VI_Dose1_jun2021", "VI_Dose1_jul2021", "VI_Dose1_aug2021"] variant: - template: Stacked + template: StackedModifier scenarios: ["variantR0adj_Week2", "variantR0adj_Week4", "variantR0adj_Week5", "variantR0adj_Week6", "variantR0adj_Week7", "variantR0adj_Week8", "variantR0adj_Week9", "variantR0adj_Week10", "variantR0adj_Week11", "variantR0adj_Week12", "variantR0adj_Week13", "variantR0adj_Week14", "variantR0adj_Week15", "variantR0adj_Week16", "variantR0adj_Week17", "variantR0adj_Week18", "variantR0adj_Week22", "variantR0adj_Week23", "variantR0adj_Week24", "variantR0adj_Week25", "variantR0adj_Week26", "variantR0adj_Week27", "variantR0adj_Week28", "variantR0adj_Week29", "variantR0adj_Week30", "variantR0adj_Week31"] inference: - template: Stacked + template: StackedModifier scenarios: ["local_variance", "NPI", "seasonal", "vaccination", "variant"] AL_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -6650,9 +6648,9 @@ interventions: a: 0 b: 1 AL_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -6662,9 +6660,9 @@ interventions: a: 0 b: 1 AL_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -6674,9 +6672,9 @@ interventions: a: 0 b: 1 AL_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -6686,9 +6684,9 @@ interventions: a: 0 b: 1 AL_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -6698,9 +6696,9 @@ interventions: a: 0 b: 1 AL_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -6710,9 +6708,9 @@ interventions: a: 0 b: 1 AL_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -6722,9 +6720,9 @@ interventions: a: 0 b: 1 AL_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -6734,9 +6732,9 @@ interventions: a: 0 b: 1 AK_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -6746,9 +6744,9 @@ interventions: a: 0 b: 1 AK_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -6758,9 +6756,9 @@ interventions: a: 0 b: 1 AK_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -6770,9 +6768,9 @@ interventions: a: 0 b: 1 AK_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -6782,9 +6780,9 @@ interventions: a: 0 b: 1 AK_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -6794,9 +6792,9 @@ interventions: a: 0 b: 1 AK_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -6806,9 +6804,9 @@ interventions: a: 0 b: 1 AK_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -6818,9 +6816,9 @@ interventions: a: 0 b: 1 AK_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -6830,9 +6828,9 @@ interventions: a: 0 b: 1 AZ_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -6842,9 +6840,9 @@ interventions: a: 0 b: 1 AZ_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -6854,9 +6852,9 @@ interventions: a: 0 b: 1 AZ_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -6866,9 +6864,9 @@ interventions: a: 0 b: 1 AZ_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -6878,9 +6876,9 @@ interventions: a: 0 b: 1 AZ_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -6890,9 +6888,9 @@ interventions: a: 0 b: 1 AZ_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -6902,9 +6900,9 @@ interventions: a: 0 b: 1 AZ_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -6914,9 +6912,9 @@ interventions: a: 0 b: 1 AZ_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -6926,9 +6924,9 @@ interventions: a: 0 b: 1 AR_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -6938,9 +6936,9 @@ interventions: a: 0 b: 1 AR_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -6950,9 +6948,9 @@ interventions: a: 0 b: 1 AR_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -6962,9 +6960,9 @@ interventions: a: 0 b: 1 AR_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -6974,9 +6972,9 @@ interventions: a: 0 b: 1 AR_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -6986,9 +6984,9 @@ interventions: a: 0 b: 1 AR_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -6998,9 +6996,9 @@ interventions: a: 0 b: 1 AR_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -7010,9 +7008,9 @@ interventions: a: 0 b: 1 AR_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -7022,9 +7020,9 @@ interventions: a: 0 b: 1 CA_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -7034,9 +7032,9 @@ interventions: a: 0 b: 1 CA_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -7046,9 +7044,9 @@ interventions: a: 0 b: 1 CA_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -7058,9 +7056,9 @@ interventions: a: 0 b: 1 CA_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -7070,9 +7068,9 @@ interventions: a: 0 b: 1 CA_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -7082,9 +7080,9 @@ interventions: a: 0 b: 1 CA_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -7094,9 +7092,9 @@ interventions: a: 0 b: 1 CA_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -7106,9 +7104,9 @@ interventions: a: 0 b: 1 CA_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -7118,9 +7116,9 @@ interventions: a: 0 b: 1 CO_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -7130,9 +7128,9 @@ interventions: a: 0 b: 1 CO_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -7142,9 +7140,9 @@ interventions: a: 0 b: 1 CO_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -7154,9 +7152,9 @@ interventions: a: 0 b: 1 CO_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -7166,9 +7164,9 @@ interventions: a: 0 b: 1 CO_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -7178,9 +7176,9 @@ interventions: a: 0 b: 1 CO_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -7190,9 +7188,9 @@ interventions: a: 0 b: 1 CO_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -7202,9 +7200,9 @@ interventions: a: 0 b: 1 CO_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -7214,9 +7212,9 @@ interventions: a: 0 b: 1 CT_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -7226,9 +7224,9 @@ interventions: a: 0 b: 1 CT_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -7238,9 +7236,9 @@ interventions: a: 0 b: 1 CT_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -7250,9 +7248,9 @@ interventions: a: 0 b: 1 CT_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -7262,9 +7260,9 @@ interventions: a: 0 b: 1 CT_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -7274,9 +7272,9 @@ interventions: a: 0 b: 1 CT_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -7286,9 +7284,9 @@ interventions: a: 0 b: 1 CT_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -7298,9 +7296,9 @@ interventions: a: 0 b: 1 CT_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -7310,9 +7308,9 @@ interventions: a: 0 b: 1 DE_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -7322,9 +7320,9 @@ interventions: a: 0 b: 1 DE_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -7334,9 +7332,9 @@ interventions: a: 0 b: 1 DE_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -7346,9 +7344,9 @@ interventions: a: 0 b: 1 DE_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -7358,9 +7356,9 @@ interventions: a: 0 b: 1 DE_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -7370,9 +7368,9 @@ interventions: a: 0 b: 1 DE_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -7382,9 +7380,9 @@ interventions: a: 0 b: 1 DE_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -7394,9 +7392,9 @@ interventions: a: 0 b: 1 DE_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -7406,9 +7404,9 @@ interventions: a: 0 b: 1 DC_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -7418,9 +7416,9 @@ interventions: a: 0 b: 1 DC_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -7430,9 +7428,9 @@ interventions: a: 0 b: 1 DC_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -7442,9 +7440,9 @@ interventions: a: 0 b: 1 DC_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -7454,9 +7452,9 @@ interventions: a: 0 b: 1 DC_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -7466,9 +7464,9 @@ interventions: a: 0 b: 1 DC_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -7478,9 +7476,9 @@ interventions: a: 0 b: 1 DC_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -7490,9 +7488,9 @@ interventions: a: 0 b: 1 DC_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -7502,9 +7500,9 @@ interventions: a: 0 b: 1 FL_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -7514,9 +7512,9 @@ interventions: a: 0 b: 1 FL_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -7526,9 +7524,9 @@ interventions: a: 0 b: 1 FL_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -7538,9 +7536,9 @@ interventions: a: 0 b: 1 FL_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -7550,9 +7548,9 @@ interventions: a: 0 b: 1 FL_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -7562,9 +7560,9 @@ interventions: a: 0 b: 1 FL_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -7574,9 +7572,9 @@ interventions: a: 0 b: 1 FL_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -7586,9 +7584,9 @@ interventions: a: 0 b: 1 FL_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -7598,9 +7596,9 @@ interventions: a: 0 b: 1 GA_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -7610,9 +7608,9 @@ interventions: a: 0 b: 1 GA_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -7622,9 +7620,9 @@ interventions: a: 0 b: 1 GA_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -7634,9 +7632,9 @@ interventions: a: 0 b: 1 GA_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -7646,9 +7644,9 @@ interventions: a: 0 b: 1 GA_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -7658,9 +7656,9 @@ interventions: a: 0 b: 1 GA_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -7670,9 +7668,9 @@ interventions: a: 0 b: 1 GA_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -7682,9 +7680,9 @@ interventions: a: 0 b: 1 GA_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -7694,9 +7692,9 @@ interventions: a: 0 b: 1 HI_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -7706,9 +7704,9 @@ interventions: a: 0 b: 1 HI_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -7718,9 +7716,9 @@ interventions: a: 0 b: 1 HI_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -7730,9 +7728,9 @@ interventions: a: 0 b: 1 HI_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -7742,9 +7740,9 @@ interventions: a: 0 b: 1 HI_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -7754,9 +7752,9 @@ interventions: a: 0 b: 1 HI_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -7766,9 +7764,9 @@ interventions: a: 0 b: 1 HI_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -7778,9 +7776,9 @@ interventions: a: 0 b: 1 HI_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -7790,9 +7788,9 @@ interventions: a: 0 b: 1 ID_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -7802,9 +7800,9 @@ interventions: a: 0 b: 1 ID_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -7814,9 +7812,9 @@ interventions: a: 0 b: 1 ID_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -7826,9 +7824,9 @@ interventions: a: 0 b: 1 ID_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -7838,9 +7836,9 @@ interventions: a: 0 b: 1 ID_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -7850,9 +7848,9 @@ interventions: a: 0 b: 1 ID_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -7862,9 +7860,9 @@ interventions: a: 0 b: 1 ID_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -7874,9 +7872,9 @@ interventions: a: 0 b: 1 ID_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -7886,9 +7884,9 @@ interventions: a: 0 b: 1 IL_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -7898,9 +7896,9 @@ interventions: a: 0 b: 1 IL_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -7910,9 +7908,9 @@ interventions: a: 0 b: 1 IL_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -7922,9 +7920,9 @@ interventions: a: 0 b: 1 IL_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -7934,9 +7932,9 @@ interventions: a: 0 b: 1 IL_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -7946,9 +7944,9 @@ interventions: a: 0 b: 1 IL_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -7958,9 +7956,9 @@ interventions: a: 0 b: 1 IL_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -7970,9 +7968,9 @@ interventions: a: 0 b: 1 IL_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -7982,9 +7980,9 @@ interventions: a: 0 b: 1 IN_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -7994,9 +7992,9 @@ interventions: a: 0 b: 1 IN_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -8006,9 +8004,9 @@ interventions: a: 0 b: 1 IN_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -8018,9 +8016,9 @@ interventions: a: 0 b: 1 IN_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -8030,9 +8028,9 @@ interventions: a: 0 b: 1 IN_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -8042,9 +8040,9 @@ interventions: a: 0 b: 1 IN_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -8054,9 +8052,9 @@ interventions: a: 0 b: 1 IN_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -8066,9 +8064,9 @@ interventions: a: 0 b: 1 IN_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -8078,9 +8076,9 @@ interventions: a: 0 b: 1 IA_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -8090,9 +8088,9 @@ interventions: a: 0 b: 1 IA_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -8102,9 +8100,9 @@ interventions: a: 0 b: 1 IA_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -8114,9 +8112,9 @@ interventions: a: 0 b: 1 IA_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -8126,9 +8124,9 @@ interventions: a: 0 b: 1 IA_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -8138,9 +8136,9 @@ interventions: a: 0 b: 1 IA_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -8150,9 +8148,9 @@ interventions: a: 0 b: 1 IA_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -8162,9 +8160,9 @@ interventions: a: 0 b: 1 IA_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -8174,9 +8172,9 @@ interventions: a: 0 b: 1 KS_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -8186,9 +8184,9 @@ interventions: a: 0 b: 1 KS_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -8198,9 +8196,9 @@ interventions: a: 0 b: 1 KS_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -8210,9 +8208,9 @@ interventions: a: 0 b: 1 KS_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -8222,9 +8220,9 @@ interventions: a: 0 b: 1 KS_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -8234,9 +8232,9 @@ interventions: a: 0 b: 1 KS_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -8246,9 +8244,9 @@ interventions: a: 0 b: 1 KS_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -8258,9 +8256,9 @@ interventions: a: 0 b: 1 KS_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -8270,9 +8268,9 @@ interventions: a: 0 b: 1 KY_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -8282,9 +8280,9 @@ interventions: a: 0 b: 1 KY_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -8294,9 +8292,9 @@ interventions: a: 0 b: 1 KY_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -8306,9 +8304,9 @@ interventions: a: 0 b: 1 KY_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -8318,9 +8316,9 @@ interventions: a: 0 b: 1 KY_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -8330,9 +8328,9 @@ interventions: a: 0 b: 1 KY_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -8342,9 +8340,9 @@ interventions: a: 0 b: 1 KY_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -8354,9 +8352,9 @@ interventions: a: 0 b: 1 KY_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -8366,9 +8364,9 @@ interventions: a: 0 b: 1 LA_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -8378,9 +8376,9 @@ interventions: a: 0 b: 1 LA_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -8390,9 +8388,9 @@ interventions: a: 0 b: 1 LA_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -8402,9 +8400,9 @@ interventions: a: 0 b: 1 LA_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -8414,9 +8412,9 @@ interventions: a: 0 b: 1 LA_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -8426,9 +8424,9 @@ interventions: a: 0 b: 1 LA_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -8438,9 +8436,9 @@ interventions: a: 0 b: 1 LA_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -8450,9 +8448,9 @@ interventions: a: 0 b: 1 LA_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -8462,9 +8460,9 @@ interventions: a: 0 b: 1 ME_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -8474,9 +8472,9 @@ interventions: a: 0 b: 1 ME_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -8486,9 +8484,9 @@ interventions: a: 0 b: 1 ME_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -8498,9 +8496,9 @@ interventions: a: 0 b: 1 ME_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -8510,9 +8508,9 @@ interventions: a: 0 b: 1 ME_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -8522,9 +8520,9 @@ interventions: a: 0 b: 1 ME_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -8534,9 +8532,9 @@ interventions: a: 0 b: 1 ME_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -8546,9 +8544,9 @@ interventions: a: 0 b: 1 ME_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -8558,9 +8556,9 @@ interventions: a: 0 b: 1 MD_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -8570,9 +8568,9 @@ interventions: a: 0 b: 1 MD_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -8582,9 +8580,9 @@ interventions: a: 0 b: 1 MD_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -8594,9 +8592,9 @@ interventions: a: 0 b: 1 MD_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -8606,9 +8604,9 @@ interventions: a: 0 b: 1 MD_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -8618,9 +8616,9 @@ interventions: a: 0 b: 1 MD_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -8630,9 +8628,9 @@ interventions: a: 0 b: 1 MD_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -8642,9 +8640,9 @@ interventions: a: 0 b: 1 MD_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -8654,9 +8652,9 @@ interventions: a: 0 b: 1 MA_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -8666,9 +8664,9 @@ interventions: a: 0 b: 1 MA_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -8678,9 +8676,9 @@ interventions: a: 0 b: 1 MA_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -8690,9 +8688,9 @@ interventions: a: 0 b: 1 MA_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -8702,9 +8700,9 @@ interventions: a: 0 b: 1 MA_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -8714,9 +8712,9 @@ interventions: a: 0 b: 1 MA_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -8726,9 +8724,9 @@ interventions: a: 0 b: 1 MA_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -8738,9 +8736,9 @@ interventions: a: 0 b: 1 MA_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -8750,9 +8748,9 @@ interventions: a: 0 b: 1 MI_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -8762,9 +8760,9 @@ interventions: a: 0 b: 1 MI_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -8774,9 +8772,9 @@ interventions: a: 0 b: 1 MI_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -8786,9 +8784,9 @@ interventions: a: 0 b: 1 MI_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -8798,9 +8796,9 @@ interventions: a: 0 b: 1 MI_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -8810,9 +8808,9 @@ interventions: a: 0 b: 1 MI_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -8822,9 +8820,9 @@ interventions: a: 0 b: 1 MI_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -8834,9 +8832,9 @@ interventions: a: 0 b: 1 MI_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -8846,9 +8844,9 @@ interventions: a: 0 b: 1 MN_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -8858,9 +8856,9 @@ interventions: a: 0 b: 1 MN_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -8870,9 +8868,9 @@ interventions: a: 0 b: 1 MN_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -8882,9 +8880,9 @@ interventions: a: 0 b: 1 MN_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -8894,9 +8892,9 @@ interventions: a: 0 b: 1 MN_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -8906,9 +8904,9 @@ interventions: a: 0 b: 1 MN_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -8918,9 +8916,9 @@ interventions: a: 0 b: 1 MN_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -8930,9 +8928,9 @@ interventions: a: 0 b: 1 MN_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -8942,9 +8940,9 @@ interventions: a: 0 b: 1 MS_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -8954,9 +8952,9 @@ interventions: a: 0 b: 1 MS_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -8966,9 +8964,9 @@ interventions: a: 0 b: 1 MS_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -8978,9 +8976,9 @@ interventions: a: 0 b: 1 MS_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -8990,9 +8988,9 @@ interventions: a: 0 b: 1 MS_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -9002,9 +9000,9 @@ interventions: a: 0 b: 1 MS_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -9014,9 +9012,9 @@ interventions: a: 0 b: 1 MS_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -9026,9 +9024,9 @@ interventions: a: 0 b: 1 MS_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -9038,9 +9036,9 @@ interventions: a: 0 b: 1 MO_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -9050,9 +9048,9 @@ interventions: a: 0 b: 1 MO_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -9062,9 +9060,9 @@ interventions: a: 0 b: 1 MO_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -9074,9 +9072,9 @@ interventions: a: 0 b: 1 MO_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -9086,9 +9084,9 @@ interventions: a: 0 b: 1 MO_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -9098,9 +9096,9 @@ interventions: a: 0 b: 1 MO_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -9110,9 +9108,9 @@ interventions: a: 0 b: 1 MO_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -9122,9 +9120,9 @@ interventions: a: 0 b: 1 MO_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -9134,9 +9132,9 @@ interventions: a: 0 b: 1 MT_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -9146,9 +9144,9 @@ interventions: a: 0 b: 1 MT_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -9158,9 +9156,9 @@ interventions: a: 0 b: 1 MT_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -9170,9 +9168,9 @@ interventions: a: 0 b: 1 MT_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -9182,9 +9180,9 @@ interventions: a: 0 b: 1 MT_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -9194,9 +9192,9 @@ interventions: a: 0 b: 1 MT_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -9206,9 +9204,9 @@ interventions: a: 0 b: 1 MT_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -9218,9 +9216,9 @@ interventions: a: 0 b: 1 MT_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -9230,9 +9228,9 @@ interventions: a: 0 b: 1 NE_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -9242,9 +9240,9 @@ interventions: a: 0 b: 1 NE_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -9254,9 +9252,9 @@ interventions: a: 0 b: 1 NE_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -9266,9 +9264,9 @@ interventions: a: 0 b: 1 NE_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -9278,9 +9276,9 @@ interventions: a: 0 b: 1 NE_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -9290,9 +9288,9 @@ interventions: a: 0 b: 1 NE_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -9302,9 +9300,9 @@ interventions: a: 0 b: 1 NE_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -9314,9 +9312,9 @@ interventions: a: 0 b: 1 NE_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -9326,9 +9324,9 @@ interventions: a: 0 b: 1 NV_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -9338,9 +9336,9 @@ interventions: a: 0 b: 1 NV_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -9350,9 +9348,9 @@ interventions: a: 0 b: 1 NV_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -9362,9 +9360,9 @@ interventions: a: 0 b: 1 NV_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -9374,9 +9372,9 @@ interventions: a: 0 b: 1 NV_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -9386,9 +9384,9 @@ interventions: a: 0 b: 1 NV_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -9398,9 +9396,9 @@ interventions: a: 0 b: 1 NV_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -9410,9 +9408,9 @@ interventions: a: 0 b: 1 NV_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -9422,9 +9420,9 @@ interventions: a: 0 b: 1 NH_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -9434,9 +9432,9 @@ interventions: a: 0 b: 1 NH_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -9446,9 +9444,9 @@ interventions: a: 0 b: 1 NH_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -9458,9 +9456,9 @@ interventions: a: 0 b: 1 NH_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -9470,9 +9468,9 @@ interventions: a: 0 b: 1 NH_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -9482,9 +9480,9 @@ interventions: a: 0 b: 1 NH_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -9494,9 +9492,9 @@ interventions: a: 0 b: 1 NH_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -9506,9 +9504,9 @@ interventions: a: 0 b: 1 NH_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -9518,9 +9516,9 @@ interventions: a: 0 b: 1 NJ_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -9530,9 +9528,9 @@ interventions: a: 0 b: 1 NJ_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -9542,9 +9540,9 @@ interventions: a: 0 b: 1 NJ_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -9554,9 +9552,9 @@ interventions: a: 0 b: 1 NJ_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -9566,9 +9564,9 @@ interventions: a: 0 b: 1 NJ_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -9578,9 +9576,9 @@ interventions: a: 0 b: 1 NJ_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -9590,9 +9588,9 @@ interventions: a: 0 b: 1 NJ_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -9602,9 +9600,9 @@ interventions: a: 0 b: 1 NJ_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -9614,9 +9612,9 @@ interventions: a: 0 b: 1 NM_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -9626,9 +9624,9 @@ interventions: a: 0 b: 1 NM_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -9638,9 +9636,9 @@ interventions: a: 0 b: 1 NM_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -9650,9 +9648,9 @@ interventions: a: 0 b: 1 NM_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -9662,9 +9660,9 @@ interventions: a: 0 b: 1 NM_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -9674,9 +9672,9 @@ interventions: a: 0 b: 1 NM_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -9686,9 +9684,9 @@ interventions: a: 0 b: 1 NM_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -9698,9 +9696,9 @@ interventions: a: 0 b: 1 NM_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -9710,9 +9708,9 @@ interventions: a: 0 b: 1 NY_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -9722,9 +9720,9 @@ interventions: a: 0 b: 1 NY_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -9734,9 +9732,9 @@ interventions: a: 0 b: 1 NY_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -9746,9 +9744,9 @@ interventions: a: 0 b: 1 NY_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -9758,9 +9756,9 @@ interventions: a: 0 b: 1 NY_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -9770,9 +9768,9 @@ interventions: a: 0 b: 1 NY_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -9782,9 +9780,9 @@ interventions: a: 0 b: 1 NY_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -9794,9 +9792,9 @@ interventions: a: 0 b: 1 NY_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -9806,9 +9804,9 @@ interventions: a: 0 b: 1 NC_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -9818,9 +9816,9 @@ interventions: a: 0 b: 1 NC_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -9830,9 +9828,9 @@ interventions: a: 0 b: 1 NC_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -9842,9 +9840,9 @@ interventions: a: 0 b: 1 NC_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -9854,9 +9852,9 @@ interventions: a: 0 b: 1 NC_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -9866,9 +9864,9 @@ interventions: a: 0 b: 1 NC_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -9878,9 +9876,9 @@ interventions: a: 0 b: 1 NC_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -9890,9 +9888,9 @@ interventions: a: 0 b: 1 NC_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -9902,9 +9900,9 @@ interventions: a: 0 b: 1 ND_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -9914,9 +9912,9 @@ interventions: a: 0 b: 1 ND_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -9926,9 +9924,9 @@ interventions: a: 0 b: 1 ND_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -9938,9 +9936,9 @@ interventions: a: 0 b: 1 ND_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -9950,9 +9948,9 @@ interventions: a: 0 b: 1 ND_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -9962,9 +9960,9 @@ interventions: a: 0 b: 1 ND_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -9974,9 +9972,9 @@ interventions: a: 0 b: 1 ND_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -9986,9 +9984,9 @@ interventions: a: 0 b: 1 ND_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -9998,9 +9996,9 @@ interventions: a: 0 b: 1 OH_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -10010,9 +10008,9 @@ interventions: a: 0 b: 1 OH_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -10022,9 +10020,9 @@ interventions: a: 0 b: 1 OH_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10034,9 +10032,9 @@ interventions: a: 0 b: 1 OH_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -10046,9 +10044,9 @@ interventions: a: 0 b: 1 OH_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -10058,9 +10056,9 @@ interventions: a: 0 b: 1 OH_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -10070,9 +10068,9 @@ interventions: a: 0 b: 1 OH_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -10082,9 +10080,9 @@ interventions: a: 0 b: 1 OH_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -10094,9 +10092,9 @@ interventions: a: 0 b: 1 OK_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -10106,9 +10104,9 @@ interventions: a: 0 b: 1 OK_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -10118,9 +10116,9 @@ interventions: a: 0 b: 1 OK_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10130,9 +10128,9 @@ interventions: a: 0 b: 1 OK_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -10142,9 +10140,9 @@ interventions: a: 0 b: 1 OK_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -10154,9 +10152,9 @@ interventions: a: 0 b: 1 OK_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -10166,9 +10164,9 @@ interventions: a: 0 b: 1 OK_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -10178,9 +10176,9 @@ interventions: a: 0 b: 1 OK_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -10190,9 +10188,9 @@ interventions: a: 0 b: 1 OR_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -10202,9 +10200,9 @@ interventions: a: 0 b: 1 OR_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -10214,9 +10212,9 @@ interventions: a: 0 b: 1 OR_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10226,9 +10224,9 @@ interventions: a: 0 b: 1 OR_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -10238,9 +10236,9 @@ interventions: a: 0 b: 1 OR_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -10250,9 +10248,9 @@ interventions: a: 0 b: 1 OR_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -10262,9 +10260,9 @@ interventions: a: 0 b: 1 OR_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -10274,9 +10272,9 @@ interventions: a: 0 b: 1 OR_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -10286,9 +10284,9 @@ interventions: a: 0 b: 1 PA_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -10298,9 +10296,9 @@ interventions: a: 0 b: 1 PA_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -10310,9 +10308,9 @@ interventions: a: 0 b: 1 PA_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10322,9 +10320,9 @@ interventions: a: 0 b: 1 PA_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -10334,9 +10332,9 @@ interventions: a: 0 b: 1 PA_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -10346,9 +10344,9 @@ interventions: a: 0 b: 1 PA_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -10358,9 +10356,9 @@ interventions: a: 0 b: 1 PA_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -10370,9 +10368,9 @@ interventions: a: 0 b: 1 PA_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -10382,9 +10380,9 @@ interventions: a: 0 b: 1 RI_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -10394,9 +10392,9 @@ interventions: a: 0 b: 1 RI_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -10406,9 +10404,9 @@ interventions: a: 0 b: 1 RI_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10418,9 +10416,9 @@ interventions: a: 0 b: 1 RI_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -10430,9 +10428,9 @@ interventions: a: 0 b: 1 RI_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -10442,9 +10440,9 @@ interventions: a: 0 b: 1 RI_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -10454,9 +10452,9 @@ interventions: a: 0 b: 1 RI_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -10466,9 +10464,9 @@ interventions: a: 0 b: 1 RI_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -10478,9 +10476,9 @@ interventions: a: 0 b: 1 SC_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -10490,9 +10488,9 @@ interventions: a: 0 b: 1 SC_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -10502,9 +10500,9 @@ interventions: a: 0 b: 1 SC_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10514,9 +10512,9 @@ interventions: a: 0 b: 1 SC_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -10526,9 +10524,9 @@ interventions: a: 0 b: 1 SC_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -10538,9 +10536,9 @@ interventions: a: 0 b: 1 SC_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -10550,9 +10548,9 @@ interventions: a: 0 b: 1 SC_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -10562,9 +10560,9 @@ interventions: a: 0 b: 1 SC_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -10574,9 +10572,9 @@ interventions: a: 0 b: 1 SD_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -10586,9 +10584,9 @@ interventions: a: 0 b: 1 SD_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -10598,9 +10596,9 @@ interventions: a: 0 b: 1 SD_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10610,9 +10608,9 @@ interventions: a: 0 b: 1 SD_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -10622,9 +10620,9 @@ interventions: a: 0 b: 1 SD_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -10634,9 +10632,9 @@ interventions: a: 0 b: 1 SD_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -10646,9 +10644,9 @@ interventions: a: 0 b: 1 SD_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -10658,9 +10656,9 @@ interventions: a: 0 b: 1 SD_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -10670,9 +10668,9 @@ interventions: a: 0 b: 1 TN_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -10682,9 +10680,9 @@ interventions: a: 0 b: 1 TN_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -10694,9 +10692,9 @@ interventions: a: 0 b: 1 TN_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10706,9 +10704,9 @@ interventions: a: 0 b: 1 TN_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -10718,9 +10716,9 @@ interventions: a: 0 b: 1 TN_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -10730,9 +10728,9 @@ interventions: a: 0 b: 1 TN_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -10742,9 +10740,9 @@ interventions: a: 0 b: 1 TN_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -10754,9 +10752,9 @@ interventions: a: 0 b: 1 TN_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -10766,9 +10764,9 @@ interventions: a: 0 b: 1 TX_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -10778,9 +10776,9 @@ interventions: a: 0 b: 1 TX_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -10790,9 +10788,9 @@ interventions: a: 0 b: 1 TX_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10802,9 +10800,9 @@ interventions: a: 0 b: 1 TX_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -10814,9 +10812,9 @@ interventions: a: 0 b: 1 TX_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -10826,9 +10824,9 @@ interventions: a: 0 b: 1 TX_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -10838,9 +10836,9 @@ interventions: a: 0 b: 1 TX_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -10850,9 +10848,9 @@ interventions: a: 0 b: 1 TX_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -10862,9 +10860,9 @@ interventions: a: 0 b: 1 UT_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -10874,9 +10872,9 @@ interventions: a: 0 b: 1 UT_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -10886,9 +10884,9 @@ interventions: a: 0 b: 1 UT_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10898,9 +10896,9 @@ interventions: a: 0 b: 1 UT_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -10910,9 +10908,9 @@ interventions: a: 0 b: 1 UT_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -10922,9 +10920,9 @@ interventions: a: 0 b: 1 UT_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -10934,9 +10932,9 @@ interventions: a: 0 b: 1 UT_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -10946,9 +10944,9 @@ interventions: a: 0 b: 1 UT_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -10958,9 +10956,9 @@ interventions: a: 0 b: 1 VT_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -10970,9 +10968,9 @@ interventions: a: 0 b: 1 VT_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -10982,9 +10980,9 @@ interventions: a: 0 b: 1 VT_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10994,9 +10992,9 @@ interventions: a: 0 b: 1 VT_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -11006,9 +11004,9 @@ interventions: a: 0 b: 1 VT_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -11018,9 +11016,9 @@ interventions: a: 0 b: 1 VT_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -11030,9 +11028,9 @@ interventions: a: 0 b: 1 VT_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -11042,9 +11040,9 @@ interventions: a: 0 b: 1 VT_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -11054,9 +11052,9 @@ interventions: a: 0 b: 1 VA_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -11066,9 +11064,9 @@ interventions: a: 0 b: 1 VA_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -11078,9 +11076,9 @@ interventions: a: 0 b: 1 VA_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -11090,9 +11088,9 @@ interventions: a: 0 b: 1 VA_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -11102,9 +11100,9 @@ interventions: a: 0 b: 1 VA_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -11114,9 +11112,9 @@ interventions: a: 0 b: 1 VA_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -11126,9 +11124,9 @@ interventions: a: 0 b: 1 VA_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -11138,9 +11136,9 @@ interventions: a: 0 b: 1 VA_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -11150,9 +11148,9 @@ interventions: a: 0 b: 1 WA_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -11162,9 +11160,9 @@ interventions: a: 0 b: 1 WA_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -11174,9 +11172,9 @@ interventions: a: 0 b: 1 WA_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -11186,9 +11184,9 @@ interventions: a: 0 b: 1 WA_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -11198,9 +11196,9 @@ interventions: a: 0 b: 1 WA_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -11210,9 +11208,9 @@ interventions: a: 0 b: 1 WA_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -11222,9 +11220,9 @@ interventions: a: 0 b: 1 WA_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -11234,9 +11232,9 @@ interventions: a: 0 b: 1 WA_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -11246,9 +11244,9 @@ interventions: a: 0 b: 1 WV_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -11258,9 +11256,9 @@ interventions: a: 0 b: 1 WV_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -11270,9 +11268,9 @@ interventions: a: 0 b: 1 WV_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -11282,9 +11280,9 @@ interventions: a: 0 b: 1 WV_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -11294,9 +11292,9 @@ interventions: a: 0 b: 1 WV_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -11306,9 +11304,9 @@ interventions: a: 0 b: 1 WV_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -11318,9 +11316,9 @@ interventions: a: 0 b: 1 WV_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -11330,9 +11328,9 @@ interventions: a: 0 b: 1 WV_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -11342,9 +11340,9 @@ interventions: a: 0 b: 1 WI_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -11354,9 +11352,9 @@ interventions: a: 0 b: 1 WI_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -11366,9 +11364,9 @@ interventions: a: 0 b: 1 WI_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -11378,9 +11376,9 @@ interventions: a: 0 b: 1 WI_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -11390,9 +11388,9 @@ interventions: a: 0 b: 1 WI_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -11402,9 +11400,9 @@ interventions: a: 0 b: 1 WI_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -11414,9 +11412,9 @@ interventions: a: 0 b: 1 WI_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -11426,9 +11424,9 @@ interventions: a: 0 b: 1 WI_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -11438,9 +11436,9 @@ interventions: a: 0 b: 1 WY_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -11450,9 +11448,9 @@ interventions: a: 0 b: 1 WY_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -11462,9 +11460,9 @@ interventions: a: 0 b: 1 WY_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -11474,9 +11472,9 @@ interventions: a: 0 b: 1 WY_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -11486,9 +11484,9 @@ interventions: a: 0 b: 1 WY_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -11498,9 +11496,9 @@ interventions: a: 0 b: 1 WY_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -11510,9 +11508,9 @@ interventions: a: 0 b: 1 WY_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -11522,9 +11520,9 @@ interventions: a: 0 b: 1 WY_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -11534,9 +11532,9 @@ interventions: a: 0 b: 1 GU_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -11546,9 +11544,9 @@ interventions: a: 0 b: 1 GU_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -11558,9 +11556,9 @@ interventions: a: 0 b: 1 GU_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -11570,9 +11568,9 @@ interventions: a: 0 b: 1 GU_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -11582,9 +11580,9 @@ interventions: a: 0 b: 1 GU_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -11594,9 +11592,9 @@ interventions: a: 0 b: 1 GU_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -11606,9 +11604,9 @@ interventions: a: 0 b: 1 GU_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -11618,9 +11616,9 @@ interventions: a: 0 b: 1 GU_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["66000"] + subpop: ["66000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -11630,9 +11628,9 @@ interventions: a: 0 b: 1 MP_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -11642,9 +11640,9 @@ interventions: a: 0 b: 1 MP_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -11654,9 +11652,9 @@ interventions: a: 0 b: 1 MP_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -11666,9 +11664,9 @@ interventions: a: 0 b: 1 MP_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -11678,9 +11676,9 @@ interventions: a: 0 b: 1 MP_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -11690,9 +11688,9 @@ interventions: a: 0 b: 1 MP_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -11702,9 +11700,9 @@ interventions: a: 0 b: 1 MP_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -11714,9 +11712,9 @@ interventions: a: 0 b: 1 MP_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["69000"] + subpop: ["69000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -11726,9 +11724,9 @@ interventions: a: 0 b: 1 PR_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -11738,9 +11736,9 @@ interventions: a: 0 b: 1 PR_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -11750,9 +11748,9 @@ interventions: a: 0 b: 1 PR_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -11762,9 +11760,9 @@ interventions: a: 0 b: 1 PR_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -11774,9 +11772,9 @@ interventions: a: 0 b: 1 PR_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -11786,9 +11784,9 @@ interventions: a: 0 b: 1 PR_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -11798,9 +11796,9 @@ interventions: a: 0 b: 1 PR_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -11810,9 +11808,9 @@ interventions: a: 0 b: 1 PR_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["72000"] + subpop: ["72000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -11822,9 +11820,9 @@ interventions: a: 0 b: 1 VI_incidD_vaccadj_jan2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: @@ -11834,9 +11832,9 @@ interventions: a: 0 b: 1 VI_incidD_vaccadj_feb2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: @@ -11846,9 +11844,9 @@ interventions: a: 0 b: 1 VI_incidD_vaccadj_mar2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -11858,9 +11856,9 @@ interventions: a: 0 b: 1 VI_incidD_vaccadj_apr2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: @@ -11870,9 +11868,9 @@ interventions: a: 0 b: 1 VI_incidD_vaccadj_may2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: @@ -11882,9 +11880,9 @@ interventions: a: 0 b: 1 VI_incidD_vaccadj_jun2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: @@ -11894,9 +11892,9 @@ interventions: a: 0 b: 1 VI_incidD_vaccadj_jul2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: @@ -11906,9 +11904,9 @@ interventions: a: 0 b: 1 VI_incidD_vaccadj_aug2021: - template: Reduce + template: SinglePeriodModifier parameter: incidD::probability - affected_geoids: ["78000"] + subpop: ["78000"] period_start_date: 2021-08-01 period_end_date: 2021-08-07 value: @@ -11921,7 +11919,7 @@ interventions: outcomes: method: delayframe param_from_file: TRUE - param_place_file: "usa-geoid-params-output_statelevel.parquet" + param_place_file: "usa-subpop-params-output_statelevel.parquet" scenarios: - med settings: @@ -12003,7 +12001,7 @@ outcomes: interventions: settings: med: - template: Stacked + template: StackedModifier scenarios: ["AL_incidD_vaccadj_jan2021", "AL_incidD_vaccadj_feb2021", "AL_incidD_vaccadj_mar2021", "AL_incidD_vaccadj_apr2021", "AL_incidD_vaccadj_may2021", "AL_incidD_vaccadj_jun2021", "AL_incidD_vaccadj_jul2021", "AL_incidD_vaccadj_aug2021", "AK_incidD_vaccadj_jan2021", "AK_incidD_vaccadj_feb2021", "AK_incidD_vaccadj_mar2021", "AK_incidD_vaccadj_apr2021", "AK_incidD_vaccadj_may2021", "AK_incidD_vaccadj_jun2021", "AK_incidD_vaccadj_jul2021", "AK_incidD_vaccadj_aug2021", "AZ_incidD_vaccadj_jan2021", "AZ_incidD_vaccadj_feb2021", "AZ_incidD_vaccadj_mar2021", "AZ_incidD_vaccadj_apr2021", "AZ_incidD_vaccadj_may2021", "AZ_incidD_vaccadj_jun2021", "AZ_incidD_vaccadj_jul2021", "AZ_incidD_vaccadj_aug2021", "AR_incidD_vaccadj_jan2021", "AR_incidD_vaccadj_feb2021", "AR_incidD_vaccadj_mar2021", "AR_incidD_vaccadj_apr2021", "AR_incidD_vaccadj_may2021", "AR_incidD_vaccadj_jun2021", "AR_incidD_vaccadj_jul2021", "AR_incidD_vaccadj_aug2021", "CA_incidD_vaccadj_jan2021", "CA_incidD_vaccadj_feb2021", "CA_incidD_vaccadj_mar2021", "CA_incidD_vaccadj_apr2021", "CA_incidD_vaccadj_may2021", "CA_incidD_vaccadj_jun2021", "CA_incidD_vaccadj_jul2021", "CA_incidD_vaccadj_aug2021", "CO_incidD_vaccadj_jan2021", "CO_incidD_vaccadj_feb2021", "CO_incidD_vaccadj_mar2021", "CO_incidD_vaccadj_apr2021", "CO_incidD_vaccadj_may2021", "CO_incidD_vaccadj_jun2021", "CO_incidD_vaccadj_jul2021", "CO_incidD_vaccadj_aug2021", "CT_incidD_vaccadj_jan2021", "CT_incidD_vaccadj_feb2021", "CT_incidD_vaccadj_mar2021", "CT_incidD_vaccadj_apr2021", "CT_incidD_vaccadj_may2021", "CT_incidD_vaccadj_jun2021", "CT_incidD_vaccadj_jul2021", "CT_incidD_vaccadj_aug2021", "DE_incidD_vaccadj_jan2021", "DE_incidD_vaccadj_feb2021", "DE_incidD_vaccadj_mar2021", "DE_incidD_vaccadj_apr2021", "DE_incidD_vaccadj_may2021", "DE_incidD_vaccadj_jun2021", "DE_incidD_vaccadj_jul2021", "DE_incidD_vaccadj_aug2021", "DC_incidD_vaccadj_jan2021", "DC_incidD_vaccadj_feb2021", "DC_incidD_vaccadj_mar2021", "DC_incidD_vaccadj_apr2021", "DC_incidD_vaccadj_may2021", "DC_incidD_vaccadj_jun2021", "DC_incidD_vaccadj_jul2021", "DC_incidD_vaccadj_aug2021", "FL_incidD_vaccadj_jan2021", "FL_incidD_vaccadj_feb2021", "FL_incidD_vaccadj_mar2021", "FL_incidD_vaccadj_apr2021", "FL_incidD_vaccadj_may2021", "FL_incidD_vaccadj_jun2021", "FL_incidD_vaccadj_jul2021", "FL_incidD_vaccadj_aug2021", "GA_incidD_vaccadj_jan2021", "GA_incidD_vaccadj_feb2021", "GA_incidD_vaccadj_mar2021", "GA_incidD_vaccadj_apr2021", "GA_incidD_vaccadj_may2021", "GA_incidD_vaccadj_jun2021", "GA_incidD_vaccadj_jul2021", "GA_incidD_vaccadj_aug2021", "HI_incidD_vaccadj_jan2021", "HI_incidD_vaccadj_feb2021", "HI_incidD_vaccadj_mar2021", "HI_incidD_vaccadj_apr2021", "HI_incidD_vaccadj_may2021", "HI_incidD_vaccadj_jun2021", "HI_incidD_vaccadj_jul2021", "HI_incidD_vaccadj_aug2021", "ID_incidD_vaccadj_jan2021", "ID_incidD_vaccadj_feb2021", "ID_incidD_vaccadj_mar2021", "ID_incidD_vaccadj_apr2021", "ID_incidD_vaccadj_may2021", "ID_incidD_vaccadj_jun2021", "ID_incidD_vaccadj_jul2021", "ID_incidD_vaccadj_aug2021", "IL_incidD_vaccadj_jan2021", "IL_incidD_vaccadj_feb2021", "IL_incidD_vaccadj_mar2021", "IL_incidD_vaccadj_apr2021", "IL_incidD_vaccadj_may2021", "IL_incidD_vaccadj_jun2021", "IL_incidD_vaccadj_jul2021", "IL_incidD_vaccadj_aug2021", "IN_incidD_vaccadj_jan2021", "IN_incidD_vaccadj_feb2021", "IN_incidD_vaccadj_mar2021", "IN_incidD_vaccadj_apr2021", "IN_incidD_vaccadj_may2021", "IN_incidD_vaccadj_jun2021", "IN_incidD_vaccadj_jul2021", "IN_incidD_vaccadj_aug2021", "IA_incidD_vaccadj_jan2021", "IA_incidD_vaccadj_feb2021", "IA_incidD_vaccadj_mar2021", "IA_incidD_vaccadj_apr2021", "IA_incidD_vaccadj_may2021", "IA_incidD_vaccadj_jun2021", "IA_incidD_vaccadj_jul2021", "IA_incidD_vaccadj_aug2021", "KS_incidD_vaccadj_jan2021", "KS_incidD_vaccadj_feb2021", "KS_incidD_vaccadj_mar2021", "KS_incidD_vaccadj_apr2021", "KS_incidD_vaccadj_may2021", "KS_incidD_vaccadj_jun2021", "KS_incidD_vaccadj_jul2021", "KS_incidD_vaccadj_aug2021", "KY_incidD_vaccadj_jan2021", "KY_incidD_vaccadj_feb2021", "KY_incidD_vaccadj_mar2021", "KY_incidD_vaccadj_apr2021", "KY_incidD_vaccadj_may2021", "KY_incidD_vaccadj_jun2021", "KY_incidD_vaccadj_jul2021", "KY_incidD_vaccadj_aug2021", "LA_incidD_vaccadj_jan2021", "LA_incidD_vaccadj_feb2021", "LA_incidD_vaccadj_mar2021", "LA_incidD_vaccadj_apr2021", "LA_incidD_vaccadj_may2021", "LA_incidD_vaccadj_jun2021", "LA_incidD_vaccadj_jul2021", "LA_incidD_vaccadj_aug2021", "ME_incidD_vaccadj_jan2021", "ME_incidD_vaccadj_feb2021", "ME_incidD_vaccadj_mar2021", "ME_incidD_vaccadj_apr2021", "ME_incidD_vaccadj_may2021", "ME_incidD_vaccadj_jun2021", "ME_incidD_vaccadj_jul2021", "ME_incidD_vaccadj_aug2021", "MD_incidD_vaccadj_jan2021", "MD_incidD_vaccadj_feb2021", "MD_incidD_vaccadj_mar2021", "MD_incidD_vaccadj_apr2021", "MD_incidD_vaccadj_may2021", "MD_incidD_vaccadj_jun2021", "MD_incidD_vaccadj_jul2021", "MD_incidD_vaccadj_aug2021", "MA_incidD_vaccadj_jan2021", "MA_incidD_vaccadj_feb2021", "MA_incidD_vaccadj_mar2021", "MA_incidD_vaccadj_apr2021", "MA_incidD_vaccadj_may2021", "MA_incidD_vaccadj_jun2021", "MA_incidD_vaccadj_jul2021", "MA_incidD_vaccadj_aug2021", "MI_incidD_vaccadj_jan2021", "MI_incidD_vaccadj_feb2021", "MI_incidD_vaccadj_mar2021", "MI_incidD_vaccadj_apr2021", "MI_incidD_vaccadj_may2021", "MI_incidD_vaccadj_jun2021", "MI_incidD_vaccadj_jul2021", "MI_incidD_vaccadj_aug2021", "MN_incidD_vaccadj_jan2021", "MN_incidD_vaccadj_feb2021", "MN_incidD_vaccadj_mar2021", "MN_incidD_vaccadj_apr2021", "MN_incidD_vaccadj_may2021", "MN_incidD_vaccadj_jun2021", "MN_incidD_vaccadj_jul2021", "MN_incidD_vaccadj_aug2021", "MS_incidD_vaccadj_jan2021", "MS_incidD_vaccadj_feb2021", "MS_incidD_vaccadj_mar2021", "MS_incidD_vaccadj_apr2021", "MS_incidD_vaccadj_may2021", "MS_incidD_vaccadj_jun2021", "MS_incidD_vaccadj_jul2021", "MS_incidD_vaccadj_aug2021", "MO_incidD_vaccadj_jan2021", "MO_incidD_vaccadj_feb2021", "MO_incidD_vaccadj_mar2021", "MO_incidD_vaccadj_apr2021", "MO_incidD_vaccadj_may2021", "MO_incidD_vaccadj_jun2021", "MO_incidD_vaccadj_jul2021", "MO_incidD_vaccadj_aug2021", "MT_incidD_vaccadj_jan2021", "MT_incidD_vaccadj_feb2021", "MT_incidD_vaccadj_mar2021", "MT_incidD_vaccadj_apr2021", "MT_incidD_vaccadj_may2021", "MT_incidD_vaccadj_jun2021", "MT_incidD_vaccadj_jul2021", "MT_incidD_vaccadj_aug2021", "NE_incidD_vaccadj_jan2021", "NE_incidD_vaccadj_feb2021", "NE_incidD_vaccadj_mar2021", "NE_incidD_vaccadj_apr2021", "NE_incidD_vaccadj_may2021", "NE_incidD_vaccadj_jun2021", "NE_incidD_vaccadj_jul2021", "NE_incidD_vaccadj_aug2021", "NV_incidD_vaccadj_jan2021", "NV_incidD_vaccadj_feb2021", "NV_incidD_vaccadj_mar2021", "NV_incidD_vaccadj_apr2021", "NV_incidD_vaccadj_may2021", "NV_incidD_vaccadj_jun2021", "NV_incidD_vaccadj_jul2021", "NV_incidD_vaccadj_aug2021", "NH_incidD_vaccadj_jan2021", "NH_incidD_vaccadj_feb2021", "NH_incidD_vaccadj_mar2021", "NH_incidD_vaccadj_apr2021", "NH_incidD_vaccadj_may2021", "NH_incidD_vaccadj_jun2021", "NH_incidD_vaccadj_jul2021", "NH_incidD_vaccadj_aug2021", "NJ_incidD_vaccadj_jan2021", "NJ_incidD_vaccadj_feb2021", "NJ_incidD_vaccadj_mar2021", "NJ_incidD_vaccadj_apr2021", "NJ_incidD_vaccadj_may2021", "NJ_incidD_vaccadj_jun2021", "NJ_incidD_vaccadj_jul2021", "NJ_incidD_vaccadj_aug2021", "NM_incidD_vaccadj_jan2021", "NM_incidD_vaccadj_feb2021", "NM_incidD_vaccadj_mar2021", "NM_incidD_vaccadj_apr2021", "NM_incidD_vaccadj_may2021", "NM_incidD_vaccadj_jun2021", "NM_incidD_vaccadj_jul2021", "NM_incidD_vaccadj_aug2021", "NY_incidD_vaccadj_jan2021", "NY_incidD_vaccadj_feb2021", "NY_incidD_vaccadj_mar2021", "NY_incidD_vaccadj_apr2021", "NY_incidD_vaccadj_may2021", "NY_incidD_vaccadj_jun2021", "NY_incidD_vaccadj_jul2021", "NY_incidD_vaccadj_aug2021", "NC_incidD_vaccadj_jan2021", "NC_incidD_vaccadj_feb2021", "NC_incidD_vaccadj_mar2021", "NC_incidD_vaccadj_apr2021", "NC_incidD_vaccadj_may2021", "NC_incidD_vaccadj_jun2021", "NC_incidD_vaccadj_jul2021", "NC_incidD_vaccadj_aug2021", "ND_incidD_vaccadj_jan2021", "ND_incidD_vaccadj_feb2021", "ND_incidD_vaccadj_mar2021", "ND_incidD_vaccadj_apr2021", "ND_incidD_vaccadj_may2021", "ND_incidD_vaccadj_jun2021", "ND_incidD_vaccadj_jul2021", "ND_incidD_vaccadj_aug2021", "OH_incidD_vaccadj_jan2021", "OH_incidD_vaccadj_feb2021", "OH_incidD_vaccadj_mar2021", "OH_incidD_vaccadj_apr2021", "OH_incidD_vaccadj_may2021", "OH_incidD_vaccadj_jun2021", "OH_incidD_vaccadj_jul2021", "OH_incidD_vaccadj_aug2021", "OK_incidD_vaccadj_jan2021", "OK_incidD_vaccadj_feb2021", "OK_incidD_vaccadj_mar2021", "OK_incidD_vaccadj_apr2021", "OK_incidD_vaccadj_may2021", "OK_incidD_vaccadj_jun2021", "OK_incidD_vaccadj_jul2021", "OK_incidD_vaccadj_aug2021", "OR_incidD_vaccadj_jan2021", "OR_incidD_vaccadj_feb2021", "OR_incidD_vaccadj_mar2021", "OR_incidD_vaccadj_apr2021", "OR_incidD_vaccadj_may2021", "OR_incidD_vaccadj_jun2021", "OR_incidD_vaccadj_jul2021", "OR_incidD_vaccadj_aug2021", "PA_incidD_vaccadj_jan2021", "PA_incidD_vaccadj_feb2021", "PA_incidD_vaccadj_mar2021", "PA_incidD_vaccadj_apr2021", "PA_incidD_vaccadj_may2021", "PA_incidD_vaccadj_jun2021", "PA_incidD_vaccadj_jul2021", "PA_incidD_vaccadj_aug2021", "RI_incidD_vaccadj_jan2021", "RI_incidD_vaccadj_feb2021", "RI_incidD_vaccadj_mar2021", "RI_incidD_vaccadj_apr2021", "RI_incidD_vaccadj_may2021", "RI_incidD_vaccadj_jun2021", "RI_incidD_vaccadj_jul2021", "RI_incidD_vaccadj_aug2021", "SC_incidD_vaccadj_jan2021", "SC_incidD_vaccadj_feb2021", "SC_incidD_vaccadj_mar2021", "SC_incidD_vaccadj_apr2021", "SC_incidD_vaccadj_may2021", "SC_incidD_vaccadj_jun2021", "SC_incidD_vaccadj_jul2021", "SC_incidD_vaccadj_aug2021", "SD_incidD_vaccadj_jan2021", "SD_incidD_vaccadj_feb2021", "SD_incidD_vaccadj_mar2021", "SD_incidD_vaccadj_apr2021", "SD_incidD_vaccadj_may2021", "SD_incidD_vaccadj_jun2021", "SD_incidD_vaccadj_jul2021", "SD_incidD_vaccadj_aug2021", "TN_incidD_vaccadj_jan2021", "TN_incidD_vaccadj_feb2021", "TN_incidD_vaccadj_mar2021", "TN_incidD_vaccadj_apr2021", "TN_incidD_vaccadj_may2021", "TN_incidD_vaccadj_jun2021", "TN_incidD_vaccadj_jul2021", "TN_incidD_vaccadj_aug2021", "TX_incidD_vaccadj_jan2021", "TX_incidD_vaccadj_feb2021", "TX_incidD_vaccadj_mar2021", "TX_incidD_vaccadj_apr2021", "TX_incidD_vaccadj_may2021", "TX_incidD_vaccadj_jun2021", "TX_incidD_vaccadj_jul2021", "TX_incidD_vaccadj_aug2021", "UT_incidD_vaccadj_jan2021", "UT_incidD_vaccadj_feb2021", "UT_incidD_vaccadj_mar2021", "UT_incidD_vaccadj_apr2021", "UT_incidD_vaccadj_may2021", "UT_incidD_vaccadj_jun2021", "UT_incidD_vaccadj_jul2021", "UT_incidD_vaccadj_aug2021", "VT_incidD_vaccadj_jan2021", "VT_incidD_vaccadj_feb2021", "VT_incidD_vaccadj_mar2021", "VT_incidD_vaccadj_apr2021", "VT_incidD_vaccadj_may2021", "VT_incidD_vaccadj_jun2021", "VT_incidD_vaccadj_jul2021", "VT_incidD_vaccadj_aug2021", "VA_incidD_vaccadj_jan2021", "VA_incidD_vaccadj_feb2021", "VA_incidD_vaccadj_mar2021", "VA_incidD_vaccadj_apr2021", "VA_incidD_vaccadj_may2021", "VA_incidD_vaccadj_jun2021", "VA_incidD_vaccadj_jul2021", "VA_incidD_vaccadj_aug2021", "WA_incidD_vaccadj_jan2021", "WA_incidD_vaccadj_feb2021", "WA_incidD_vaccadj_mar2021", "WA_incidD_vaccadj_apr2021", "WA_incidD_vaccadj_may2021", "WA_incidD_vaccadj_jun2021", "WA_incidD_vaccadj_jul2021", "WA_incidD_vaccadj_aug2021", "WV_incidD_vaccadj_jan2021", "WV_incidD_vaccadj_feb2021", "WV_incidD_vaccadj_mar2021", "WV_incidD_vaccadj_apr2021", "WV_incidD_vaccadj_may2021", "WV_incidD_vaccadj_jun2021", "WV_incidD_vaccadj_jul2021", "WV_incidD_vaccadj_aug2021", "WI_incidD_vaccadj_jan2021", "WI_incidD_vaccadj_feb2021", "WI_incidD_vaccadj_mar2021", "WI_incidD_vaccadj_apr2021", "WI_incidD_vaccadj_may2021", "WI_incidD_vaccadj_jun2021", "WI_incidD_vaccadj_jul2021", "WI_incidD_vaccadj_aug2021", "WY_incidD_vaccadj_jan2021", "WY_incidD_vaccadj_feb2021", "WY_incidD_vaccadj_mar2021", "WY_incidD_vaccadj_apr2021", "WY_incidD_vaccadj_may2021", "WY_incidD_vaccadj_jun2021", "WY_incidD_vaccadj_jul2021", "WY_incidD_vaccadj_aug2021", "GU_incidD_vaccadj_jan2021", "GU_incidD_vaccadj_feb2021", "GU_incidD_vaccadj_mar2021", "GU_incidD_vaccadj_apr2021", "GU_incidD_vaccadj_may2021", "GU_incidD_vaccadj_jun2021", "GU_incidD_vaccadj_jul2021", "GU_incidD_vaccadj_aug2021", "MP_incidD_vaccadj_jan2021", "MP_incidD_vaccadj_feb2021", "MP_incidD_vaccadj_mar2021", "MP_incidD_vaccadj_apr2021", "MP_incidD_vaccadj_may2021", "MP_incidD_vaccadj_jun2021", "MP_incidD_vaccadj_jul2021", "MP_incidD_vaccadj_aug2021", "PR_incidD_vaccadj_jan2021", "PR_incidD_vaccadj_feb2021", "PR_incidD_vaccadj_mar2021", "PR_incidD_vaccadj_apr2021", "PR_incidD_vaccadj_may2021", "PR_incidD_vaccadj_jun2021", "PR_incidD_vaccadj_jul2021", "PR_incidD_vaccadj_aug2021", "VI_incidD_vaccadj_jan2021", "VI_incidD_vaccadj_feb2021", "VI_incidD_vaccadj_mar2021", "VI_incidD_vaccadj_apr2021", "VI_incidD_vaccadj_may2021", "VI_incidD_vaccadj_jun2021", "VI_incidD_vaccadj_jul2021", "VI_incidD_vaccadj_aug2021"] inference: diff --git a/flepimop/R_packages/config.writer/tests/testthat/test-gen_npi.R b/flepimop/R_packages/config.writer/tests/testthat/test-gen_npi.R index 193bb40ce..a9b2b4506 100644 --- a/flepimop/R_packages/config.writer/tests/testthat/test-gen_npi.R +++ b/flepimop/R_packages/config.writer/tests/testthat/test-gen_npi.R @@ -27,7 +27,7 @@ generate_processed <- function(geodata_path, seasonality_dat <- set_seasonality_params(sim_start_date = sim_start, sim_end_date = sim_end, inference = TRUE, - template = "MultiTimeReduce", + template = "MultiPeriodModifier", v_dist="truncnorm", v_mean = c(-0.2, -0.133, -0.067, 0, 0.067, 0.133, 0.2, 0.133, 0.067, 0, -0.067, -0.133), v_sd = 0.05, v_a = -1, v_b = 1, @@ -47,7 +47,7 @@ generate_processed <- function(geodata_path, vacc_dat <- set_vacc_rates_params(vacc_path = vaccination_path, sim_end_date = sim_end, vacc_start_date="2021-01-01", - incl_geoid = NULL, + incl_subpop = NULL, scenario = vacc_scenario, compartment = FALSE) @@ -68,7 +68,7 @@ generate_processed <- function(geodata_path, sim_start_date = sim_start, sim_end_date = sim_end, inference = FALSE, - incl_geoid = NULL, + incl_subpop = NULL, scenario = vacc_scenario, v_dist="truncnorm", v_sd = 0.01, v_a = 0, v_b = 1, @@ -105,7 +105,7 @@ test_that("Interventions processing works", { outcomes_path = "outcome_adj.csv") interventions <- readr::read_csv("processed_intervention_data.csv") %>% - dplyr::filter(USPS %in% c("all", "KS", "DE", "") | geoid == "all") %>% + dplyr::filter(USPS %in% c("all", "KS", "DE", "") | subpop == "all") %>% dplyr::mutate(dplyr::across(pert_mean:pert_b, ~ifelse(stringr::str_detect(name, "variant") & start_date < as.Date("2021-06-15") | stringr::str_detect(name, "variant", negate = TRUE) , .x, NA_real_)), diff --git a/flepimop/R_packages/config.writer/tests/testthat/test-print_config.R b/flepimop/R_packages/config.writer/tests/testthat/test-print_config.R index e0a7dd95c..1a1d69be0 100644 --- a/flepimop/R_packages/config.writer/tests/testthat/test-print_config.R +++ b/flepimop/R_packages/config.writer/tests/testthat/test-print_config.R @@ -10,7 +10,7 @@ generate_config <- function(){ sim_end_date = "2021-08-07", dt = 0.25, nslots = 300, - sim_states = unique(interventions$USPS[!interventions$USPS %in% c("", "all") & !is.na(interventions$USPS)]), + modeled_states = unique(interventions$USPS[!interventions$USPS %in% c("", "all") & !is.na(interventions$USPS)]), setup_name = "usa_inference_territories_statelevel", geodata = "geodata_territories_2019_statelevel.csv", mobility = "mobility_territories_2011-2015_statelevel.csv") @@ -39,7 +39,7 @@ generate_config <- function(){ print_outcomes(dat = interventions, ifr = "med", - outcomes_parquet_file="usa-geoid-params-output_statelevel.parquet", + outcomes_parquet_file="usa-subpop-params-output_statelevel.parquet", incidC_prob_value = c(0.4, 0.4, 0.4), compartment = FALSE) diff --git a/flepimop/R_packages/config.writer/tests/testthat/vacc_rates.csv b/flepimop/R_packages/config.writer/tests/testthat/vacc_rates.csv index 10342062c..e32e53cbe 100644 --- a/flepimop/R_packages/config.writer/tests/testthat/vacc_rates.csv +++ b/flepimop/R_packages/config.writer/tests/testthat/vacc_rates.csv @@ -1,4 +1,4 @@ -USPS,geoid,month,year,scenario,start_date,end_date,pop_unvacc,vacc_rate +USPS,subpop,month,year,scenario,start_date,end_date,pop_unvacc,vacc_rate DE,10000,12,2020,2,2020-12-17,2020-12-31,956923.6607142857,1.64e-4 DE,10000,1,2021,2,2021-01-01,2021-01-31,946565.0215053763,4.24e-4 DE,10000,2,2021,2,2021-02-01,2021-02-28,923839.6870748299,0.003744 diff --git a/flepimop/R_packages/flepicommon/NAMESPACE b/flepimop/R_packages/flepicommon/NAMESPACE index d7f0983f1..3346b9a88 100644 --- a/flepimop/R_packages/flepicommon/NAMESPACE +++ b/flepimop/R_packages/flepicommon/NAMESPACE @@ -13,7 +13,7 @@ export(download_CSSE_global_data) export(download_reichlab_data) export(fix_negative_counts) export(fix_negative_counts_global) -export(fix_negative_counts_single_geoid) +export(fix_negative_counts_single_subpop) export(get_CSSE_US_data) export(get_CSSE_US_matchGlobal_data) export(get_CSSE_global_data) diff --git a/flepimop/R_packages/flepicommon/R/DataUtils.R b/flepimop/R_packages/flepicommon/R/DataUtils.R index c1a73d0b9..f76b5bfbf 100755 --- a/flepimop/R_packages/flepicommon/R/DataUtils.R +++ b/flepimop/R_packages/flepicommon/R/DataUtils.R @@ -4,14 +4,14 @@ ##' Convenience function to load the geodata file ##' ##' @param filename filename of geodata file -##' @param geoid_len length of geoid character string -##' @param geoid_pad what to pad the geoid character string with +##' @param subpop_len length of subpop character string +##' @param subpop_pad what to pad the subpop character string with ##' @param state_name whether to add column state with the US state name; defaults to TRUE for forecast or scenario hub runs. ##' ##' @details -##' Currently, the package only supports a geodata object with at least two columns: USPS with the state abbreviation and geoid with the geo IDs of the area. . +##' Currently, the package only supports a geodata object with at least two columns: USPS with the state abbreviation and subpop with the geo IDs of the area. . ##' -##' @return a data frame with columns for state USPS, county geoid and population +##' @return a data frame with columns for state USPS, county subpop and population ##' @examples ##' geodata <- load_geodata_file(filename = system.file("extdata", "geodata_territories_2019_statelevel.csv", package = "config.writer")) ##' geodata @@ -19,21 +19,21 @@ ##' @export load_geodata_file <- function(filename, - geoid_len = 0, - geoid_pad = "0", + subpop_len = 0, + subpop_pad = "0", state_name = TRUE ) { if(!file.exists(filename)){stop(paste(filename,"does not exist in",getwd()))} geodata <- readr::read_csv(filename) %>% - dplyr::mutate(geoid = as.character(geoid)) + dplyr::mutate(subpop = as.character(subpop)) - if (!("geoid" %in% names(geodata))) { - stop(paste(filename, "does not have a column named geoid")) + if (!("subpop" %in% names(geodata))) { + stop(paste(filename, "does not have a column named subpop")) } - if (geoid_len > 0) { - geodata$geoid <- stringr::str_pad(geodata$geoid, geoid_len, pad = geoid_pad) + if (subpop_len > 0) { + geodata$subpop <- stringr::str_pad(geodata$subpop, subpop_len, pad = subpop_pad) } if(state_name) { @@ -69,7 +69,7 @@ read_file_of_type <- function(extension,...){ time=col_date(), uid=col_character(), comp=col_character(), - geoid=col_character() + subpop=col_character() )))}) } if(extension == 'parquet'){ @@ -213,7 +213,7 @@ get_islandareas_data <- function() { #' @export #' #' @examples -fix_negative_counts_single_geoid <- function(.x,.y, incid_col_name, date_col_name, cum_col_name, type){ +fix_negative_counts_single_subpop <- function(.x,.y, incid_col_name, date_col_name, cum_col_name, type){ original_names <- names(.x) .x <- dplyr::arrange(.x,!!rlang::sym(date_col_name)) @@ -278,7 +278,7 @@ fix_negative_counts_single_geoid <- function(.x,.y, incid_col_name, date_col_nam # Add missing dates, fix counts that go negative, and fix NA values # -# See fix_negative_counts_single_geoid() for more details on the algorithm, +# See fix_negative_counts_single_subpop() for more details on the algorithm, # specified by argument "type" #' Title #' @@ -311,7 +311,7 @@ fix_negative_counts <- function( df <- dplyr::group_by(df, FIPS,source) # Add missing dates df <- tidyr::complete(df, !!rlang::sym(date_col_name) := min_date + seq_len(max_date - min_date)-1) - df <- dplyr::group_map(df, fix_negative_counts_single_geoid, + df <- dplyr::group_map(df, fix_negative_counts_single_subpop, incid_col_name=incid_col_name, date_col_name=date_col_name, cum_col_name=cum_col_name, @@ -324,7 +324,7 @@ fix_negative_counts <- function( # Add missing dates, fix counts that go negative, and fix NA values for global dataset (group by Country_Region and Province_State instead of by FIPS) # -# See fix_negative_counts_single_geoid() for more details on the algorithm, +# See fix_negative_counts_single_subpop() for more details on the algorithm, # specified by argument "type" #' Title #' @@ -357,7 +357,7 @@ fix_negative_counts_global <- function( df <- dplyr::group_by(df, Country_Region, Province_State, source) # Add missing dates df <- tidyr::complete(df, !!rlang::sym(date_col_name) := min_date + seq_len(max_date - min_date)-1) - df <- dplyr::group_map(df, fix_negative_counts_single_geoid, + df <- dplyr::group_map(df, fix_negative_counts_single_subpop, incid_col_name=incid_col_name, date_col_name=date_col_name, cum_col_name=cum_col_name, diff --git a/flepimop/R_packages/flepicommon/R/config_test_new.R b/flepimop/R_packages/flepicommon/R/config_test_new.R index 0264ae7f0..e6c71d0ef 100644 --- a/flepimop/R_packages/flepicommon/R/config_test_new.R +++ b/flepimop/R_packages/flepicommon/R/config_test_new.R @@ -82,7 +82,7 @@ validation_list$nslots<- function(value,full_config,config_name){ ##Checking if the following values are present or not. ##If they do not have an assigned default value then the execution will be stopped. ##If they have a default then A statement will be printed and test will continue -## NO Default: Base Path, Modeled States, Year. Nodenames +## No Default: Base Path, Modeled States, Year. subpop ## With Default: Geodata, Mobility, Popnodes, Statelevel validation_list$spatial_setup <- list() @@ -153,9 +153,9 @@ validation_list$spatial_setup$census_year <- function(value, full_config,config_ return(TRUE) } -validation_list$spatial_setup$nodenames <- function(value, full_config,config_name) { +validation_list$spatial_setup$subpop <- function(value, full_config,config_name) { if (is.null(value)) { - print("No Nodenames mentioned") #Should display a better error message than nodenames. + print("No subpops mentioned") #Should display a better error message than subpop. return(FALSE) } return(TRUE) @@ -163,7 +163,7 @@ validation_list$spatial_setup$nodenames <- function(value, full_config,config_na validation_list$spatial_setup$popnodes <- function(value, full_config,config_name) { if (is.null(value)) { - print("No Population Nodes mentioned") #Should display a better error message than nodenames. + print("No Population Nodes mentioned") #Should display a better error message than subpop. return(FALSE) } return(TRUE) @@ -176,7 +176,7 @@ validation_list$spatial_setup$include_in_report <- function(value, full_config,c validation_list$setup_name <- function(value, full_config,config_name) { if (is.null(value)) { - print("No runtype mentioned") #Should display a better error message than nodenames. + print("No runtype mentioned") #Should display a better error message than subpop. return(FALSE) } if (length(strsplit(config_copy$setup_name,split=" ")[[1]])!=1 | length(config_copy$setup_name)!=1){ diff --git a/flepimop/R_packages/inference/R/documentation.Rmd b/flepimop/R_packages/inference/R/documentation.Rmd index 547b07e60..54993796b 100644 --- a/flepimop/R_packages/inference/R/documentation.Rmd +++ b/flepimop/R_packages/inference/R/documentation.Rmd @@ -8,7 +8,7 @@ We describe these options below and present default values in the example config # Modifications to `seeding` -The model can perform inference on the seeding date and initial number of seeding infections in each geoid. An example of this new config section is: +The model can perform inference on the seeding date and initial number of seeding infections in each subpop. An example of this new config section is: ``` seeding: @@ -39,7 +39,8 @@ interventions: - Scenario1 settings: local_variance: - template: ReduceR0 + template: SinglePeriodModifier + parameter: r0 value: distribution: truncnorm mean: 0 @@ -53,7 +54,8 @@ interventions: a: -1 b: 1 stayhome: - template: ReduceR0 + template: SinglePeriodModifier + parameter: r0 period_start_date: 2020-04-04 period_end_date: 2020-04-30 value: @@ -69,7 +71,7 @@ interventions: a: -1 b: 1 Scenario1: - template: Stacked + template: StackedModifier scenarios: - local_variance - stayhome @@ -77,23 +79,23 @@ interventions: ## `interventions::settings::[setting_name]` -This configuration allows us to infer geoid-level baseline R0 estimates by adding a `local_variance` intervention. The baseline geoid-specific R0 estimate may be calculated as $$R0*(1-local_variance),$$ where R0 is the baseline simulation R0 value, and local_variance is an estimated geoid-specific value. +This configuration allows us to infer subpop-level baseline R0 estimates by adding a `local_variance` intervention. The baseline subpop-specific R0 estimate may be calculated as $$R0*(1-local_variance),$$ where R0 is the baseline simulation R0 value, and local_variance is an estimated subpop-specific value. -Interventions may be specified in the same way as before, or with an added `perturbation` section that indicates that inference should be performed on a given intervention's effectiveness. As previously, interventions with perturbations may be specified for all modeled locations or for explicit `affected_geoids.` In this setup, both the prior distribution and the range of the support of the final inferred value are specified by the `value` section. In the configuration above, the inference algorithm will search 0 to 0.9 for all geoids to estimate the effectiveness of the `stayhome` intervention period. The prior distribution on intervention effectiveness follows a truncated normal distribution with a mean of 0.6 and a standard deviation of 0.3. The `perturbation` section specifies the perturbation/step size between the previously-accepted values and the next proposal value. +Interventions may be specified in the same way as before, or with an added `perturbation` section that indicates that inference should be performed on a given intervention's effectiveness. As previously, interventions with perturbations may be specified for all modeled locations or for explicit `subpop.` In this setup, both the prior distribution and the range of the support of the final inferred value are specified by the `value` section. In the configuration above, the inference algorithm will search 0 to 0.9 for all subpop to estimate the effectiveness of the `stayhome` intervention period. The prior distribution on intervention effectiveness follows a truncated normal distribution with a mean of 0.6 and a standard deviation of 0.3. The `perturbation` section specifies the perturbation/step size between the previously-accepted values and the next proposal value. | Item | Required? | Type/Format | |-------------------|-----------------------|-------------------------------------------------| -| template | **required** | "ReduceR0" or "Stacked" | -| period_start_date | optional for ReduceR0 | date between global `start_date` and `end_date`; default is global `start_date` | -| period_end_date | optional for ReduceR0 | date between global `start_date` and `end_date`; default is global `end_date` | -| value | required for ReduceR0 | specifies both the prior distribution and range of support for the final inferred values | -| perturbation | optional for ReduceR0 | this option indicates whether inference will be performed on this setting and how the proposal value will be identified from the last accepted value | -| affected_geoids | optional for ReduceR0 | list of geoids, which must be in geodata | +| template | **required** | "SinglePeriodModifier" or "StackedModifier" | +| period_start_date | optional for SinglePeriodModifier | date between global `start_date` and `end_date`; default is global `start_date` | +| period_end_date | optional for SinglePeriodModifier | date between global `start_date` and `end_date`; default is global `end_date` | +| value | required for SinglePeriodModifier | specifies both the prior distribution and range of support for the final inferred values | +| perturbation | optional for SinglePeriodModifier | this option indicates whether inference will be performed on this setting and how the proposal value will be identified from the last accepted value | +| subpop | optional for SinglePeriodModifier | list of subpop, which must be in geodata | # New `inference` section -This section configures the settings for the inference algorithm. The below example shows the settings for some typical default settings, where the model is calibrated to the weekly incident deaths and weekly incident confirmed cases for each geoid. +This section configures the settings for the inference algorithm. The below example shows the settings for some typical default settings, where the model is calibrated to the weekly incident deaths and weekly incident confirmed cases for each subpop. ``` inference: diff --git a/flepimop/R_packages/inference/R/functions.R b/flepimop/R_packages/inference/R/functions.R index 3b237695f..0285fe7ef 100644 --- a/flepimop/R_packages/inference/R/functions.R +++ b/flepimop/R_packages/inference/R/functions.R @@ -208,14 +208,14 @@ logLikStat <- function(obs, sim, distr, param, add_one = F) { ##' ##' @param stat the statistic to calculate the penalty on ##' @param infer_frame data frame with the statistics in it -##' @param geodata geodata containing geoid from npi fram and the grouping column +##' @param geodata geodata containing subpop from npi fram and the grouping column ##' @param geo_group_col the column to group on ##' @param stat_name_col column holding stats name...default is npi_name ##' @param stat_col column hold the stat ##' @param transform how should the data be transformed before calc ##' @param min_sd what is the minimum SD to consider. Default is .1 ##' -##' @return a data frame with geoids and a per geoid LL adjustment +##' @return a data frame with subpop and a per subpop LL adjustment ##' ##' @export ##' @@ -253,7 +253,7 @@ calc_hierarchical_likadj <- function (stat, mean(!!sym(stat_col)), max(sd(!!sym(stat_col)), min_sd, na.rm=T), log=TRUE))%>% ungroup()%>% - select(geoid, likadj) + select(subpop, likadj) return(rc) } @@ -290,7 +290,7 @@ calc_prior_likadj <- function(params, ##' ##' -##' Function to compute cumulative counts across geoids +##' Function to compute cumulative counts across subpop ##' ##' @param sim_hosp output of ouctomes branching process ##' @@ -300,8 +300,8 @@ calc_prior_likadj <- function(params, ##' compute_cumulative_counts <- function(sim_hosp) { res <- sim_hosp %>% - gather(var, value, -time, -geoid) %>% - group_by(geoid, var) %>% + gather(var, value, -time, -subpop) %>% + group_by(subpop, var) %>% arrange(time) %>% mutate(cumul = cumsum(value)) %>% ungroup() %>% @@ -314,7 +314,7 @@ compute_cumulative_counts <- function(sim_hosp) { ##' ##' -##' Function to compute cumulative counts across geoids +##' Function to compute cumulative counts across subpop ##' ##' @param sim_hosp output of ouctomes branching process ##' @@ -326,7 +326,7 @@ compute_totals <- function(sim_hosp) { sim_hosp %>% group_by(time) %>% summarise_if(is.numeric, sum, na.rm = TRUE) %>% - mutate(geoid = "all") %>% + mutate(subpop = "all") %>% select(all_of(colnames(sim_hosp))) %>% rbind(sim_hosp) } @@ -505,18 +505,18 @@ perturb_hpar <- function(hpar, intervention_settings) { return(hpar) } -##' Function to go through to accept or reject proposed parameters for each geoid based -##' on a geoid specific likelihood. -##' -##' -##' @param seeding_orig original seeding data frame (must have column place) -##' @param seeding_prop proposal seeding (must have column place) -##' @param snpi_orig original npi data frame (must have column geoid) -##' @param snpi_prop proposal npi data frame (must have column geoid) -##' @param hnpi_orig original npi data frame (must have column geoid) -##' @param hnpi_prop proposal npi data frame (must have column geoid) -##' @param orig_lls original ll data frame (must have column ll and geoid) -##' @param prop_lls proposal ll fata frame (must have column ll and geoid) +##' Function to go through to accept or reject proposed parameters for each subpop based +##' on a subpop specific likelihood. +##' +##' +##' @param seeding_orig original seeding data frame (must have column subpop) +##' @param seeding_prop proposal seeding (must have column subpop) +##' @param snpi_orig original npi data frame (must have column subpop) +##' @param snpi_prop proposal npi data frame (must have column subpop) +##' @param hnpi_orig original npi data frame (must have column subpop) +##' @param hnpi_prop proposal npi data frame (must have column subpop) +##' @param orig_lls original ll data frame (must have column ll and subpop) +##' @param prop_lls proposal ll fata frame (must have column ll and subpop) ##' @return a new data frame with the confirmed seedin. ##' @export accept_reject_new_seeding_npis <- function( @@ -536,8 +536,8 @@ accept_reject_new_seeding_npis <- function( rc_hnpi <- hnpi_orig rc_hpar <- hpar_orig - if (!all(orig_lls$geoid == prop_lls$geoid)) { - stop("geoids must match") + if (!all(orig_lls$subpop == prop_lls$subpop)) { + stop("subpop must match") } ##draw accepts/rejects ratio <- exp(prop_lls$ll - orig_lls$ll) @@ -548,11 +548,11 @@ accept_reject_new_seeding_npis <- function( orig_lls$accept <- as.numeric(accept) # added column for acceptance decision orig_lls$accept_prob <- min(1,ratio) # added column for acceptance decision - for (place in orig_lls$geoid[accept]) { - rc_seeding[rc_seeding$place == place, ] <- seeding_prop[seeding_prop$place ==place, ] - rc_snpi[rc_snpi$geoid == place, ] <- snpi_prop[snpi_prop$geoid == place, ] - rc_hnpi[rc_hnpi$geoid == place, ] <- hnpi_prop[hnpi_prop$geoid == place, ] - rc_hpar[rc_hpar$geoid == place, ] <- hpar_prop[hpar_prop$geoid == place, ] + for (subpop in orig_lls$subpop[accept]) { + rc_seeding[rc_seeding$subpop == subpop, ] <- seeding_prop[seeding_prop$subpop ==subpop, ] + rc_snpi[rc_snpi$subpop == subpop, ] <- snpi_prop[snpi_prop$subpop == subpop, ] + rc_hnpi[rc_hnpi$subpop == subpop, ] <- hnpi_prop[hnpi_prop$subpop == subpop, ] + rc_hpar[rc_hpar$subpop == subpop, ] <- hpar_prop[hpar_prop$subpop == subpop, ] } return(list( @@ -654,11 +654,11 @@ perturb_snpi_from_file <- function(snpi, intervention_settings, llik){ } ## for each of them generate the perturbation and update their value - for (this_npi_ind in which(ind)){ # for each geoid that has this interventions + for (this_npi_ind in which(ind)){ # for each subpop that has this interventions - this_geoid <- snpi[["geoid"]][this_npi_ind] - this_accept_avg <- llik$accept_avg[llik$geoid==this_geoid] - his_accept_prob <- llik$accept_prob[llik$geoid==this_geoid] + this_subpop <- snpi[["subpop"]][this_npi_ind] + this_accept_avg <- llik$accept_avg[llik$subpop==this_subpop] + his_accept_prob <- llik$accept_prob[llik$subpop==this_subpop] this_intervention_setting<- intervention_settings[[intervention]] ##get the random distribution from flepicommon package @@ -750,10 +750,10 @@ perturb_hnpi_from_file <- function(hnpi, intervention_settings, llik){ } ## for each of them generate the perturbation and update their value - for (this_npi_ind in which(ind)){ # for each geoid that has this interventions + for (this_npi_ind in which(ind)){ # for each subpop that has this interventions - this_geoid <- hnpi[["geoid"]][this_npi_ind] - this_accept_avg <- llik$accept_avg[llik$geoid==this_geoid] + this_subpop <- hnpi[["subpop"]][this_npi_ind] + this_accept_avg <- llik$accept_avg[llik$subpop==this_subpop] this_intervention_setting<- intervention_settings[[intervention]] ##get the random distribution from flepicommon package diff --git a/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R b/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R index 10f62fd36..f97300d3b 100644 --- a/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R +++ b/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R @@ -5,7 +5,7 @@ ##' ##' @param all_locations all of the locations to calculate likelihood for ##' @param modeled_outcome the hospital data for the simulations -##' @param obs_nodename the name of the column containing locations. +##' @param obs_subpop the name of the column containing locations. ##' @param config the full configuration setup ##' @param obs the full observed data ##' @param ground_truth_data the data we are going to compare to aggregated to the right statistic @@ -24,7 +24,7 @@ aggregate_and_calc_loc_likelihoods <- function( all_locations, modeled_outcome, - obs_nodename, + obs_subpop, targets_config, obs, ground_truth_data, @@ -51,8 +51,8 @@ aggregate_and_calc_loc_likelihoods <- function( ## Filter to this location dplyr::filter( modeled_outcome, - !!rlang::sym(obs_nodename) == location, - time %in% unique(obs$date[obs$geoid == location]) + !!rlang::sym(obs_subpop) == location, + time %in% unique(obs$date[obs$subpop == location]) ) %>% ## Reformat into form the algorithm is looking for inference::getStats( @@ -85,12 +85,12 @@ aggregate_and_calc_loc_likelihoods <- function( likelihood_data[[location]] <- dplyr::tibble( ll = this_location_log_likelihood, filename = hosp_file, - geoid = location, + subpop = location, accept = 0, # acceptance decision (0/1) . Will be updated later when accept/reject decisions made accept_avg = 0, # running average acceptance decision accept_prob = 0 # probability of acceptance of proposal ) - names(likelihood_data)[names(likelihood_data) == 'geoid'] <- obs_nodename + names(likelihood_data)[names(likelihood_data) == 'subpop'] <- obs_subpop } #' @importFrom magrittr %>% @@ -138,7 +138,7 @@ aggregate_and_calc_loc_likelihoods <- function( ##probably a more efficient what to do this, but unclear... - likelihood_data <- dplyr::left_join(likelihood_data, ll_adjs, by = obs_nodename) %>% + likelihood_data <- dplyr::left_join(likelihood_data, ll_adjs, by = obs_subpop) %>% tidyr::replace_na(list(likadj = 0)) %>% ##avoid unmatched location problems dplyr::mutate(ll = ll + likadj) %>% dplyr::select(-likadj) @@ -155,7 +155,7 @@ aggregate_and_calc_loc_likelihoods <- function( defined_priors[[prior]]$likelihood$dist, defined_priors[[prior]]$likelihood$param )) %>% - dplyr::select(geoid, likadj) + dplyr::select(subpop, likadj) } else if (defined_priors[[prior]]$module == "outcomes_interventions") { #' @importFrom magrittr %>% @@ -165,7 +165,7 @@ aggregate_and_calc_loc_likelihoods <- function( defined_priors[[prior]]$likelihood$dist, defined_priors[[prior]]$likelihood$param )) %>% - dplyr::select(geoid, likadj) + dplyr::select(subpop, likadj) } else if (defined_priors[[prior]]$module %in% c("outcomes_parameters", "hospitalization")) { @@ -175,7 +175,7 @@ aggregate_and_calc_loc_likelihoods <- function( defined_priors[[prior]]$likelihood$dist, defined_priors[[prior]]$likelihood$param )) %>% - dplyr::select(geoid, likadj) + dplyr::select(subpop, likadj) } else if (hierarchical_stats[[stat]]$module == "seir_parameters") { stop("We currently do not support priors on seir parameters, since we don't do inference on them except via npis.") @@ -184,7 +184,7 @@ aggregate_and_calc_loc_likelihoods <- function( } ##probably a more efficient what to do this, but unclear... - likelihood_data<- dplyr::left_join(likelihood_data, ll_adjs, by = obs_nodename) %>% + likelihood_data<- dplyr::left_join(likelihood_data, ll_adjs, by = obs_subpop) %>% dplyr::mutate(ll = ll + likadj) %>% dplyr::select(-likadj) } @@ -737,7 +737,7 @@ initialize_mcmc_first_block <- function( # These functions save variables to files of the form variable/name/npi_scenario/outcome_scenario/run_id/global/intermediate/slot.(block-1),runID.variable.ext if (any(checked_par_files %in% global_file_names)) { if (!all(checked_par_files %in% global_file_names)) { - stop("Provided some InferenceSimulator input, but not all") + stop("Provided some GempyorSimulator input, but not all") } if (any(checked_sim_files %in% global_file_names)) { if (!all(checked_sim_files %in% global_file_names)) { @@ -745,7 +745,7 @@ initialize_mcmc_first_block <- function( } gempyor_inference_runner$one_simulation(sim_id2write = block - 1) } else { - stop("Provided some InferenceSimulator output(seir, hosp), but not InferenceSimulator input") + stop("Provided some GempyorSimulator output(seir, hosp), but not GempyorSimulator input") } } else { if (any(checked_sim_files %in% global_file_names)) { diff --git a/flepimop/R_packages/inference/archive/InferenceTest.R b/flepimop/R_packages/inference/archive/InferenceTest.R index 7b26d2bb6..83e383aa6 100644 --- a/flepimop/R_packages/inference/archive/InferenceTest.R +++ b/flepimop/R_packages/inference/archive/InferenceTest.R @@ -31,7 +31,7 @@ single_loc_inference_test <- function(to_fit, registerDoSNOW(cl) # Column name that stores spatial unique id - obs_nodename <- config$spatial_setup$nodenames + obs_subpop <- config$spatial_setup$subpop # Set number of simulations iterations_per_slot <- config$inference$iterations_per_slot @@ -48,13 +48,13 @@ single_loc_inference_test <- function(to_fit, sim_times <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days") # Get unique geonames - geonames <- unique(obs[[obs_nodename]]) + geonames <- unique(obs[[obs_subpop]]) # Compute statistics of observations data_stats <- lapply( geonames, function(x) { - df <- obs[obs[[obs_nodename]] == x, ] + df <- obs[obs[[obs_subpop]] == x, ] getStats( df, "date", @@ -63,7 +63,7 @@ single_loc_inference_test <- function(to_fit, }) %>% set_names(geonames) - all_locations <- unique(obs[[obs_nodename]]) + all_locations <- unique(obs[[obs_subpop]]) # Inference loops required_packages <- c("dplyr", "magrittr", "xts", "zoo", "purrr", "stringr", "truncnorm", @@ -97,7 +97,7 @@ single_loc_inference_test <- function(to_fit, write_csv(seeding_file, append = file.exists(seeding_file)) initial_npis %>% - distinct(reduction, npi_name, geoid) %>% + distinct(reduction, npi_name, subpop) %>% mutate(slot = s, index = 0) %>% write_csv(npi_file, append = file.exists(npi_file)) @@ -136,7 +136,7 @@ single_loc_inference_test <- function(to_fit, # Compute log-likelihoods initial_log_likelihood_data <- dplyr::tibble( ll = sum(unlist(log_likelihood)), - geoid = 1 + subpop = 1 ) # Compute total loglik for each sim @@ -188,7 +188,7 @@ single_loc_inference_test <- function(to_fit, # Compute log-likelihoods log_likelihood_data <- dplyr::tibble( ll = sum(unlist(log_likelihood)), - geoid = 1 + subpop = 1 ) # Compute total loglik for each sim @@ -209,13 +209,13 @@ single_loc_inference_test <- function(to_fit, seeding_npis_list <- accept_reject_new_seeding_npis( seeding_orig = initial_seeding, seeding_prop = current_seeding, - npis_orig = distinct(initial_npis, reduction, npi_name, geoid), - npis_prop = distinct(current_npis, reduction, npi_name, geoid), + npis_orig = distinct(initial_npis, reduction, npi_name, subpop), + npis_prop = distinct(current_npis, reduction, npi_name, subpop), orig_lls = previous_likelihood_data, prop_lls = log_likelihood_data ) initial_seeding <- seeding_npis_list$seeding - initial_npis <- inner_join(seeding_npis_list$npis, select(current_npis, -reduction), by = c("geoid", "npi_name")) + initial_npis <- inner_join(seeding_npis_list$npis, select(current_npis, -reduction), by = c("subpop", "npi_name")) previous_likelihood_data <- seeding_npis_list$ll # Write to file @@ -274,7 +274,7 @@ multi_loc_inference_test <- function(to_fit, N <- length(S0s) # Column name that stores spatial unique id - obs_nodename <- config$spatial_setup$nodenames + obs_subpop <- config$spatial_setup$subpop # Set number of simulations iterations_per_slot <- config$inference$iterations_per_slot @@ -291,13 +291,13 @@ multi_loc_inference_test <- function(to_fit, sim_times <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days") # Get unique geonames - geonames <- unique(obs[[obs_nodename]]) + geonames <- unique(obs[[obs_subpop]]) # Compute statistics of observations data_stats <- lapply( geonames, function(x) { - df <- obs[obs[[obs_nodename]] == x, ] + df <- obs[obs[[obs_subpop]] == x, ] getStats( df, "date", @@ -306,7 +306,7 @@ multi_loc_inference_test <- function(to_fit, }) %>% set_names(geonames) - all_locations <- unique(obs[[obs_nodename]]) + all_locations <- unique(obs[[obs_subpop]]) # Inference loops required_packages <- c("dplyr", "magrittr", "xts", "zoo", "purrr", "stringr", "truncnorm", @@ -325,7 +325,7 @@ multi_loc_inference_test <- function(to_fit, npis_init <- pmap(list(x = 1:N, y = offsets), function(x,y) npis_dataframe(config, - geoid = x, + subpop = x, offset = y, random = T)) %>% bind_rows() @@ -346,12 +346,12 @@ multi_loc_inference_test <- function(to_fit, write_csv(seeding_file, append = file.exists(seeding_file)) initial_npis %>% - distinct(reduction, npi_name, geoid) %>% + distinct(reduction, npi_name, subpop) %>% mutate(slot = s, index = 0) %>% write_csv(npi_file, append = file.exists(npi_file)) - npi_mat <- select(initial_npis, date, geoid, reduction) %>% - pivot_wider(values_from = "reduction", names_from = "geoid", id_cols = "date") + npi_mat <- select(initial_npis, date, subpop, reduction) %>% + pivot_wider(values_from = "reduction", names_from = "subpop", id_cols = "date") # Simulate epi initial_sim_hosp <- simulate_multi_epi(times = sim_times, @@ -371,13 +371,13 @@ multi_loc_inference_test <- function(to_fit, initial_likelihood_data <- list() for(location in all_locations) { - local_sim_hosp <- dplyr::filter(initial_sim_hosp, !!rlang::sym(obs_nodename) == location) %>% - dplyr::filter(time %in% unique(obs$date[obs$geoid == location])) + local_sim_hosp <- dplyr::filter(initial_sim_hosp, !!rlang::sym(obs_subpop) == location) %>% + dplyr::filter(time %in% unique(obs$date[obs$subpop == location])) initial_sim_stats <- inference::getStats( local_sim_hosp, "time", "sim_var", - #end_date = max(obs$date[obs[[obs_nodename]] == location]), + #end_date = max(obs$date[obs[[obs_subpop]] == location]), stat_list = config$inference$statistics ) @@ -396,7 +396,7 @@ multi_loc_inference_test <- function(to_fit, # Compute log-likelihoods initial_likelihood_data[[location]] <- dplyr::tibble( ll = sum(unlist(log_likelihood)), - geoid = location + subpop = location ) } @@ -423,8 +423,8 @@ multi_loc_inference_test <- function(to_fit, current_seeding <- perturb_seeding(initial_seeding, config$seeding$perturbation_sd, date_bounds) current_npis <- perturb_expand_npis(initial_npis, config$interventions$settings, multi = T) - npi_mat <- select(current_npis, date, geoid, reduction) %>% - pivot_wider(values_from = "reduction", names_from = "geoid", id_cols = "date") + npi_mat <- select(current_npis, date, subpop, reduction) %>% + pivot_wider(values_from = "reduction", names_from = "subpop", id_cols = "date") # Simulate hospitalizatoins sim_hosp <- simulate_multi_epi(times = sim_times, @@ -442,13 +442,13 @@ multi_loc_inference_test <- function(to_fit, current_likelihood_data <- list() for(location in all_locations) { - local_sim_hosp <- dplyr::filter(sim_hosp, !!rlang::sym(obs_nodename) == location) %>% - dplyr::filter(time %in% unique(obs$date[obs$geoid == location])) + local_sim_hosp <- dplyr::filter(sim_hosp, !!rlang::sym(obs_subpop) == location) %>% + dplyr::filter(time %in% unique(obs$date[obs$subpop == location])) sim_stats <- inference::getStats( local_sim_hosp, "time", "sim_var", - #end_date = max(obs$date[obs[[obs_nodename]] == location]), + #end_date = max(obs$date[obs[[obs_subpop]] == location]), stat_list = config$inference$statistics ) @@ -467,7 +467,7 @@ multi_loc_inference_test <- function(to_fit, # Compute log-likelihoods current_likelihood_data[[location]] <- dplyr::tibble( ll = sum(unlist(log_likelihood)), - geoid = location + subpop = location ) } @@ -496,13 +496,13 @@ multi_loc_inference_test <- function(to_fit, seeding_npis_list <- accept_reject_new_seeding_npis( seeding_orig = initial_seeding, seeding_prop = current_seeding, - npis_orig = distinct(initial_npis, reduction, npi_name, geoid), - npis_prop = distinct(current_npis, reduction, npi_name, geoid), + npis_orig = distinct(initial_npis, reduction, npi_name, subpop), + npis_prop = distinct(current_npis, reduction, npi_name, subpop), orig_lls = previous_likelihood_data, prop_lls = current_likelihood_data ) initial_seeding <- seeding_npis_list$seeding - initial_npis <- inner_join(seeding_npis_list$npis, select(current_npis, -reduction), by = c("geoid", "npi_name")) + initial_npis <- inner_join(seeding_npis_list$npis, select(current_npis, -reduction), by = c("subpop", "npi_name")) previous_likelihood_data <- seeding_npis_list$ll # Write to file @@ -712,10 +712,10 @@ simulate_multi_epi <- function(times, } } - epi <- lapply(1:N, function(x) as.data.frame(epi[,,x]) %>% mutate(geoid = x)) %>% + epi <- lapply(1:N, function(x) as.data.frame(epi[,,x]) %>% mutate(subpop = x)) %>% bind_rows() %>% mutate(time=rep(times, N)) %>% - pivot_longer(cols = c(-time, -geoid), values_to="N", names_to="comp") + pivot_longer(cols = c(-time, -subpop), values_to="N", names_to="comp") return(epi) } @@ -742,11 +742,11 @@ single_hosp_run <- function(epi, config) { dat_ <- dplyr::filter(epi, comp == "incidI") %>% select(-comp) %>% rename(incidI = N) %>% - mutate(uid = epi$geoid[1]) %>% + mutate(uid = epi$subpop[1]) %>% as.data.table() - if ("geoid" %in% colnames(dat_)) { - dat_ <- select(dat_, -geoid) + if ("subpop" %in% colnames(dat_)) { + dat_ <- select(dat_, -subpop) } dat_H <- hosp_create_delay_frame('incidI',p_hosp,dat_,time_hosp_pars,"H") @@ -771,7 +771,7 @@ single_hosp_run <- function(epi, config) { list(hosp_curr = 0)) %>% arrange(date_inds) %>% select(-date_inds) %>% - mutate(geoid = uid) %>% + mutate(subpop = uid) %>% select(-uid) return(res) @@ -780,7 +780,7 @@ single_hosp_run <- function(epi, config) { ##' @export multi_hosp_run <- function(epi, N, config) { map_df(1:N, - ~ single_hosp_run(dplyr::filter(epi, geoid == .), config)) %>% + ~ single_hosp_run(dplyr::filter(epi, subpop == .), config)) %>% dplyr::filter(time >= config$start_date, time <= config$end_date) } @@ -795,10 +795,10 @@ multi_hosp_run <- function(epi, N, config) { ##' ##' ##' @export -npis_dataframe <- function(config, random = F, geoid = 1, offset = 0, intervention_multi = 1) { +npis_dataframe <- function(config, random = F, subpop = 1, offset = 0, intervention_multi = 1) { times <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days") - npis <- tibble(date = times, reduction = 0, npi_name = "local_variation", geoid = geoid) + npis <- tibble(date = times, reduction = 0, npi_name = "local_variation", subpop = subpop) interventions <- config$interventions$settings date_changes <- map_chr(interventions[1:2], ~ifelse(is.null(.$period_start_date), @@ -886,7 +886,7 @@ synthetic_data_multi <- function(S0s, seedings, mob, config, offsets, interventi npis <- pmap(list(x = 1:N, y = offsets, z = interventions_multi), function(x,y,z) npis_dataframe(config, - geoid = x, + subpop = x, offset = y, intervention_multi = z)) %>% bind_rows() @@ -895,8 +895,8 @@ synthetic_data_multi <- function(S0s, seedings, mob, config, offsets, interventi gamma <- flepicommon::as_evaled_expression(config$seir$parameters$gamma$value) sigma <- flepicommon::as_evaled_expression(config$seir$parameters$sigma) - npi_mat <- select(npis, date, geoid, reduction) %>% - pivot_wider(values_from = "reduction", names_from = "geoid", id_cols = "date") + npi_mat <- select(npis, date, subpop, reduction) %>% + pivot_wider(values_from = "reduction", names_from = "subpop", id_cols = "date") # Simulate epi epi <- simulate_multi_epi(times = times, @@ -912,7 +912,7 @@ synthetic_data_multi <- function(S0s, seedings, mob, config, offsets, interventi # - - - - # Setup fake data fake_data <- map_df(1:N, - ~ single_hosp_run(dplyr::filter(epi, geoid == .), config)) %>% + ~ single_hosp_run(dplyr::filter(epi, subpop == .), config)) %>% rename(date = time) %>% dplyr::filter(date >= config$start_date, date <= config$end_date) @@ -933,12 +933,12 @@ synthetic_data_multi <- function(S0s, seedings, mob, config, offsets, interventi perturb_expand_npis <- function(npis, intervention_settings, multi = F) { if(multi) { npis %>% - distinct(reduction, npi_name, geoid) %>% - group_by(geoid) %>% + distinct(reduction, npi_name, subpop) %>% + group_by(subpop) %>% group_map(~perturb_npis(.x, intervention_settings) %>% - mutate(geoid = .y$geoid[1])) %>% + mutate(subpop = .y$subpop[1])) %>% bind_rows() %>% - inner_join(select(npis, -reduction), by = c("npi_name", "geoid")) + inner_join(select(npis, -reduction), by = c("npi_name", "subpop")) } else { npis %>% distinct(reduction, npi_name) %>% diff --git a/flepimop/R_packages/inference/tests/testthat/test-accept_reject_new_seeding_npis.R b/flepimop/R_packages/inference/tests/testthat/test-accept_reject_new_seeding_npis.R index 9b8c0930a..5aba8a8a5 100644 --- a/flepimop/R_packages/inference/tests/testthat/test-accept_reject_new_seeding_npis.R +++ b/flepimop/R_packages/inference/tests/testthat/test-accept_reject_new_seeding_npis.R @@ -2,20 +2,20 @@ context("accept_reject_new_seeding_npis") test_that("all blocks are accpeted when all proposals are better",{ - seed_orig <- data.frame(place=c(rep("A",5),rep("B",5),rep("C",5)), + seed_orig <- data.frame(subpop=c(rep("A",5),rep("B",5),rep("C",5)), date=16:30, value=1:15) - seed_prop <- data.frame(place=c(rep("A",5),rep("B",5),rep("C",5)), + seed_prop <- data.frame(subpop=c(rep("A",5),rep("B",5),rep("C",5)), date=16:30, value=(1:15)*10) - npis_orig <- data.frame(geoid=c(rep("A",3),rep("B",3),rep("C",3)), + npis_orig <- data.frame(subpop=c(rep("A",3),rep("B",3),rep("C",3)), name=rep(c("X","Y","Z"),3), value=1:9) - npis_prop <- data.frame(geoid=c(rep("A",3),rep("B",3),rep("C",3)), + npis_prop <- data.frame(subpop=c(rep("A",3),rep("B",3),rep("C",3)), name=rep(c("X","Y","Z"),3), value=(1:9)*10) @@ -26,8 +26,8 @@ test_that("all blocks are accpeted when all proposals are better",{ hpar_prop$value <- runif(nrow(hpar_prop)) - orig_lls <- data.frame(geoid=c("A","B","C"),ll=rep(-10,3)) - prop_lls <- data.frame(geoid=c("A","B","C"),ll=rep(-9,3)) + orig_lls <- data.frame(subpop=c("A","B","C"),ll=rep(-10,3)) + prop_lls <- data.frame(subpop=c("A","B","C"),ll=rep(-9,3)) tmp <- accept_reject_new_seeding_npis( @@ -57,20 +57,20 @@ test_that("all blocks are accpeted when all proposals are better",{ test_that("all blocks are rejected when all proposals are 1x10^12 times worse",{ - seed_orig <- data.frame(place=c(rep("A",5),rep("B",5),rep("C",5)), + seed_orig <- data.frame(subpop=c(rep("A",5),rep("B",5),rep("C",5)), date=16:30, value=1:15) - seed_prop <- data.frame(place=c(rep("A",5),rep("B",5),rep("C",5)), + seed_prop <- data.frame(subpop=c(rep("A",5),rep("B",5),rep("C",5)), date=16:30, value=(1:15)*10) - npis_orig <- data.frame(geoid=c(rep("A",3),rep("B",3),rep("C",3)), + npis_orig <- data.frame(subpop=c(rep("A",3),rep("B",3),rep("C",3)), name=rep(c("X","Y","Z"),3), value=1:9) - npis_prop <- data.frame(geoid=c(rep("A",3),rep("B",3),rep("C",3)), + npis_prop <- data.frame(subpop=c(rep("A",3),rep("B",3),rep("C",3)), name=rep(c("X","Y","Z"),3), value=(1:9)*10) @@ -82,8 +82,8 @@ test_that("all blocks are rejected when all proposals are 1x10^12 times worse",{ - orig_lls <- data.frame(geoid=c("A","B","C"),ll=rep(-1,3)) - prop_lls <- data.frame(geoid=c("A","B","C"),ll=rep(-13,3)) + orig_lls <- data.frame(subpop=c("A","B","C"),ll=rep(-1,3)) + prop_lls <- data.frame(subpop=c("A","B","C"),ll=rep(-13,3)) tmp <- accept_reject_new_seeding_npis( @@ -112,20 +112,20 @@ test_that("all blocks are rejected when all proposals are 1x10^12 times worse",{ test_that("only middle block is accepted when appropriate",{ - seed_orig <- data.frame(place=c(rep("A",5),rep("B",5),rep("C",5)), + seed_orig <- data.frame(subpop=c(rep("A",5),rep("B",5),rep("C",5)), date=16:30, value=1:15) - seed_prop <- data.frame(place=c(rep("A",5),rep("B",5),rep("C",5)), + seed_prop <- data.frame(subpop=c(rep("A",5),rep("B",5),rep("C",5)), date=16:30, value=(1:15)*10) - npis_orig <- data.frame(geoid=c(rep("A",3),rep("B",3),rep("C",3)), + npis_orig <- data.frame(subpop=c(rep("A",3),rep("B",3),rep("C",3)), name=rep(c("X","Y","Z"),3), value=1:9) - npis_prop <- data.frame(geoid=c(rep("A",3),rep("B",3),rep("C",3)), + npis_prop <- data.frame(subpop=c(rep("A",3),rep("B",3),rep("C",3)), name=rep(c("X","Y","Z"),3), value=(1:9)*10) @@ -137,9 +137,9 @@ test_that("only middle block is accepted when appropriate",{ hpar_prop$value <- runif(nrow(hpar_prop)) - orig_lls <- data.frame(geoid=c("A","B","C"),ll=rep(-2,3)) - prop_lls <- data.frame(geoid=c("A","B","C"),ll=rep(-15,3)) - prop_lls$ll[prop_lls$geoid=="B"] <- -1 + orig_lls <- data.frame(subpop=c("A","B","C"),ll=rep(-2,3)) + prop_lls <- data.frame(subpop=c("A","B","C"),ll=rep(-15,3)) + prop_lls$ll[prop_lls$subpop=="B"] <- -1 tmp <- accept_reject_new_seeding_npis( @@ -155,9 +155,9 @@ test_that("only middle block is accepted when appropriate",{ prop_lls = prop_lls ) - sd_inds <- which(seed_orig$place!="B") - npi_inds <- which(npis_orig$geoid!="B") - ll_inds <- which(prop_lls$geoid!="B") + sd_inds <- which(seed_orig$subpop!="B") + npi_inds <- which(npis_orig$subpop!="B") + ll_inds <- which(prop_lls$subpop!="B") expect_that(tmp$seeding$value[sd_inds], equals(seed_orig$value[sd_inds])) expect_that(tmp$snpi$value[npi_inds], equals(npis_orig$value[npi_inds])) @@ -165,9 +165,9 @@ test_that("only middle block is accepted when appropriate",{ expect_that(tmp$lls$ll[ll_inds], equals(orig_lls$ll[ll_inds])) - sd_inds <- which(seed_orig$place=="B") - npi_inds <- which(npis_orig$geoid=="B") - ll_inds <- which(prop_lls$geoid=="B") + sd_inds <- which(seed_orig$subpop=="B") + npi_inds <- which(npis_orig$subpop=="B") + ll_inds <- which(prop_lls$subpop=="B") expect_that(tmp$seeding$value[sd_inds], equals(seed_prop$value[sd_inds])) expect_that(tmp$snpi$value[npi_inds], equals(npis_prop$value[npi_inds])) diff --git a/flepimop/R_packages/inference/tests/testthat/test-aggregate_and_calc_loc_likelihoods.R b/flepimop/R_packages/inference/tests/testthat/test-aggregate_and_calc_loc_likelihoods.R index e6b0be24a..6fcb39f15 100644 --- a/flepimop/R_packages/inference/tests/testthat/test-aggregate_and_calc_loc_likelihoods.R +++ b/flepimop/R_packages/inference/tests/testthat/test-aggregate_and_calc_loc_likelihoods.R @@ -7,25 +7,25 @@ context("aggregate_and_calc_loc_likelihoods") ##' get_minimal_setup <- function () { - #3geoids - geoids <- c("06001", "06002", "06003", "32001","32002","32003") + #3subpop + subpop <- c("06001", "06002", "06003", "32001","32002","32003") USPS <- c(rep("CA",3), rep("NY",3)) ##list of lcations to consider...all of them - all_locations <- geoids + all_locations <- subpop - obs_nodename <- "geoid" + obs_subpop <- "subpop" - ##Generate observed data per geoid the simulated data will be compared too + ##Generate observed data per subpop the simulated data will be compared too ##TODO times <- seq(as.Date("2020-02-15"),as.Date("2020-06-30"), by="days") day <- 1:length(times) obs_sims <- list() - for (i in 1:length(geoids)) { + for (i in 1:length(subpop)) { obs_sims[[i]] <- dplyr::tibble(date = times, - geoid = geoids[i], + subpop = subpop[i], death_incid = rpois(length(day), 1000*dnorm(day, 32, 10)), confirmed_incid = rpois(length(day), 10000*dnorm(day, 32, 10))) } @@ -33,7 +33,7 @@ get_minimal_setup <- function () { ##Aggregate the observed data to the appropriate level - geonames <- unique(obs[[obs_nodename]]) + geonames <- unique(obs[[obs_subpop]]) ##minimal confif information used by function config <- list() @@ -67,7 +67,7 @@ get_minimal_setup <- function () { data_stats <- lapply( geonames, function(x) { - df <- obs[obs[[obs_nodename]] == x, ] + df <- obs[obs[[obs_subpop]] == x, ] inference::getStats( df, "date", @@ -76,7 +76,7 @@ get_minimal_setup <- function () { }) %>% setNames(geonames) - ##Simulated data per geoid, multiple vars. Just perturb obs by default + ##Simulated data per subpop, multiple vars. Just perturb obs by default sim_hosp <- obs %>% dplyr::rename(incidD = death_incid, incidC = confirmed_incid) %>% dplyr::mutate(incidD = incidD + rpois(length(incidD), incidD))%>% @@ -84,7 +84,7 @@ get_minimal_setup <- function () { dplyr::rename(time=date) ##the observed node name. - obs_nodename <- "geoid" + obs_subpop <- "subpop" @@ -100,26 +100,26 @@ get_minimal_setup <- function () { ##geodata data frame - geodata <- dplyr::tibble(geoid = geoids, + geodata <- dplyr::tibble(subpop = subpop, USPS = USPS) ##The file containing information on the given npis. Creating 2 by default. - npi1 <- dplyr::tibble(geoid=geoids, + npi1 <- dplyr::tibble(subpop=subpop, npi_name = "local_variance", start_date = "2020-01-01", end_date = "2020-06-30", parameter = "r0", reduction = runif(6,-.5, .5)) - npi2A <- dplyr::tibble(geoid = geoids[1:3], + npi2A <- dplyr::tibble(subpop = subpop[1:3], npi_name = "full_lockdown_CA", start_date = "2020-03-25", end_date = "2020-06-01", parameter = "r0", reduction = runif(3,-.8, -.5)) - npi2B <- dplyr::tibble(geoid = geoids[4:6], + npi2B <- dplyr::tibble(subpop = subpop[4:6], npi_name = "full_lockdown_NY", start_date = "2020-03-15", end_date = "2020-05-22", @@ -129,14 +129,14 @@ get_minimal_setup <- function () { snpi <- dplyr::bind_rows(npi1, npi2A, npi2B) ##The file containing information on the given hospitalization npis. Creating 2 by default. - npi1 <- dplyr::tibble(geoid=geoids, + npi1 <- dplyr::tibble(subpop=subpop, npi_name = "local_variance", start_date = "2020-01-01", end_date = "2020-06-30", parameter = "hosp::inf", reduction = runif(6,-.5, .5)) - npi2 <- dplyr::tibble(geoid = geoids[1:3], + npi2 <- dplyr::tibble(subpop = subpop[1:3], npi_name = "full_lockdown_CA", start_date = "2020-03-25", end_date = "2020-06-01", @@ -147,11 +147,11 @@ get_minimal_setup <- function () { hnpi <- dplyr::bind_rows(npi1, npi2) ##Set up hospitalizatoin params. - hpar1 <- dplyr::tibble(geoid=geoids, + hpar1 <- dplyr::tibble(subpop=subpop, parameter="p_confirmed_inf", value=0.1) - hpar2 <- dplyr::tibble(geoid=geoids, + hpar2 <- dplyr::tibble(subpop=subpop, parameter="p_hosp_inf", value=.07) @@ -160,7 +160,7 @@ get_minimal_setup <- function () { return(list(all_locations=all_locations, sim_hosp=sim_hosp, - obs_nodename=obs_nodename, + obs_subpop=obs_subpop, config=config, obs=obs, data_stats=data_stats, @@ -182,7 +182,7 @@ test_that("aggregate_and_calc_loc_likelihoods returns a likelihood per location tmp <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -195,7 +195,7 @@ test_that("aggregate_and_calc_loc_likelihoods returns a likelihood per location expect_that(nrow(tmp), equals(length(stuff$all_locations))) - expect_that(sort(colnames(tmp)), equals(sort(c("ll","accept","accept_prob","accept_avg","filename",stuff$obs_nodename)))) + expect_that(sort(colnames(tmp)), equals(sort(c("ll","accept","accept_prob","accept_avg","filename",stuff$obs_subpop)))) }) @@ -212,7 +212,7 @@ test_that("likelihood of perfect data is less that likelihood of imperfect data" tmp1 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -226,7 +226,7 @@ test_that("likelihood of perfect data is less that likelihood of imperfect data" tmp2 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = alt_sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -262,7 +262,7 @@ test_that("removing deaths as a stat makes the likelihood invariant to changes i tmp1 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -278,7 +278,7 @@ test_that("removing deaths as a stat makes the likelihood invariant to changes i tmp2 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = alt_sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -297,7 +297,7 @@ test_that("removing deaths as a stat makes the likelihood invariant to changes i tmp1 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -313,7 +313,7 @@ test_that("removing deaths as a stat makes the likelihood invariant to changes i tmp2 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = alt_sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -348,7 +348,7 @@ test_that("removing confirmed as a stat makes the likelihood invariant to change tmp1 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -364,7 +364,7 @@ test_that("removing confirmed as a stat makes the likelihood invariant to change tmp2 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = alt_sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -383,7 +383,7 @@ test_that("removing confirmed as a stat makes the likelihood invariant to change tmp1 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -399,7 +399,7 @@ test_that("removing confirmed as a stat makes the likelihood invariant to change tmp2 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = alt_sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -427,7 +427,7 @@ test_that("likelihoood insenstive to parameters with no multi-level compoenent o tmp1 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -443,7 +443,7 @@ test_that("likelihoood insenstive to parameters with no multi-level compoenent o tmp2 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -481,7 +481,7 @@ test_that("likelihood is senstive to changes to correct npi paramerers when mult tmp1 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -497,7 +497,7 @@ test_that("likelihood is senstive to changes to correct npi paramerers when mult tmp2 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -512,7 +512,7 @@ test_that("likelihood is senstive to changes to correct npi paramerers when mult tmp3 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -552,7 +552,7 @@ test_that("likelihood is sensitive to changes to correct hpar parameters when mu tmp1 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -569,7 +569,7 @@ test_that("likelihood is sensitive to changes to correct hpar parameters when mu tmp2 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -585,7 +585,7 @@ test_that("likelihood is sensitive to changes to correct hpar parameters when mu tmp3 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -626,7 +626,7 @@ test_that("when prior is specified, likilhood is higher when nearer prior mean f tmp1 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -642,7 +642,7 @@ test_that("when prior is specified, likilhood is higher when nearer prior mean f tmp2 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -657,7 +657,7 @@ test_that("when prior is specified, likilhood is higher when nearer prior mean f tmp3 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -697,7 +697,7 @@ test_that("when prior is specified, likilhood is higher when nearer prior mean f tmp1 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -714,7 +714,7 @@ test_that("when prior is specified, likilhood is higher when nearer prior mean f tmp2 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -730,7 +730,7 @@ test_that("when prior is specified, likilhood is higher when nearer prior mean f tmp3 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -775,7 +775,7 @@ test_that("Hierarchical structure works on interventions not defined for all loc tmp1 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -791,7 +791,7 @@ test_that("Hierarchical structure works on interventions not defined for all loc tmp2 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -806,7 +806,7 @@ test_that("Hierarchical structure works on interventions not defined for all loc tmp3 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, @@ -821,7 +821,7 @@ test_that("Hierarchical structure works on interventions not defined for all loc tmp4 <- aggregate_and_calc_loc_likelihoods( all_locations = stuff$all_locations, modeled_outcome = stuff$sim_hosp, - obs_nodename = stuff$obs_nodename, + obs_subpop = stuff$obs_subpop, targets_config = stuff$config[['inference']][['statistics']], obs = stuff$obs, ground_truth_data = stuff$data_stats, diff --git a/flepimop/R_packages/inference/tests/testthat/test-calc_hierarchical_likadj.R b/flepimop/R_packages/inference/tests/testthat/test-calc_hierarchical_likadj.R index 93245f1ad..86379b019 100644 --- a/flepimop/R_packages/inference/tests/testthat/test-calc_hierarchical_likadj.R +++ b/flepimop/R_packages/inference/tests/testthat/test-calc_hierarchical_likadj.R @@ -7,7 +7,7 @@ test_that("penalty is based on selected stat", { npi2 <- runif (6,-1,1) ##makes data frame with stats - infer_frame <- data.frame(geoid=rep(c("01001","01002","01003", + infer_frame <- data.frame(subpop=rep(c("01001","01002","01003", "06001", "06002", "06003"),2), npi_name=rep(c("npi 1", "npi 2"), each=6), reduction=c(npi1,npi2)) @@ -16,7 +16,7 @@ test_that("penalty is based on selected stat", { ##make geodata dataframe - geodata <- data.frame(geoid=c("01001","01002","01003", + geodata <- data.frame(subpop=c("01001","01002","01003", "06001", "06002","06003"), USPS=rep(c("HI","CA"), each=3)) @@ -46,7 +46,7 @@ test_that("NPIs with equal values have highe LL than npis with different values" npi2 <- rep(runif (1,-1,1),6) ##makes data frame with stats - infer_frame <- data.frame(geoid=rep(c("01001","01002","01003", + infer_frame <- data.frame(subpop=rep(c("01001","01002","01003", "06001", "06002", "06003"),2), npi_name=rep(c("npi 1", "npi 2"), each=6), reduction=c(npi1,npi2)) @@ -55,7 +55,7 @@ test_that("NPIs with equal values have highe LL than npis with different values" ##make geodata dataframe - geodata <- data.frame(geoid=c("01001","01002","01003", + geodata <- data.frame(subpop=c("01001","01002","01003", "06001", "06002","06003"), USPS=rep(c("HI","CA"), each=3)) @@ -81,7 +81,7 @@ test_that("Groups with equal values have highe LL than npis with different value npi2 <- c(rep(runif(1,-1,1),3),runif(3,-1,1)) ##makes data frame with stats - infer_frame <- data.frame(geoid=rep(c("01001","01002","01003", + infer_frame <- data.frame(subpop=rep(c("01001","01002","01003", "06001", "06002", "06003"),2), npi_name=rep(c("npi 1", "npi 2"), each=6), reduction=c(npi1,npi2)) @@ -90,7 +90,7 @@ test_that("Groups with equal values have highe LL than npis with different value ##make geodata dataframe - geodata <- data.frame(geoid=c("01001","01002","01003", + geodata <- data.frame(subpop=c("01001","01002","01003", "06001", "06002","06003"), USPS=rep(c("HI","CA"), each=3)) @@ -127,7 +127,7 @@ test_that("equal values use minimum variance", { npi1 <- rep(1,3) ##makes data frame with stats - infer_frame <- dplyr::tibble(geoid=c("01001","01002","01003"), + infer_frame <- dplyr::tibble(subpop=c("01001","01002","01003"), npi_name=rep("npi 1", 3), reduction=npi1) @@ -135,7 +135,7 @@ test_that("equal values use minimum variance", { ##make geodata dataframe - geodata <- dplyr::tibble(geoid=c("01001","01002","01003", + geodata <- dplyr::tibble(subpop=c("01001","01002","01003", "06001", "06002","06003"), USPS=rep(c("HI","CA"), each=3)) @@ -155,13 +155,13 @@ test_that("transforms give the appropriate likelihoods", { # val <- c(0.25698943, 0.23411552, 0.09412548) ##makes data frame with stats - infer_frame <- dplyr::tibble(geoid=c("01001","01002","01003"), + infer_frame <- dplyr::tibble(subpop=c("01001","01002","01003"), npi_name=rep("val1", each=3), value=val) ##make geodata dataframe - geodata <- dplyr::tibble(geoid=c("01001","01002","01003", + geodata <- dplyr::tibble(subpop=c("01001","01002","01003", "06001", "06002","06003"), USPS=rep(c("HI","CA"), each=3)) @@ -197,18 +197,18 @@ test_that("transforms give the appropriate likelihoods", { }) -test_that("sensible things are returned whern there is only 1 geoid in a location", { +test_that("sensible things are returned whern there is only 1 subpop in a location", { val<- runif(4,0,1) ##makes data frame with stats - infer_frame <- dplyr::tibble(geoid=c("01001", "06001", "06002","06003"), + infer_frame <- dplyr::tibble(subpop=c("01001", "06001", "06002","06003"), npi_name=rep("val1", 4), value=val) ##make geodata dataframe - geodata <- dplyr::tibble(geoid=c("01001","01002","01003", + geodata <- dplyr::tibble(subpop=c("01001","01002","01003", "06001", "06002","06003"), USPS=rep(c("HI","CA"), each=3)) @@ -222,8 +222,8 @@ test_that("sensible things are returned whern there is only 1 geoid in a locatio ##print(adj) - ##make sure that the one geoid thing is zero - expect_true(!is.na(adj$likadj[adj$geoid=="01001"])) + ##make sure that the one subpop thing is zero + expect_true(!is.na(adj$likadj[adj$subpop=="01001"])) }) @@ -234,13 +234,13 @@ test_that("logit transform does not blow up on 0 or 1", { val[2] <- 1 ##makes data frame with stats - infer_frame <- dplyr::tibble(geoid=c("01001","01002","01003"), + infer_frame <- dplyr::tibble(subpop=c("01001","01002","01003"), npi_name=rep("val1", each=3), value=val) ##make geodata dataframe - geodata <- dplyr::tibble(geoid=c("01001","01002","01003", + geodata <- dplyr::tibble(subpop=c("01001","01002","01003", "06001", "06002","06003"), USPS=rep(c("HI","CA"), each=3)) diff --git a/flepimop/R_packages/inference/tests/testthat/test-perturb_npis.R b/flepimop/R_packages/inference/tests/testthat/test-perturb_npis.R index e1df1baca..e8d506237 100644 --- a/flepimop/R_packages/inference/tests/testthat/test-perturb_npis.R +++ b/flepimop/R_packages/inference/tests/testthat/test-perturb_npis.R @@ -3,7 +3,7 @@ context("perturb_npis") test_that("perturb_snpi always stays within support", { N <- 10000 npis <- data.frame( - geoid = rep('00000',times=N), + subpop = rep('00000',times=N), npi_name = rep("test_npi",times=N), start_date = rep("2020-02-01",times=N), end_date = rep("2020-02-02",times=N), @@ -11,7 +11,7 @@ test_that("perturb_snpi always stays within support", { reduction = rep(-.099,times=N) ) npi_settings <- list(test_npi = list( - template = "Reduce", + template = "SinglePeriodModifier", parameter = "r0", value = list( distribution = "truncnorm", @@ -35,7 +35,7 @@ test_that("perturb_snpi always stays within support", { test_that("perturb_snpi has a median of 0 after 10000 sims",{ N <- 10000 npis <- data.frame( - geoid = rep('00000',times=N), + subpop = rep('00000',times=N), npi_name = rep("test_npi",times=N), start_date = rep("2020-02-01",times=N), end_date = rep("2020-02-02",times=N), @@ -43,7 +43,7 @@ test_that("perturb_snpi has a median of 0 after 10000 sims",{ reduction = rep(0,times=N) ) npi_settings <- list( - template = "Reduce", + template = "SinglePeriodModifier", parameter = "r0", value = list( distribution = "truncnorm", @@ -79,7 +79,7 @@ test_that("perturb_snpi has a median of 0 after 10000 sims",{ test_that("perturb_snpi does not perturb npis without a perturbation section", { N <- 10000 npis <- data.frame( - geoid = rep('00000',times=N), + subpop = rep('00000',times=N), npi_name = rep("test_npi",times=N), start_date = rep("2020-02-01",times=N), end_date = rep("2020-02-02",times=N), @@ -87,7 +87,7 @@ test_that("perturb_snpi does not perturb npis without a perturbation section", { reduction = rep(-.099,times=N) ) npi_settings <- list(test_npi = list( - template = "Reduce", + template = "SinglePeriodModifier", parameter = "r0", value = list( distribution = "truncnorm", diff --git a/flepimop/R_packages/inference/tests/testthat/test-perturb_seeding.R b/flepimop/R_packages/inference/tests/testthat/test-perturb_seeding.R index b64e1a6c1..420f71e73 100644 --- a/flepimop/R_packages/inference/tests/testthat/test-perturb_seeding.R +++ b/flepimop/R_packages/inference/tests/testthat/test-perturb_seeding.R @@ -3,7 +3,7 @@ context("perturb_seeding") test_that("seeding date always stays within date bounds", { N <- 10000 seeding <- data.frame(date=rep(as.Date("2020-02-01"), N), - place=1:N, + subpop=1:N, amount=rep(10,N)) date_bounds <- as.Date(c("2020-01-31", "2020-02-02")) @@ -18,7 +18,7 @@ test_that("seeding date always stays within date bounds", { test_that("the median of the seeding pertubations is 0 after 10000 sims", { N <- 10000 seeding <- data.frame(date=rep(as.Date("2020-02-01"), N), - place=1:N, + subpop=1:N, amount=rep(10,N)) date_bounds <- as.Date(c("2020-01-20", "2020-02-20")) diff --git a/flepimop/gempyor_pkg/docs/Rinterface.Rmd b/flepimop/gempyor_pkg/docs/Rinterface.Rmd index 16145addb..af1d61d44 100644 --- a/flepimop/gempyor_pkg/docs/Rinterface.Rmd +++ b/flepimop/gempyor_pkg/docs/Rinterface.Rmd @@ -44,10 +44,10 @@ gempyor <- reticulate::import("gempyor") ### Building a simulator -We create an `InferenceSimulator` object by providing the path of config file. The config used here, from the npi continuous integration tests, is derived from our recent forcast hub config: `config_FCH_R12_optSev_lowIE_blk5_Mar6.yml` on March 6, 2022. +We create an `GempyorSimulator` object by providing the path of config file. The config used here, from the npi continuous integration tests, is derived from our recent forcast hub config: `config_FCH_R12_optSev_lowIE_blk5_Mar6.yml` on March 6, 2022. ```{r} config_filepath = '../tests/npi/config_npi.yml' -gempyor_simulator <- gempyor$InferenceSimulator( +gempyor_simulator <- gempyor$GempyorSimulator( config_path=config_filepath, run_id="test_run_id", prefix="test_prefix/", @@ -115,12 +115,12 @@ We can also get the reduction in time that applies to each parameter. This is a reduc <- npi_seir$getReduction(param = 'r0') -reduc <- reduc %>% rownames_to_column(var = 'geoid') +reduc <- reduc %>% rownames_to_column(var = 'subpop') reduc <- reduc %>% - pivot_longer(cols=colnames(reduc %>% select(-geoid)), names_to = "date", values_to = "reduction") %>% + pivot_longer(cols=colnames(reduc %>% select(-subpop)), names_to = "date", values_to = "reduction") %>% mutate(date=as.Date(date)) # let's plot it: -reduc %>% ggplot(aes(x=date, y=reduction)) + geom_path() + facet_wrap(~ geoid) +reduc %>% ggplot(aes(x=date, y=reduction)) + geom_path() + facet_wrap(~ subpop) ``` Now the same for outcome. We can check which parameters gets modified by this NPI by using the `getReductionDF()` method: @@ -132,12 +132,12 @@ as it is only `inciditoc_all` here, we can plot it ```{r, fig.show=TRUE} reduc <- npi_outcome$getReduction(param = 'inciditoc_all') # There is a bit of R to get it to something usable, it's probably a very ugly way to do this: -reduc <- reduc %>% rownames_to_column(var = 'geoid') +reduc <- reduc %>% rownames_to_column(var = 'subpop') reduc <- reduc %>% - pivot_longer(cols=colnames(reduc %>% select(-geoid)), names_to = "date", values_to = "reduction") %>% + pivot_longer(cols=colnames(reduc %>% select(-subpop)), names_to = "date", values_to = "reduction") %>% mutate(date=as.Date(date)) -reduc %>% ggplot(aes(x=date, y=reduction)) + geom_path() + facet_wrap(~ geoid) +reduc %>% ggplot(aes(x=date, y=reduction)) + geom_path() + facet_wrap(~ subpop) ``` @@ -149,15 +149,15 @@ param_reduc = gempyor_simulator$get_seir_parameter_reduced(npi_seir=npi_seir) # # We can also provide an array as returned by gempyor_simulator$get_seir_parameters() param_reduc_from = gempyor_simulator$get_seir_parameter_reduced(npi_seir=npi_seir, p_draw=params_draw_arr) -param_reduc <- param_reduc %>% pivot_longer(cols = param_reduc %>% select(-date, -geoid) %>% colnames(), names_to = 'parameter') +param_reduc <- param_reduc %>% pivot_longer(cols = param_reduc %>% select(-date, -subpop) %>% colnames(), names_to = 'parameter') -param_reduc %>% filter(parameter=='r0') %>% ggplot(aes(x=date, y=value)) + geom_line() + facet_wrap(~geoid) +param_reduc %>% filter(parameter=='r0') %>% ggplot(aes(x=date, y=value)) + geom_line() + facet_wrap(~subpop) ``` Let's plot the vaccination rate, the same way, from the same dataframe: ```{r, fig.show=TRUE} -param_reduc %>% filter(parameter=='nu1age18to64') %>% ggplot(aes(x=date, y=value)) + geom_path() + facet_wrap(~ geoid) +param_reduc %>% filter(parameter=='nu1age18to64') %>% ggplot(aes(x=date, y=value)) + geom_path() + facet_wrap(~ subpop) ``` ### Get compartment graph image diff --git a/flepimop/gempyor_pkg/docs/Rinterface.html b/flepimop/gempyor_pkg/docs/Rinterface.html index bdbeb1978..26cd8f05c 100644 --- a/flepimop/gempyor_pkg/docs/Rinterface.html +++ b/flepimop/gempyor_pkg/docs/Rinterface.html @@ -240,21 +240,21 @@

Import

library(ggplot2)
 library(tibble)
 # reticulate::use_python(Sys.which('python'),require=TRUE)
-reticulate::use_condaenv('flepimop-env')   
+reticulate::use_condaenv('flepimop-env')
 gempyor <- reticulate::import("gempyor")

Building a simulator

-

We create an InferenceSimulator object by providing the path of config file. It may take a while to run all of that. First build the object. The config used here, from the npi continuous integration tests, is derived from our recent forcast hub config: config_FCH_R12_optSev_lowIE_blk5_Mar6.yml on March 6, 2022.

+

We create an GempyorSimulator object by providing the path of config file. It may take a while to run all of that. First build the object. The config used here, from the npi continuous integration tests, is derived from our recent forcast hub config: config_FCH_R12_optSev_lowIE_blk5_Mar6.yml on March 6, 2022.

config_filepath = '../tests/npi/config_npi.yml'
-gempyor_simulator <- gempyor$InferenceSimulator(
+gempyor_simulator <- gempyor$GempyorSimulator(
                           config_path=config_filepath,
                           run_id="test_run_id",
                           prefix="test_prefix/",
                           first_sim_index=1,
                           npi_scenario="inference",   # NPIs scenario to use
                           outcome_scenario="med",        # Outcome scenario to use
-                          stoch_traj_flag=FALSE,      
+                          stoch_traj_flag=FALSE,
                           spatial_path_prefix = '../tests/npi/' # prefix where to find the folder indicated in spatial_setup
 )

Here we specify that the data folder specified in the config lies in the test/npi/ folder, not in the current directory. The only mandatory arguments is the config_path. The default values of the other arguments are

@@ -266,7 +266,7 @@

Building a simulator

stoch_traj_flag=False, rng_seed=None, nslots=1, - initialize=True, + initialize=True, out_run_id=None, # if out_run_id should be different from in_run_id, put it here out_prefix=None, # if out_prefix should be different from in_prefix, put it here spatial_path_prefix="", # in case the data folder is on another directory @@ -285,7 +285,7 @@

Exploration methods

Parameters

It is possible to draw the parameters of the disease dynamics. The following line draw from config (hence each call will return a different draw from the prior), but the syntax would be the same with load_ID, bypass_FN, bypass_DF, where a spar file would be loaded.

-
# this variation returns a dataframe. 
+
# this variation returns a dataframe.
 params_draw_df = gempyor_simulator$get_seir_parametersDF()   # could also accept (load_ID=True, sim_id2load=XXX) or (bypass_DF=<some_spar_df>) or (bypass_FN=<some_spar_filename>)
 
 ## This return an array, which is useful together with a NPI to get the reduce parameter (cf. later in the tutorial)
@@ -309,19 +309,19 @@ 
NPIs

 
npi_seir$getReductionDF()

 

 
 

 
We can also get the reduction in time that applies to each parameter. This is a time-serie. The parameter should be lower case (This will be removed soon, TODO).

 
reduc <- npi_seir$getReduction(param = 'r0')
 
 
-reduc <- reduc %>% rownames_to_column(var = 'geoid') 
-reduc <- reduc %>% 
-  pivot_longer(cols=colnames(reduc %>% select(-geoid)), names_to = "date", values_to = "reduction") %>% 
-  mutate(date=as.Date(date))  
+reduc <- reduc %>% rownames_to_column(var = 'subpop')
+reduc <- reduc %>%
+  pivot_longer(cols=colnames(reduc %>% select(-subpop)), names_to = "date", values_to = "reduction") %>%
+  mutate(date=as.Date(date))
 # let's plot it:
-reduc %>% ggplot(aes(x=date, y=reduction)) + geom_path() + facet_wrap(~ geoid)

+reduc %>% ggplot(aes(x=date, y=reduction)) + geom_path() + facet_wrap(~ subpop)

 

 
Now the same for outcome. We can check which parameters gets modified by this NPI by using the getReductionDF() method:

 
npi_outcome$getReductionDF() %>% select('parameter') %>% unique()

@@ -333,35 +333,35 @@ 
NPIs

 
as it is only inciditoc_all here, we can plot it

 
reduc <- npi_outcome$getReduction(param = 'inciditoc_all')
 # There is a bit of R to get it to something usable, it's probably a very ugly way to do this:
-reduc <- reduc %>% rownames_to_column(var = 'geoid') 
-reduc <- reduc %>% 
-  pivot_longer(cols=colnames(reduc %>% select(-geoid)), names_to = "date", values_to = "reduction") %>%
+reduc <- reduc %>% rownames_to_column(var = 'subpop')
+reduc <- reduc %>%
+  pivot_longer(cols=colnames(reduc %>% select(-subpop)), names_to = "date", values_to = "reduction") %>%
   mutate(date=as.Date(date))
 
-reduc %>% ggplot(aes(x=date, y=reduction)) + geom_path() + facet_wrap(~ geoid)

+reduc %>% ggplot(aes(x=date, y=reduction)) + geom_path() + facet_wrap(~ subpop)

SEIR Parameters, but reduced

We can also plot the pameters after reduction with the npi. We just have to provided the npi object. The reduction contains all parameter. Here we build it and plot R0 in time (not that the trends are inverted from the getReduction above ^)

-
# This will draw new parameters from config and applies the already defined NPI. If load_ID, bypass_DF or bypass_FN 
+
# This will draw new parameters from config and applies the already defined NPI. If load_ID, bypass_DF or bypass_FN
 param_reduc = gempyor_simulator$get_seir_parameter_reduced(npi_seir=npi_seir) # could also accept (load_ID=True, sim_id2load=XXX) or (bypass_DF=<some_spar_df>) or (bypass_FN=<some_spar_filename>)
 
-# We can also provide an array as returned by gempyor_simulator$get_seir_parameters() 
+# We can also provide an array as returned by gempyor_simulator$get_seir_parameters()
 param_reduc_from = gempyor_simulator$get_seir_parameter_reduced(npi_seir=npi_seir, p_draw=params_draw_arr)
-param_reduc <- param_reduc %>% pivot_longer(cols = param_reduc %>% select(-date, -geoid) %>% colnames(), names_to = 'parameter')
+param_reduc <- param_reduc %>% pivot_longer(cols = param_reduc %>% select(-date, -subpop) %>% colnames(), names_to = 'parameter')
 
-param_reduc %>% filter(parameter=='r0') %>% ggplot(aes(x=date, y=value)) + geom_line() + facet_wrap(~geoid)

+param_reduc %>% filter(parameter=='r0') %>% ggplot(aes(x=date, y=value)) + geom_line() + facet_wrap(~subpop)

Let’s plot the vaccination rate, the same way, from the same dataframe:

-
param_reduc %>% filter(parameter=='nu1age18to64') %>% ggplot(aes(x=date, y=value)) + geom_path() + facet_wrap(~ geoid)
+
param_reduc %>% filter(parameter=='nu1age18to64') %>% ggplot(aes(x=date, y=value)) + geom_path() + facet_wrap(~ subpop)

Get compartment graph image

We can plot the compartment transition graph with this config. There is a possibility to apply filters in order to have tractable graph. The graph is plotted as a separate pdf file.

gempyor_simulator$plot_transition_graph(output_file="full_graph")
-gempyor_simulator$plot_transition_graph(output_file="readable_graph", 
-                                        source_filters= list(list("age0to17"), list("OMICRON", "WILD")), 
+gempyor_simulator$plot_transition_graph(output_file="readable_graph",
+                                        source_filters= list(list("age0to17"), list("OMICRON", "WILD")),
                                         destination_filters= list(list("OMICRON", "WILD")))

here if source_filters is [[“age0to17”], [“OMICRON”, “WILD”]], it means filter (keep) all transitions that have as source: age0to17 AND (OMICRON OR WILD).

diff --git a/flepimop/gempyor_pkg/docs/integration_benchmark.ipynb b/flepimop/gempyor_pkg/docs/integration_benchmark.ipynb index eb6094c93..e392401cd 100644 --- a/flepimop/gempyor_pkg/docs/integration_benchmark.ipynb +++ b/flepimop/gempyor_pkg/docs/integration_benchmark.ipynb @@ -200,12 +200,12 @@ "\n", "s = setup.Setup(\n", " setup_name=config[\"name\"].get() + \"_\" + str(npi_scenario),\n", - " spatial_setup=setup.SpatialSetup(\n", + " spatial_setup=subpopulation_structure.SubpopulationStructure(\n", " setup_name=config[\"setup_name\"].get(),\n", " geodata_file=spatial_base_path / spatial_config[\"geodata\"].get(),\n", " mobility_file=spatial_base_path / spatial_config[\"mobility\"].get(),\n", " popnodes_key=spatial_config[\"popnodes\"].get(),\n", - " nodenames_key=spatial_config[\"nodenames\"].get(),\n", + " subpop_key=spatial_config[\"subpop\"].get(),\n", " ),\n", " nslots=nslots,\n", " npi_scenario=npi_scenario,\n", @@ -307,7 +307,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", "):\n", @@ -331,7 +331,7 @@ " keys_ref = [\n", " \"seeding_sources\",\n", " \"seeding_destinations\",\n", - " \"seeding_places\",\n", + " \"seeding_subpops\",\n", " \"day_start_idx\",\n", " ]\n", " for key, item in seeding_data.items():\n", @@ -347,8 +347,8 @@ " assert len(mobility_data) > 0\n", "\n", " assert type(mobility_data[0]) == np.float64\n", - " assert len(mobility_data) == len(mobility_geoid_indices)\n", - " assert type(mobility_geoid_indices[0]) == np.int32\n", + " assert len(mobility_data) == len(mobility_subpop_indices)\n", + " assert type(mobility_subpop_indices[0]) == np.int32\n", " assert len(mobility_data_indices) == s.nnodes + 1\n", " assert type(mobility_data_indices[0]) == np.int32\n", " assert len(s.popnodes) == s.nnodes\n", @@ -367,7 +367,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " s.popnodes,\n", " stoch_traj_flag,\n", @@ -444,7 +444,7 @@ " npi = NPI.NPIBase.execute(\n", " npi_config=s.npi_config,\n", " global_config=config,\n", - " geoids=s.spatset.nodenames,\n", + " subpop=s.subpop_struct.subpop,\n", " pnames_overlap_operation_sum=s.parameters.intervention_overlap_operation[\"sum\"],\n", " )\n", "\n", @@ -452,7 +452,7 @@ " initial_conditions = s.seedingAndIC.draw_ic(sim_id, setup=s)\n", " seeding_data, seeding_amounts = s.seedingAndIC.draw_seeding(sim_id, setup=s)\n", "\n", - "mobility_geoid_indices = s.mobility.indices\n", + "mobility_subpop_indices = s.mobility.indices\n", "mobility_data_indices = s.mobility.indptr\n", "mobility_data = s.mobility.data\n", "\n", @@ -561,7 +561,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -626,7 +626,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -729,7 +729,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -792,7 +792,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -855,7 +855,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -932,7 +932,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -991,7 +991,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -1050,7 +1050,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -1096,7 +1096,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -1154,7 +1154,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -1175,7 +1175,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -12050,7 +12050,7 @@ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/var/folders/y5/jj4qlxkx619gkh07d2zt6h840000gn/T/ipykernel_76044/342140651.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegration_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmet\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m states = steps_SEIR(\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mparsed_parameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/var/folders/y5/jj4qlxkx619gkh07d2zt6h840000gn/T/ipykernel_76044/2880317610.py\u001b[0m in \u001b[0;36msteps_SEIR\u001b[0;34m(s, parsed_parameters, transition_array, proportion_array, proportion_info, initial_conditions, seeding_data, seeding_amounts, mobility_data, mobility_geoid_indices, mobility_data_indices, stoch_traj_flag)\u001b[0m\n\u001b[1;32m 96\u001b[0m raise ValueError(f\"with method {s.integration_method}, only deterministic\"\n\u001b[1;32m 97\u001b[0m f\"integration is possible (got stoch_straj_flag={stoch_traj_flag}\")\n\u001b[0;32m---> 98\u001b[0;31m \u001b[0mseir_sim\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m/var/folders/y5/jj4qlxkx619gkh07d2zt6h840000gn/T/ipykernel_76044/2880317610.py\u001b[0m in \u001b[0;36msteps_SEIR\u001b[0;34m(s, parsed_parameters, transition_array, proportion_array, proportion_info, initial_conditions, seeding_data, seeding_amounts, mobility_data, mobility_subpop_indices, mobility_data_indices, stoch_traj_flag)\u001b[0m\n\u001b[1;32m 96\u001b[0m raise ValueError(f\"with method {s.integration_method}, only deterministic\"\n\u001b[1;32m 97\u001b[0m f\"integration is possible (got stoch_straj_flag={stoch_traj_flag}\")\n\u001b[0;32m---> 98\u001b[0;31m \u001b[0mseir_sim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msteps_ode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mode_integration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfnct_args\u001b[0m\u001b[0;34m,\u001b[0m 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transition_sum_compartments, initial_conditions, seeding_data, seeding_amounts, mobility_data, mobility_row_indices, mobility_data_indices, population, stochastic_p, integration_method)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0mx_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mintegration_method\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'scipy.solve_ivp'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m \u001b[0msol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolve_ivp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrhs_wrapper\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36msolve_ivp\u001b[0;34m(fun, t_span, y0, method, t_eval, dense_output, events, vectorized, args, **options)\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 575\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'finished'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/opt/miniconda3/envs/covidSProd6/lib/python3.9/site-packages/scipy/integrate/_ivp/base.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_step_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", @@ -12083,7 +12083,7 @@ " seeding_data,\n", " seeding_amounts,\n", " mobility_data,\n", - " mobility_geoid_indices,\n", + " mobility_subpop_indices,\n", " mobility_data_indices,\n", " stoch_traj_flag,\n", " )\n", @@ -12569,19 +12569,19 @@ " seeding_data['day_start_idx'][today + 1]\n", " ):\n", " this_seeding_amounts = seeding_amounts[seeding_instance_idx]\n", - " seeding_places = seeding_data['seeding_places'][seeding_instance_idx]\n", + " seeding_subpops = seeding_data['seeding_subpops'][seeding_instance_idx]\n", " seeding_sources = seeding_data['seeding_sources'][seeding_instance_idx]\n", " seeding_destinations = seeding_data['seeding_destinations'][seeding_instance_idx]\n", " # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx]\n", - " states_next[seeding_sources][seeding_places] -= this_seeding_amounts\n", - " states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * \\\n", - " (states_next[seeding_sources][seeding_places] > 0)\n", - " states_next[seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * \\\n", + " (states_next[seeding_sources][seeding_subpops] > 0)\n", + " states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " total_seeded += this_seeding_amounts\n", " times_seeded += 1\n", " # ADD TO cumulative, this is debatable,\n", - " states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " ### Shape\n", "\n", @@ -12766,19 +12766,19 @@ " seeding_data['day_start_idx'][today + 1]\n", " ):\n", " this_seeding_amounts = seeding_amounts[seeding_instance_idx]\n", - " seeding_places = seeding_data['seeding_places'][seeding_instance_idx]\n", + " seeding_subpops = seeding_data['seeding_subpops'][seeding_instance_idx]\n", " seeding_sources = seeding_data['seeding_sources'][seeding_instance_idx]\n", " seeding_destinations = seeding_data['seeding_destinations'][seeding_instance_idx]\n", " # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx]\n", - " states_next[seeding_sources][seeding_places] -= this_seeding_amounts\n", - " states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * \\\n", - " (states_next[seeding_sources][seeding_places] > 0)\n", - " states_next[seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * \\\n", + " (states_next[seeding_sources][seeding_subpops] > 0)\n", + " states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " total_seeded += this_seeding_amounts\n", " times_seeded += 1\n", " # ADD TO cumulative, this is debatable,\n", - " states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " ### Shape\n", "\n", @@ -12948,19 +12948,19 @@ " seeding_data['day_start_idx'][today + 1]\n", " ):\n", " this_seeding_amounts = seeding_amounts[seeding_instance_idx]\n", - " seeding_places = seeding_data['seeding_places'][seeding_instance_idx]\n", + " seeding_subpops = seeding_data['seeding_subpops'][seeding_instance_idx]\n", " seeding_sources = seeding_data['seeding_sources'][seeding_instance_idx]\n", " seeding_destinations = seeding_data['seeding_destinations'][seeding_instance_idx]\n", " # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx]\n", - " states_next[seeding_sources][seeding_places] -= this_seeding_amounts\n", - " states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * \\\n", - " (states_next[seeding_sources][seeding_places] > 0)\n", - " states_next[seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * \\\n", + " (states_next[seeding_sources][seeding_subpops] > 0)\n", + " states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " total_seeded += this_seeding_amounts\n", " times_seeded += 1\n", " # ADD TO cumulative, this is debatable,\n", - " states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " ### Shape\n", "\n", @@ -13000,7 +13000,7 @@ " stochastic_p # 16\n", " ):\n", "\n", - " seeding_places_dict = seeding_data['seeding_places']\n", + " seeding_subpops_dict = seeding_data['seeding_subpops']\n", " seeding_sources_dict = seeding_data['seeding_sources']\n", " seeding_destinations_dict = seeding_data['seeding_destinations']\n", " day_start_idx_dict = seeding_data['day_start_idx']\n", @@ -13125,19 +13125,19 @@ " day_start_idx_dict[today + 1]\n", " ):\n", " this_seeding_amounts = seeding_amounts[seeding_instance_idx]\n", - " seeding_places = seeding_places_dict[seeding_instance_idx]\n", + " seeding_subpops = seeding_subpops_dict[seeding_instance_idx]\n", " seeding_sources = seeding_sources_dict[seeding_instance_idx]\n", " seeding_destinations = seeding_destinations_dict[seeding_instance_idx]\n", " # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx]\n", - " states_next[seeding_sources][seeding_places] -= this_seeding_amounts\n", - " states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * \\\n", - " (states_next[seeding_sources][seeding_places] > 0)\n", - " states_next[seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * \\\n", + " (states_next[seeding_sources][seeding_subpops] > 0)\n", + " states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", "\n", " # ADD TO cumulative, this is debatable,\n", " # WARNING this here.\n", - " x_[1][seeding_destinations][seeding_places] += this_seeding_amounts\n", + " x_[1][seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " ### Shape\n", "\n", @@ -13324,19 +13324,19 @@ " seeding_data['day_start_idx'][today + 1]\n", " ):\n", " this_seeding_amounts = seeding_amounts[seeding_instance_idx]\n", - " seeding_places = seeding_data['seeding_places'][seeding_instance_idx]\n", + " seeding_subpops = seeding_data['seeding_subpops'][seeding_instance_idx]\n", " seeding_sources = seeding_data['seeding_sources'][seeding_instance_idx]\n", " seeding_destinations = seeding_data['seeding_destinations'][seeding_instance_idx]\n", " # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx]\n", - " states_next[seeding_sources][seeding_places] -= this_seeding_amounts\n", - " states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * \\\n", - " (states_next[seeding_sources][seeding_places] > 0)\n", - " states_next[seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * \\\n", + " (states_next[seeding_sources][seeding_subpops] > 0)\n", + " states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " total_seeded += this_seeding_amounts\n", " times_seeded += 1\n", " # ADD TO cumulative, this is debatable,\n", - " states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " ### Shape\n", "\n", @@ -13421,19 +13421,19 @@ " seeding_data['day_start_idx'][today + 1]\n", " ):\n", " this_seeding_amounts = seeding_amounts[seeding_instance_idx]\n", - " seeding_places = seeding_data['seeding_places'][seeding_instance_idx]\n", + " seeding_subpops = seeding_data['seeding_subpops'][seeding_instance_idx]\n", " seeding_sources = seeding_data['seeding_sources'][seeding_instance_idx]\n", " seeding_destinations = seeding_data['seeding_destinations'][seeding_instance_idx]\n", " # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx]\n", - " states_next[seeding_sources][seeding_places] -= this_seeding_amounts\n", - " states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * \\\n", - " (states_next[seeding_sources][seeding_places] > 0)\n", - " states_next[seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * \\\n", + " (states_next[seeding_sources][seeding_subpops] > 0)\n", + " states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " total_seeded += this_seeding_amounts\n", " times_seeded += 1\n", " # ADD TO cumulative, this is debatable,\n", - " states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " ### Shape\n", "\n", @@ -13706,19 +13706,19 @@ " seeding_data['day_start_idx'][today + 1]\n", " ):\n", " this_seeding_amounts = seeding_amounts[seeding_instance_idx]\n", - " seeding_places = seeding_data['seeding_places'][seeding_instance_idx]\n", + " seeding_subpops = seeding_data['seeding_subpops'][seeding_instance_idx]\n", " seeding_sources = seeding_data['seeding_sources'][seeding_instance_idx]\n", " seeding_destinations = seeding_data['seeding_destinations'][seeding_instance_idx]\n", " # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx]\n", - " states_next[seeding_sources][seeding_places] -= this_seeding_amounts\n", - " states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * \\\n", - " (states_next[seeding_sources][seeding_places] > 0)\n", - " states_next[seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * \\\n", + " (states_next[seeding_sources][seeding_subpops] > 0)\n", + " states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " total_seeded += this_seeding_amounts\n", " times_seeded += 1\n", " # ADD TO cumulative, this is debatable,\n", - " states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " ### Shape\n", "\n", @@ -13794,7 +13794,7 @@ " ## Initial Conditions\n", " \"float64[:,:],\" ## initial_conditions [ ncompartments x nspatial_nodes ]\n", " ## Seeding\n", - " \"DictType(unicode_type, int64[:]),\" # seeding keys: 'seeding_places', 'seeding_destinations', 'seeding_sources'\n", + " \"DictType(unicode_type, int64[:]),\" # seeding keys: 'seeding_subpops', 'seeding_destinations', 'seeding_sources'\n", " \"float64[:],\" # seeding_amounts\n", " ## Mobility\n", " \"float64[:],\" # mobility_data [ nmobility_instances ]\n", @@ -13952,19 +13952,19 @@ " seeding_data['day_start_idx'][today + 1]\n", " ):\n", " this_seeding_amounts = seeding_amounts[seeding_instance_idx]\n", - " seeding_places = seeding_data['seeding_places'][seeding_instance_idx]\n", + " seeding_subpops = seeding_data['seeding_subpops'][seeding_instance_idx]\n", " seeding_sources = seeding_data['seeding_sources'][seeding_instance_idx]\n", " seeding_destinations = seeding_data['seeding_destinations'][seeding_instance_idx]\n", " # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx]\n", - " states_next[seeding_sources][seeding_places] -= this_seeding_amounts\n", - " states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * \\\n", - " (states_next[seeding_sources][seeding_places] > 0)\n", - " states_next[seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts\n", + " states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * \\\n", + " (states_next[seeding_sources][seeding_subpops] > 0)\n", + " states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " total_seeded += this_seeding_amounts\n", " times_seeded += 1\n", " # ADD TO cumulative, this is debatable,\n", - " states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts\n", + " states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts\n", "\n", " ### Shape\n", "\n", diff --git a/flepimop/gempyor_pkg/docs/integration_doc.ipynb b/flepimop/gempyor_pkg/docs/integration_doc.ipynb index bf938e5ad..f98873201 100644 --- a/flepimop/gempyor_pkg/docs/integration_doc.ipynb +++ b/flepimop/gempyor_pkg/docs/integration_doc.ipynb @@ -55,7 +55,7 @@ ], "source": [ "config_filepath = \"../tests/npi/config_npi.yml\"\n", - "gempyor_simulator = gempyor.InferenceSimulator(\n", + "gempyor_simulator = gempyor.GempyorSimulator(\n", " config_path=config_filepath,\n", " run_id=\"test_run_id\",\n", " prefix=\"test_prefix/\",\n", @@ -95,7 +95,7 @@ } ], "source": [ - "# copied from InferenceSimulator/one_simulation\n", + "# copied from GempyorSimulator/one_simulation\n", "\n", "sim_id2write = 0\n", "load_ID = False\n", diff --git a/flepimop/gempyor_pkg/docs/interface.ipynb b/flepimop/gempyor_pkg/docs/interface.ipynb index c50d29997..bde9af0ec 100644 --- a/flepimop/gempyor_pkg/docs/interface.ipynb +++ b/flepimop/gempyor_pkg/docs/interface.ipynb @@ -46,7 +46,7 @@ ], "source": [ "config_filepath = \"../tests/npi/config_npi.yml\"\n", - "gempyor_simulator = gempyor.InferenceSimulator(\n", + "gempyor_simulator = gempyor.GempyorSimulator(\n", " config_path=config_filepath,\n", " run_id=\"test_run_id\",\n", " prefix=\"test_prefix/\",\n", diff --git a/flepimop/gempyor_pkg/setup.cfg b/flepimop/gempyor_pkg/setup.cfg index 96edcfd49..3de937cb7 100644 --- a/flepimop/gempyor_pkg/setup.cfg +++ b/flepimop/gempyor_pkg/setup.cfg @@ -39,6 +39,7 @@ install_requires = console_scripts = gempyor-outcomes = gempyor.simulate_outcome:simulate gempyor-seir = gempyor.simulate_seir:simulate + gempyor-simulate = gempyor.simulate:simulate [options.packages.find] where = src diff --git a/flepimop/gempyor_pkg/src/gempyor/NPI/ReduceIntervention.py b/flepimop/gempyor_pkg/src/gempyor/NPI/ModifierModifier.py similarity index 91% rename from flepimop/gempyor_pkg/src/gempyor/NPI/ReduceIntervention.py rename to flepimop/gempyor_pkg/src/gempyor/NPI/ModifierModifier.py index 526f5797d..ab7e5d902 100644 --- a/flepimop/gempyor_pkg/src/gempyor/NPI/ReduceIntervention.py +++ b/flepimop/gempyor_pkg/src/gempyor/NPI/ModifierModifier.py @@ -8,13 +8,13 @@ debug_print = False -class ReduceIntervention(NPIBase): +class ModifierModifier(NPIBase): def __init__( self, *, npi_config, global_config, - geoids, + subpops, loaded_df=None, pnames_overlap_operation_sum=[], ): @@ -23,11 +23,11 @@ def __init__( self.start_date = global_config["start_date"].as_date() self.end_date = global_config["end_date"].as_date() - self.geoids = geoids + self.subpops = subpops self.parameters = pd.DataFrame( 0.0, - index=self.geoids, + index=self.subpops, columns=["npi_name", "start_date", "end_date", "parameter", "reduction"], ) @@ -61,7 +61,7 @@ def __init__( self.sub_npi = NPIBase.execute( npi_config=scenario_npi_config, global_config=global_config, - geoids=geoids, + subpops=subpops, loaded_df=loaded_df, ) new_params = self.sub_npi.param_name # either a list (if stacked) or a string @@ -122,9 +122,9 @@ def __checkErrors(self): if not (self.parameters["start_date"] <= self.parameters["end_date"]).all(): raise ValueError(f"at least one period_start_date is greater than the corresponding period end date") - for n in self.affected_geoids: - if n not in self.geoids: - raise ValueError(f"Invalid config value {n} not in geoids") + for n in self.affected_subpops: + if n not in self.subpops: + raise ValueError(f"Invalid config value {n} not in subpops") # if not ((min_start_date >= self.scenario_start_date)): # raise ValueError(f"{self.name} : at least one period_start_date occurs before the baseline intervention begins") @@ -152,7 +152,7 @@ def getReductionToWrite(self): return pd.concat(self.reduction_params, ignore_index=True) def __createFromDf(self, loaded_df, npi_config): - loaded_df.index = loaded_df.geoid + loaded_df.index = loaded_df.subpop loaded_df = loaded_df[loaded_df["npi_name"] == self.name] self.parameters = loaded_df[["npi_name", "start_date", "end_date", "parameter", "reduction"]].copy() @@ -173,7 +173,7 @@ def __createFromDf(self, loaded_df, npi_config): # else: # self.parameters["start_date"] = self.end_date - self.affected_geoids = set(self.parameters.index) + self.affected_subpops = set(self.parameters.index) # parameter name is picked from config too: (before: ) # self.param_name = self.parameters["parameter"].unique()[0] # [0] to convert ndarray to str # now: @@ -184,14 +184,14 @@ def __createFromConfig(self, npi_config): # Get name of the parameter to reduce self.param_name = npi_config["parameter"].as_str().lower().replace(" ", "") - # Optional config field "affected_geoids" - # If values of "affected_geoids" is "all" or unspecified, run on all geoids. - # Otherwise, run only on geoids specified. - self.affected_geoids = set(self.geoids) - if npi_config["affected_geoids"].exists() and npi_config["affected_geoids"].get() != "all": - self.affected_geoids = {str(n.get()) for n in npi_config["affected_geoids"]} + # Optional config field "subpop" + # If values of "subpop" is "all" or unspecified, run on all subpops. + # Otherwise, run only on subpops specified. + self.affected_subpops = set(self.subpops) + if npi_config["subpop"].exists() and npi_config["subpop"].get() != "all": + self.affected_subpops = {str(n.get()) for n in npi_config["subpop"]} - self.parameters = self.parameters[self.parameters.index.isin(self.affected_geoids)] + self.parameters = self.parameters[self.parameters.index.isin(self.affected_subpops)] # Create reduction self.dist = npi_config["value"].as_random_distribution() @@ -204,6 +204,6 @@ def __createFromConfig(self, npi_config): ) self.parameters["parameter"] = self.param_name - self.spatial_groups = helpers.get_spatial_groups(npi_config, list(self.affected_geoids)) + self.spatial_groups = helpers.get_spatial_groups(npi_config, list(self.affected_subpops)) if self.spatial_groups["grouped"]: - raise ValueError("Spatial groups are not supported for ReduceIntervention interventions") + raise ValueError("Spatial groups are not supported for ModifierModifier interventions") diff --git a/flepimop/gempyor_pkg/src/gempyor/NPI/MultiTimeReduce.py b/flepimop/gempyor_pkg/src/gempyor/NPI/MultiPeriodModifier.py similarity index 67% rename from flepimop/gempyor_pkg/src/gempyor/NPI/MultiTimeReduce.py rename to flepimop/gempyor_pkg/src/gempyor/NPI/MultiPeriodModifier.py index 12953d38c..3438aac1f 100644 --- a/flepimop/gempyor_pkg/src/gempyor/NPI/MultiTimeReduce.py +++ b/flepimop/gempyor_pkg/src/gempyor/NPI/MultiPeriodModifier.py @@ -4,13 +4,13 @@ from .base import NPIBase -class MultiTimeReduce(NPIBase): +class MultiPeriodModifier(NPIBase): def __init__( self, *, npi_config, global_config, - geoids, + subpops, loaded_df=None, pnames_overlap_operation_sum=[], sanitize=False, @@ -27,23 +27,23 @@ def __init__( self.start_date = global_config["start_date"].as_date() self.end_date = global_config["end_date"].as_date() - self.geoids = geoids + self.subpops = subpops self.npi = pd.DataFrame( 0.0, - index=self.geoids, + index=self.subpops, columns=pd.date_range(self.start_date, self.end_date), ) self.parameters = pd.DataFrame( data={ - "npi_name": [""] * len(self.geoids), - "parameter": [""] * len(self.geoids), - "start_date": [[self.start_date]] * len(self.geoids), - "end_date": [[self.end_date]] * len(self.geoids), - "reduction": [0.0] * len(self.geoids), + "npi_name": [""] * len(self.subpops), + "parameter": [""] * len(self.subpops), + "start_date": [[self.start_date]] * len(self.subpops), + "end_date": [[self.end_date]] * len(self.subpops), + "reduction": [0.0] * len(self.subpops), }, - index=self.geoids, + index=self.subpops, ) self.param_name = npi_config["parameter"].as_str().lower() @@ -61,14 +61,14 @@ def __init__( raise ValueError("at least one period start or end date is not between global dates") for grp_config in npi_config["groups"]: - affected_geoids_grp = self.__get_affected_geoids_grp(grp_config) - for sub_index in range(len(self.parameters["start_date"][affected_geoids_grp[0]])): + affected_subpops_grp = self.__get_affected_subpops_grp(grp_config) + for sub_index in range(len(self.parameters["start_date"][affected_subpops_grp[0]])): period_range = pd.date_range( - self.parameters["start_date"][affected_geoids_grp[0]][sub_index], - self.parameters["end_date"][affected_geoids_grp[0]][sub_index], + self.parameters["start_date"][affected_subpops_grp[0]][sub_index], + self.parameters["end_date"][affected_subpops_grp[0]][sub_index], ) - self.npi.loc[affected_geoids_grp, period_range] = np.tile( - self.parameters["reduction"][affected_geoids_grp], + self.npi.loc[affected_subpops_grp, period_range] = np.tile( + self.parameters["reduction"][affected_subpops_grp], (len(period_range), 1), ).T @@ -100,9 +100,9 @@ def __checkErrors(self): if not (self.parameters["start_date"] <= self.parameters["end_date"]).all(): raise ValueError(f"at least one period_start_date is greater than the corresponding period end date") - for n in self.affected_geoids: - if n not in self.geoids: - raise ValueError(f"Invalid config value {n} not in geoids") + for n in self.affected_subpops: + if n not in self.subpops: + raise ValueError(f"Invalid config value {n} not in subpops") ### if self.param_name not in REDUCE_PARAMS: ### raise ValueError(f"Invalid parameter name: {self.param_name}. Must be one of {REDUCE_PARAMS}") @@ -120,16 +120,16 @@ def __createFromConfig(self, npi_config): # Get name of the parameter to reduce self.param_name = npi_config["parameter"].as_str().lower().replace(" ", "") - self.affected_geoids = self.__get_affected_geoids(npi_config) + self.affected_subpops = self.__get_affected_subpops(npi_config) - self.parameters = self.parameters[self.parameters.index.isin(self.affected_geoids)] + self.parameters = self.parameters[self.parameters.index.isin(self.affected_subpops)] dist = npi_config["value"].as_random_distribution() self.parameters["npi_name"] = self.name self.parameters["parameter"] = self.param_name self.spatial_groups = [] for grp_config in npi_config["groups"]: - affected_geoids_grp = self.__get_affected_geoids_grp(grp_config) + affected_subpops_grp = self.__get_affected_subpops_grp(grp_config) # Create reduction start_dates = [] end_dates = [] @@ -140,52 +140,52 @@ def __createFromConfig(self, npi_config): else: start_dates = [self.start_date] end_dates = [self.end_date] - this_spatial_group = helpers.get_spatial_groups(grp_config, affected_geoids_grp) + this_spatial_group = helpers.get_spatial_groups(grp_config, affected_subpops_grp) self.spatial_groups.append(this_spatial_group) # print(self.name, this_spatial_groups) # unfortunately, we cannot use .loc here, because it is not possible to assign a list of list # to a subset of a dataframe... so we iterate. - for geoid in this_spatial_group["ungrouped"]: - self.parameters.at[geoid, "start_date"] = start_dates - self.parameters.at[geoid, "end_date"] = end_dates - self.parameters.at[geoid, "reduction"] = dist(size=1) + for subpop in this_spatial_group["ungrouped"]: + self.parameters.at[subpop, "start_date"] = start_dates + self.parameters.at[subpop, "end_date"] = end_dates + self.parameters.at[subpop, "reduction"] = dist(size=1) for group in this_spatial_group["grouped"]: drawn_value = dist(size=1) - for geoid in group: - self.parameters.at[geoid, "start_date"] = start_dates - self.parameters.at[geoid, "end_date"] = end_dates - self.parameters.at[geoid, "reduction"] = drawn_value - - def __get_affected_geoids_grp(self, grp_config): - if grp_config["affected_geoids"].get() == "all": - affected_geoids_grp = self.geoids + for subpop in group: + self.parameters.at[subpop, "start_date"] = start_dates + self.parameters.at[subpop, "end_date"] = end_dates + self.parameters.at[subpop, "reduction"] = drawn_value + + def __get_affected_subpops_grp(self, grp_config): + if grp_config["subpop"].get() == "all": + affected_subpops_grp = self.subpops else: - affected_geoids_grp = [str(n.get()) for n in grp_config["affected_geoids"]] - return affected_geoids_grp + affected_subpops_grp = [str(n.get()) for n in grp_config["subpop"]] + return affected_subpops_grp def __createFromDf(self, loaded_df, npi_config): - loaded_df.index = loaded_df.geoid + loaded_df.index = loaded_df.subpop loaded_df = loaded_df[loaded_df["npi_name"] == self.name] - self.affected_geoids = self.__get_affected_geoids(npi_config) + self.affected_subpops = self.__get_affected_subpops(npi_config) - self.parameters = self.parameters[self.parameters.index.isin(self.affected_geoids)] + self.parameters = self.parameters[self.parameters.index.isin(self.affected_subpops)] self.parameters["npi_name"] = self.name self.parameters["parameter"] = self.param_name # self.parameters = loaded_df[["npi_name", "start_date", "end_date", "parameter", "reduction"]].copy() # self.parameters["start_date"] = [[datetime.date.fromisoformat(date) for date in strdate.split(",")] for strdate in self.parameters["start_date"]] # self.parameters["end_date"] = [[datetime.date.fromisoformat(date) for date in strdate.split(",")] for strdate in self.parameters["end_date"]] - # self.affected_geoids = set(self.parameters.index) + # self.affected_subpops = set(self.parameters.index) if self.sanitize: - if len(self.affected_geoids) != len(self.parameters): - print(f"loading {self.name} and we got {len(self.parameters)} geoids") - print(f"getting from config that it affects {len(self.affected_geoids)}") + if len(self.affected_subpops) != len(self.parameters): + print(f"loading {self.name} and we got {len(self.parameters)} subpops") + print(f"getting from config that it affects {len(self.affected_subpops)}") self.spatial_groups = [] for grp_config in npi_config["groups"]: - affected_geoids_grp = self.__get_affected_geoids_grp(grp_config) + affected_subpops_grp = self.__get_affected_subpops_grp(grp_config) # Create reduction start_dates = [] end_dates = [] @@ -196,36 +196,36 @@ def __createFromDf(self, loaded_df, npi_config): else: start_dates = [self.start_date] end_dates = [self.end_date] - this_spatial_group = helpers.get_spatial_groups(grp_config, affected_geoids_grp) + this_spatial_group = helpers.get_spatial_groups(grp_config, affected_subpops_grp) self.spatial_groups.append(this_spatial_group) - for geoid in this_spatial_group["ungrouped"]: - if not geoid in loaded_df.index: - self.parameters.at[geoid, "start_date"] = start_dates - self.parameters.at[geoid, "end_date"] = end_dates + for subpop in this_spatial_group["ungrouped"]: + if not subpop in loaded_df.index: + self.parameters.at[subpop, "start_date"] = start_dates + self.parameters.at[subpop, "end_date"] = end_dates dist = npi_config["value"].as_random_distribution() - self.parameters.at[geoid, "reduction"] = dist(size=1) + self.parameters.at[subpop, "reduction"] = dist(size=1) else: - self.parameters.at[geoid, "start_date"] = start_dates - self.parameters.at[geoid, "end_date"] = end_dates - self.parameters.at[geoid, "reduction"] = loaded_df.at[geoid, "reduction"] + self.parameters.at[subpop, "start_date"] = start_dates + self.parameters.at[subpop, "end_date"] = end_dates + self.parameters.at[subpop, "reduction"] = loaded_df.at[subpop, "reduction"] for group in this_spatial_group["grouped"]: if ",".join(group) in loaded_df.index: # ordered, so it's ok - for geoid in group: - self.parameters.at[geoid, "start_date"] = start_dates - self.parameters.at[geoid, "end_date"] = end_dates - self.parameters.at[geoid, "reduction"] = loaded_df.at[",".join(group), "reduction"] + for subpop in group: + self.parameters.at[subpop, "start_date"] = start_dates + self.parameters.at[subpop, "end_date"] = end_dates + self.parameters.at[subpop, "reduction"] = loaded_df.at[",".join(group), "reduction"] else: dist = npi_config["value"].as_random_distribution() drawn_value = dist(size=1) - for geoid in group: - self.parameters.at[geoid, "start_date"] = start_dates - self.parameters.at[geoid, "end_date"] = end_dates - self.parameters.at[geoid, "reduction"] = drawn_value + for subpop in group: + self.parameters.at[subpop, "start_date"] = start_dates + self.parameters.at[subpop, "end_date"] = end_dates + self.parameters.at[subpop, "reduction"] = drawn_value - self.parameters = self.parameters.loc[list(self.affected_geoids)] - # self.parameters = self.parameters[self.parameters.index.isin(self.affected_geoids) ] - # self.parameters = self.parameters[self.affected_geoids] + self.parameters = self.parameters.loc[list(self.affected_subpops)] + # self.parameters = self.parameters[self.parameters.index.isin(self.affected_subpops) ] + # self.parameters = self.parameters[self.affected_subpops] # parameter name is picked from config too: (before: ) # self.param_name = self.parameters["parameter"].unique()[0] # [0] to convert ndarray to str @@ -233,20 +233,20 @@ def __createFromDf(self, loaded_df, npi_config): self.param_name = npi_config["parameter"].as_str().lower().replace(" ", "") self.parameters["parameter"] = self.param_name - def __get_affected_geoids(self, npi_config): - # Optional config field "affected_geoids" - # If values of "affected_geoids" is "all" or unspecified, run on all geoids. - # Otherwise, run only on geoids specified. - affected_geoids_grp = [] + def __get_affected_subpops(self, npi_config): + # Optional config field "subpop" + # If values of "subpop" is "all" or unspecified, run on all subpops. + # Otherwise, run only on subpops specified. + affected_subpops_grp = [] for grp_config in npi_config["groups"]: - if grp_config["affected_geoids"].get() == "all": - affected_geoids_grp = self.geoids + if grp_config["subpop"].get() == "all": + affected_subpops_grp = self.subpops else: - affected_geoids_grp += [str(n.get()) for n in grp_config["affected_geoids"]] - affected_geoids = set(affected_geoids_grp) - if len(affected_geoids) != len(affected_geoids_grp): - raise ValueError(f"In NPI {self.name}, some geoids belong to several groups. This is unsupported.") - return affected_geoids + affected_subpops_grp += [str(n.get()) for n in grp_config["subpop"]] + affected_subpops = set(affected_subpops_grp) + if len(affected_subpops) != len(affected_subpops_grp): + raise ValueError(f"In NPI {self.name}, some subpops belong to several groups. This is unsupported.") + return affected_subpops def getReduction(self, param, default=0.0): "Return the reduction for this param, `default` if no reduction defined" @@ -257,11 +257,11 @@ def getReduction(self, param, default=0.0): def getReductionToWrite(self): df_list = [] - # self.parameters.index is a list of geoids + # self.parameters.index is a list of subpops for this_spatial_groups in self.spatial_groups: # spatially ungrouped dataframe df_ungroup = self.parameters[self.parameters.index.isin(this_spatial_groups["ungrouped"])].copy() - df_ungroup.index.name = "geoid" + df_ungroup.index.name = "subpop" df_ungroup["start_date"] = df_ungroup["start_date"].apply( lambda l: ",".join([d.strftime("%Y-%m-%d") for d in l]) ) @@ -272,12 +272,12 @@ def getReductionToWrite(self): # spatially grouped dataframe. They are nested within multitime reduce groups, # so we can set the same dates for allof them for group in this_spatial_groups["grouped"]: - # we use the first geoid to represent the group + # we use the first subpop to represent the group df_group = self.parameters[self.parameters.index == group[0]].copy() row_group = pd.DataFrame.from_dict( { - "geoid": ",".join(group), + "subpop": ",".join(group), "npi_name": df_group["npi_name"], "parameter": df_group["parameter"], "start_date": df_group["start_date"].apply( @@ -286,7 +286,7 @@ def getReductionToWrite(self): "end_date": df_group["end_date"].apply(lambda l: ",".join([d.strftime("%Y-%m-%d") for d in l])), "reduction": df_group["reduction"], } - ).set_index("geoid") + ).set_index("subpop") df_list.append(row_group) df = pd.concat(df_list) diff --git a/flepimop/gempyor_pkg/src/gempyor/NPI/ReduceR0.py b/flepimop/gempyor_pkg/src/gempyor/NPI/ReduceR0.py deleted file mode 100644 index d24b255dd..000000000 --- a/flepimop/gempyor_pkg/src/gempyor/NPI/ReduceR0.py +++ /dev/null @@ -1,17 +0,0 @@ -import pandas as pd -import numpy as np - -from .base import NPIBase -from .Reduce import Reduce - - -class ReduceR0(Reduce): - def __init__(self, *, npi_config, global_config, geoids, loaded_df=None, pnames_overlap_operation_sum=[]): - npi_config["parameter"] = "r0" - super().__init__( - npi_config=npi_config, - global_config=global_config, - geoids=geoids, - loaded_df=loaded_df, - pnames_overlap_operation_sum=pnames_overlap_operation_sum, - ) diff --git a/flepimop/gempyor_pkg/src/gempyor/NPI/Reduce.py b/flepimop/gempyor_pkg/src/gempyor/NPI/SinglePeriodModifier.py similarity index 87% rename from flepimop/gempyor_pkg/src/gempyor/NPI/Reduce.py rename to flepimop/gempyor_pkg/src/gempyor/NPI/SinglePeriodModifier.py index 9c38f6eac..54232c61f 100644 --- a/flepimop/gempyor_pkg/src/gempyor/NPI/Reduce.py +++ b/flepimop/gempyor_pkg/src/gempyor/NPI/SinglePeriodModifier.py @@ -5,13 +5,13 @@ from .base import NPIBase -class Reduce(NPIBase): +class SinglePeriodModifier(NPIBase): def __init__( self, *, npi_config, global_config, - geoids, + subpops, loaded_df=None, pnames_overlap_operation_sum=[], ): @@ -26,16 +26,16 @@ def __init__( self.start_date = global_config["start_date"].as_date() self.end_date = global_config["end_date"].as_date() - self.geoids = geoids + self.subpops = subpops self.npi = pd.DataFrame( 0.0, - index=self.geoids, + index=self.subpops, columns=pd.date_range(self.start_date, self.end_date), ) self.parameters = pd.DataFrame( 0.0, - index=self.geoids, + index=self.subpops, columns=["npi_name", "start_date", "end_date", "parameter", "reduction"], ) @@ -77,9 +77,9 @@ def __checkErrors(self): if not (self.parameters["start_date"] <= self.parameters["end_date"]).all(): raise ValueError(f"at least one period_start_date is greater than the corresponding period end date") - for n in self.affected_geoids: - if n not in self.geoids: - raise ValueError(f"Invalid config value {n} not in geoids") + for n in self.affected_subpops: + if n not in self.subpops: + raise ValueError(f"Invalid config value {n} not in subpops") ### if self.param_name not in REDUCE_PARAMS: ### raise ValueError(f"Invalid parameter name: {self.param_name}. Must be one of {REDUCE_PARAMS}") @@ -97,14 +97,14 @@ def __createFromConfig(self, npi_config): # Get name of the parameter to reduce self.param_name = npi_config["parameter"].as_str().lower().replace(" ", "") - # Optional config field "affected_geoids" - # If values of "affected_geoids" is "all" or unspecified, run on all geoids. - # Otherwise, run only on geoids specified. - self.affected_geoids = set(self.geoids) - if npi_config["affected_geoids"].exists() and npi_config["affected_geoids"].get() != "all": - self.affected_geoids = {str(n.get()) for n in npi_config["affected_geoids"]} + # Optional config field "subpop" + # If values of "subpop" is "all" or unspecified, run on all subpops. + # Otherwise, run only on subpops specified. + self.affected_subpops = set(self.subpops) + if npi_config["subpop"].exists() and npi_config["subpop"].get() != "all": + self.affected_subpops = {str(n.get()) for n in npi_config["subpop"]} - self.parameters = self.parameters[self.parameters.index.isin(self.affected_geoids)] + self.parameters = self.parameters[self.parameters.index.isin(self.affected_subpops)] # Create reduction self.dist = npi_config["value"].as_random_distribution() @@ -116,7 +116,7 @@ def __createFromConfig(self, npi_config): npi_config["period_end_date"].as_date() if npi_config["period_end_date"].exists() else self.end_date ) self.parameters["parameter"] = self.param_name - self.spatial_groups = helpers.get_spatial_groups(npi_config, list(self.affected_geoids)) + self.spatial_groups = helpers.get_spatial_groups(npi_config, list(self.affected_subpops)) if self.spatial_groups["ungrouped"]: self.parameters.loc[self.spatial_groups["ungrouped"], "reduction"] = self.dist( size=len(self.spatial_groups["ungrouped"]) @@ -127,15 +127,15 @@ def __createFromConfig(self, npi_config): self.parameters.loc[group, "reduction"] = drawn_value def __createFromDf(self, loaded_df, npi_config): - loaded_df.index = loaded_df.geoid + loaded_df.index = loaded_df.subpop loaded_df = loaded_df[loaded_df["npi_name"] == self.name] - self.affected_geoids = set(self.geoids) - if npi_config["affected_geoids"].exists() and npi_config["affected_geoids"].get() != "all": - self.affected_geoids = {str(n.get()) for n in npi_config["affected_geoids"]} + self.affected_subpops = set(self.subpops) + if npi_config["subpop"].exists() and npi_config["subpop"].get() != "all": + self.affected_subpops = {str(n.get()) for n in npi_config["subpop"]} self.param_name = npi_config["parameter"].as_str().lower().replace(" ", "") - self.parameters = self.parameters[self.parameters.index.isin(self.affected_geoids)] + self.parameters = self.parameters[self.parameters.index.isin(self.affected_subpops)] self.parameters["npi_name"] = self.name self.parameters["parameter"] = self.param_name @@ -161,10 +161,10 @@ def __createFromDf(self, loaded_df, npi_config): # self.param_name = self.parameters["parameter"].unique()[0] # [0] to convert ndarray to str # now: - # TODO: to be consistent with MTR, we want to also draw the values for the geoids + # TODO: to be consistent with MTR, we want to also draw the values for the subpops # that are not in the loaded_df. - self.spatial_groups = helpers.get_spatial_groups(npi_config, list(self.affected_geoids)) + self.spatial_groups = helpers.get_spatial_groups(npi_config, list(self.affected_subpops)) if self.spatial_groups["ungrouped"]: self.parameters.loc[self.spatial_groups["ungrouped"], "reduction"] = loaded_df.loc[ self.spatial_groups["ungrouped"], "reduction" @@ -182,25 +182,25 @@ def getReduction(self, param, default=0.0): def getReductionToWrite(self): # spatially ungrouped dataframe df = self.parameters[self.parameters.index.isin(self.spatial_groups["ungrouped"])].copy() - df.index.name = "geoid" + df.index.name = "subpop" df["start_date"] = df["start_date"].astype("str") df["end_date"] = df["end_date"].astype("str") # spatially grouped dataframe for group in self.spatial_groups["grouped"]: - # we use the first geoid to represent the group + # we use the first subpop to represent the group df_group = self.parameters[self.parameters.index == group[0]].copy() row_group = pd.DataFrame.from_dict( { - "geoid": ",".join(group), + "subpop": ",".join(group), "npi_name": df_group["npi_name"], "parameter": df_group["parameter"], "start_date": df_group["start_date"].astype("str"), "end_date": df_group["end_date"].astype("str"), "reduction": df_group["reduction"], } - ).set_index("geoid") + ).set_index("subpop") df = pd.concat([df, row_group]) df = df.reset_index() diff --git a/flepimop/gempyor_pkg/src/gempyor/NPI/Stacked.py b/flepimop/gempyor_pkg/src/gempyor/NPI/StackedModifier.py similarity index 94% rename from flepimop/gempyor_pkg/src/gempyor/NPI/Stacked.py rename to flepimop/gempyor_pkg/src/gempyor/NPI/StackedModifier.py index 7181f8d66..def228f2b 100644 --- a/flepimop/gempyor_pkg/src/gempyor/NPI/Stacked.py +++ b/flepimop/gempyor_pkg/src/gempyor/NPI/StackedModifier.py @@ -14,13 +14,13 @@ REDUCTION_METADATA_CAP = int(os.getenv("FLEPI_MAX_STACK_SIZE", 50000)) -class Stacked(NPIBase): +class StackedModifier(NPIBase): def __init__( self, *, npi_config, global_config, - geoids, + subpops, loaded_df=None, pnames_overlap_operation_sum=[], ): @@ -29,7 +29,7 @@ def __init__( self.start_date = global_config["start_date"].as_date() self.end_date = global_config["end_date"].as_date() - self.geoids = geoids + self.subpops = subpops self.param_name = [] self.reductions = {} # {param: 1 for param in REDUCE_PARAMS} self.reduction_params = collections.deque() @@ -59,7 +59,7 @@ def __init__( sub_npi = NPIBase.execute( npi_config=scenario_npi_config, global_config=global_config, - geoids=geoids, + subpops=subpops, loaded_df=loaded_df, ) @@ -103,7 +103,7 @@ def __init__( # check that no NPI is called several times, and retourn them if len(sub_npis_unique_names) != len(set(sub_npis_unique_names)): raise ValueError( - f"Stacked NPI {self.name} calls a NPI, which calls another NPI. The NPI that is called multiple time is/are: {set([x for x in sub_npis_unique_names if sub_npis_unique_names.count(x) > 1])}" + f"StackedModifier NPI {self.name} calls a NPI, which calls another NPI. The NPI that is called multiple time is/are: {set([x for x in sub_npis_unique_names if sub_npis_unique_names.count(x) > 1])}" ) self.__checkErrors() diff --git a/flepimop/gempyor_pkg/src/gempyor/NPI/base.py b/flepimop/gempyor_pkg/src/gempyor/NPI/base.py index b5f739ce9..1e375d3e6 100644 --- a/flepimop/gempyor_pkg/src/gempyor/NPI/base.py +++ b/flepimop/gempyor_pkg/src/gempyor/NPI/base.py @@ -16,7 +16,7 @@ def __init__(self, *, name): def getReduction(self, param, default=None): pass - # Returns dataframe with columns: , time, parameter, name. Index is sequential. + # Returns dataframe with columns: , time, parameter, name. Index is sequential. @abc.abstractmethod def getReductionToWrite(self): pass @@ -28,7 +28,7 @@ def execute( *, npi_config, global_config, - geoids, + subpops, loaded_df=None, pnames_overlap_operation_sum=[], ): @@ -37,7 +37,7 @@ def execute( return npi_class( npi_config=npi_config, global_config=global_config, - geoids=geoids, + subpops=subpops, loaded_df=loaded_df, pnames_overlap_operation_sum=pnames_overlap_operation_sum, ) diff --git a/flepimop/gempyor_pkg/src/gempyor/NPI/helpers.py b/flepimop/gempyor_pkg/src/gempyor/NPI/helpers.py index bd9f53082..297b33977 100644 --- a/flepimop/gempyor_pkg/src/gempyor/NPI/helpers.py +++ b/flepimop/gempyor_pkg/src/gempyor/NPI/helpers.py @@ -21,12 +21,12 @@ def reduce_parameter( raise ValueError(f"Unknown method to do NPI reduction, got {method}") -def get_spatial_groups(grp_config, affected_geoids: list) -> dict: +def get_spatial_groups(grp_config, affected_subpops: list) -> dict: """ Spatial groups are defined in the config file as a list (of lists). They have the same value. - grouped is a list of lists of geoids - ungrouped is a list of geoids + grouped is a list of lists of subpops + ungrouped is a list of subpops the list are ordered, and this is important so we can get back and forth from the written to disk part that is comma separated """ @@ -34,28 +34,28 @@ def get_spatial_groups(grp_config, affected_geoids: list) -> dict: spatial_groups = {"grouped": [], "ungrouped": []} if not grp_config["spatial_groups"].exists(): - spatial_groups["ungrouped"] = affected_geoids + spatial_groups["ungrouped"] = affected_subpops else: if grp_config["spatial_groups"].get() == "all": - spatial_groups["grouped"] = [affected_geoids] + spatial_groups["grouped"] = [affected_subpops] else: spatial_groups["grouped"] = grp_config["spatial_groups"].get() spatial_groups["ungrouped"] = list( - set(affected_geoids) - set(flatten_list_of_lists(spatial_groups["grouped"])) + set(affected_subpops) - set(flatten_list_of_lists(spatial_groups["grouped"])) ) - # flatten the list of lists of grouped geoids, so we can do some checks + # flatten the list of lists of grouped subpops, so we can do some checks flat_grouped_list = flatten_list_of_lists(spatial_groups["grouped"]) - # check that all geoids are either grouped or ungrouped - if set(flat_grouped_list + spatial_groups["ungrouped"]) != set(affected_geoids): - print("set of grouped and ungrouped geoids", set(flat_grouped_list + spatial_groups["ungrouped"])) - print("set of affected geoids ", set(affected_geoids)) + # check that all subpops are either grouped or ungrouped + if set(flat_grouped_list + spatial_groups["ungrouped"]) != set(affected_subpops): + print("set of grouped and ungrouped subpops", set(flat_grouped_list + spatial_groups["ungrouped"])) + print("set of affected subpops ", set(affected_subpops)) raise ValueError(f"The two above sets are differs for for intervention with config \n {grp_config}") if len(set(flat_grouped_list + spatial_groups["ungrouped"])) != len( flat_grouped_list + spatial_groups["ungrouped"] ): raise ValueError( - f"spatial_group error. for intervention with config \n {grp_config} \n duplicate entries in the set of grouped and ungrouped geoids" + f"spatial_group error. for intervention with config \n {grp_config} \n duplicate entries in the set of grouped and ungrouped subpops" ) spatial_groups["grouped"] = make_list_of_list(spatial_groups["grouped"]) diff --git a/flepimop/gempyor_pkg/src/gempyor/compartments.py b/flepimop/gempyor_pkg/src/gempyor/compartments.py index 5a5c032d1..bb568a436 100644 --- a/flepimop/gempyor_pkg/src/gempyor/compartments.py +++ b/flepimop/gempyor_pkg/src/gempyor/compartments.py @@ -65,7 +65,6 @@ def access_original_config_by_multi_index(self, config_piece, index, dimension=N if dimension is None: dimension = [None for i in index] tmp = [y for y in zip(index, range(len(index)), dimension)] - tmp = zip(index, range(len(index)), dimension) tmp = [list_access_element(config_piece[x[1]], x[0], x[2], encapsulate_as_list) for x in tmp] return tmp @@ -304,7 +303,7 @@ def constructFromConfig(self, seir_config, compartment_config): def get_transition_array(self): with Timer("SEIR.compartments"): - transition_array = np.zeros((self.transitions.shape[1], self.transitions.shape[0]), dtype="int") + transition_array = np.zeros((self.transitions.shape[1], self.transitions.shape[0]), dtype="int64") for cit, colname in enumerate(("source", "destination")): for it, elem in enumerate(self.transitions[colname]): elem = reduce(lambda a, b: a + "_" + b, elem) diff --git a/flepimop/gempyor_pkg/src/gempyor/data/usa-geoid-params-output.parquet b/flepimop/gempyor_pkg/src/gempyor/data/usa-geoid-params-output.parquet index ccadc92e18e4ccb80e6931ab74dac039ac8814d0..d08a4fc58f7c327f2d2e9a89d9e52a250bea8d2a 100644 GIT binary patch delta 1204 zcmb_cPfXKL7=JBLU|Dnr-m;RaXta?CW1GVaPktS25C^P{bD5F|bjyUmItSz5g^Mx9 zn>1z~G|>wuE{YMa7(t0w4jwsplamKShzGw{${?Y7@}+(6d#~U3`~ALOo36jEd-<%c zDG6GT*3#{b8oIZ}CFr{lC2yKe`#I0*MgNcUZVwSepDC^7q!S2&M3pA6sK|Hw%4T0 zTy7XE=wsQwXv!y8Q7>_1DNrpzUC6SRj37%;jWjN8+AI}!oD+p78@&ADllfHjIqMA2 zaDwuP@Lp|2gdH{3xUyvpWP@c0WafB0+;YQYPR5_Xmay1OhS=x&yh)g^6fi}S1v zN!-2G#$OeeL|xc$Wd)_wKmXYvKl;j7h9$18!wE~A=p^W@em^%kLS2NqFY$*)SNt(< z_=NR~CsE}8?Xp}ol)d+Qqdct}llFV7oQ-q!$1Ii{^!&|Dr`&%S=FqD5yLpOzJ#vHh z?UJjB5K;2ELDL4)*AG1Oy{hS8V*wNZQ1NEtCcrExd58hzA)2?&6in!6Q52w$t++p2 zahC#Egl-F9f<28}Rbdt~WQg5DmIwwt-92ot%(B@ESzrNx63V3Kf_GGiGh!5gt2Ls~ zSFMjZ*XNNRl(}p=R?fyJ0y#^H5@yX!C2?YWYRWo2Ks{{M))MauX5#}hqv;eFEWc|U zCrhwRXftz%pVf(ntP$^9l0r>z*R{Wt{YVe&Fe-$9L_e=pQfc_YwY3aH=7< z6=~xh)5ODkr@>r4dlfLzLmtXPt*56xBOyB*+J4?=llD>^-))U-!_l7re>4tax zgXK`bN9rV7Bm;0hR6aK4155wLr&-T)>stAR=^t-y8CqFS$baiQZ{*i+H$DL;4pxjz z6iF}G{$4%=SkJJ`^qpH{ll9Z=^Zv~rypdnNU%$vw&)VdE+w_z7HvL8No<#bt{lwnB z?eSf|GV57AH2v4*z$_WSBwx>YLi;ZN`DEnM9q-1~SxmT65Hoh z^8rzAi|>P5Q`@|8G_kvkE`=srI}2&IR#)bYb{B}9537pM<+~$HvNPhl-Sz&xAq z($=vw7Nw3xV`1oktu}HgsVJz!7l_~tV%a6`xN{eSAMbw^KchwlAq$m}^PEH|f#?Qe z5Ne=w!xqwL;MNbY5RAt9<#O8D!`t>^>+=YVXoTPwJ6Tx7Kd?urn1J*2Yk5sI>)A@8 z8ZY$)D_1cDeNkCbXra0?Xyi2zVwsxi2pX%(*~+iKxzU=6^O034QO(~nfZQI^EwsS?!KCz(*#JJiQ6nsnagJLTaFSd|^n>DXn+gBl8uNKbnT*|ba zv@+&mIx)=aF}S!j_|oGZScLT{_%7fV((o&5-}_^DY2R~Z0j3MyW)@%wfY2TeFgGR- z%o$MC5N$bw2G0r@JP3``2&;4k4=GsCG;2G+$PNHrAUcNicmko^q$lte6}Jg6wo@2L z@JtAtwDmbGw1llrFE7*{4!DrVdSDKK<9iLfz$;{;fhQ+8w1LVKmKi3CQS7w12*9`m z5hhf1vXgbD6F%!aCr%JSnCRWJrfeJ$JDs`lSf8V!$tZPXrG%DOa!YT9-kv20_lNi_ JKoIy}=)aWSOWFVc diff --git a/flepimop/gempyor_pkg/src/gempyor/dev/dev_seir.py b/flepimop/gempyor_pkg/src/gempyor/dev/dev_seir.py index 0268e0b09..3781497bb 100644 --- a/flepimop/gempyor_pkg/src/gempyor/dev/dev_seir.py +++ b/flepimop/gempyor_pkg/src/gempyor/dev/dev_seir.py @@ -20,12 +20,12 @@ config.read(user=False) config.set_file(f"{DATA_DIR}/config.yml") -ss = setup.SpatialSetup( +ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) first_sim_index = 1 @@ -54,11 +54,11 @@ seeding_data = s.seedingAndIC.draw_seeding(sim_id=100, setup=s) initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) -mobility_geoid_indices = s.mobility.indices +mobility_subpop_indices = s.mobility.indices mobility_data_indices = s.mobility.indptr mobility_data = s.mobility.data -npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) +npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) params = s.parameters.parameters_quick_draw(s.n_days, s.nnodes) params = s.parameters.parameters_reduce(params, npi) @@ -84,7 +84,7 @@ initial_conditions, seeding_data, mobility_data, - mobility_geoid_indices, + mobility_subpop_indices, mobility_data_indices, s.popnodes, True, diff --git a/flepimop/gempyor_pkg/src/gempyor/dev/steps.py b/flepimop/gempyor_pkg/src/gempyor/dev/steps.py index 8cc22b2f9..002529df5 100644 --- a/flepimop/gempyor_pkg/src/gempyor/dev/steps.py +++ b/flepimop/gempyor_pkg/src/gempyor/dev/steps.py @@ -167,20 +167,20 @@ def rhs(t, x, today): seeding_data["day_start_idx"][today + 1], ): this_seeding_amounts = seeding_amounts[seeding_instance_idx] - seeding_places = seeding_data["seeding_places"][seeding_instance_idx] + seeding_subpops = seeding_data["seeding_subpops"][seeding_instance_idx] seeding_sources = seeding_data["seeding_sources"][seeding_instance_idx] seeding_destinations = seeding_data["seeding_destinations"][seeding_instance_idx] # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx] - states_next[seeding_sources][seeding_places] -= this_seeding_amounts - states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * ( - states_next[seeding_sources][seeding_places] > 0 + states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts + states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * ( + states_next[seeding_sources][seeding_subpops] > 0 ) - states_next[seeding_destinations][seeding_places] += this_seeding_amounts + states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts total_seeded += this_seeding_amounts times_seeded += 1 # ADD TO cumulative, this is debatable, - states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts + states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts ### Shape @@ -380,20 +380,20 @@ def rk4_integrate(t, x, today): seeding_data["day_start_idx"][today + 1], ): this_seeding_amounts = seeding_amounts[seeding_instance_idx] - seeding_places = seeding_data["seeding_places"][seeding_instance_idx] + seeding_subpops = seeding_data["seeding_subpops"][seeding_instance_idx] seeding_sources = seeding_data["seeding_sources"][seeding_instance_idx] seeding_destinations = seeding_data["seeding_destinations"][seeding_instance_idx] # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx] - states_next[seeding_sources][seeding_places] -= this_seeding_amounts - states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * ( - states_next[seeding_sources][seeding_places] > 0 + states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts + states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * ( + states_next[seeding_sources][seeding_subpops] > 0 ) - states_next[seeding_destinations][seeding_places] += this_seeding_amounts + states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts total_seeded += this_seeding_amounts times_seeded += 1 # ADD TO cumulative, this is debatable, - states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts + states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts ### Shape @@ -573,20 +573,20 @@ def rk4_integrate(t, x, today): seeding_data["day_start_idx"][today + 1], ): this_seeding_amounts = seeding_amounts[seeding_instance_idx] - seeding_places = seeding_data["seeding_places"][seeding_instance_idx] + seeding_subpops = seeding_data["seeding_subpops"][seeding_instance_idx] seeding_sources = seeding_data["seeding_sources"][seeding_instance_idx] seeding_destinations = seeding_data["seeding_destinations"][seeding_instance_idx] # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx] - states_next[seeding_sources][seeding_places] -= this_seeding_amounts - states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * ( - states_next[seeding_sources][seeding_places] > 0 + states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts + states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * ( + states_next[seeding_sources][seeding_subpops] > 0 ) - states_next[seeding_destinations][seeding_places] += this_seeding_amounts + states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts total_seeded += this_seeding_amounts times_seeded += 1 # ADD TO cumulative, this is debatable, - states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts + states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts ### Shape @@ -628,7 +628,7 @@ def rk4_integration3( stochastic_p, # 16 ): - seeding_places_dict = seeding_data["seeding_places"] + seeding_subpops_dict = seeding_data["seeding_subpops"] seeding_sources_dict = seeding_data["seeding_sources"] seeding_destinations_dict = seeding_data["seeding_destinations"] day_start_idx_dict = seeding_data["day_start_idx"] @@ -759,19 +759,19 @@ def day_wrapper_rk4(today, states_next): x_ = np.zeros((2, ncompartments, nspatial_nodes)) for seeding_instance_idx in range(day_start_idx_dict[today], day_start_idx_dict[today + 1]): this_seeding_amounts = seeding_amounts[seeding_instance_idx] - seeding_places = seeding_places_dict[seeding_instance_idx] + seeding_subpops = seeding_subpops_dict[seeding_instance_idx] seeding_sources = seeding_sources_dict[seeding_instance_idx] seeding_destinations = seeding_destinations_dict[seeding_instance_idx] # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx] - states_next[seeding_sources][seeding_places] -= this_seeding_amounts - states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * ( - states_next[seeding_sources][seeding_places] > 0 + states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts + states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * ( + states_next[seeding_sources][seeding_subpops] > 0 ) - states_next[seeding_destinations][seeding_places] += this_seeding_amounts + states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts # ADD TO cumulative, this is debatable, # WARNING this here. - x_[1][seeding_destinations][seeding_places] += this_seeding_amounts + x_[1][seeding_destinations][seeding_subpops] += this_seeding_amounts ### Shape @@ -966,20 +966,20 @@ def rk4_integrate(t, x, today): seeding_data["day_start_idx"][today + 1], ): this_seeding_amounts = seeding_amounts[seeding_instance_idx] - seeding_places = seeding_data["seeding_places"][seeding_instance_idx] + seeding_subpops = seeding_data["seeding_subpops"][seeding_instance_idx] seeding_sources = seeding_data["seeding_sources"][seeding_instance_idx] seeding_destinations = seeding_data["seeding_destinations"][seeding_instance_idx] # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx] - states_next[seeding_sources][seeding_places] -= this_seeding_amounts - states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * ( - states_next[seeding_sources][seeding_places] > 0 + states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts + states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * ( + states_next[seeding_sources][seeding_subpops] > 0 ) - states_next[seeding_destinations][seeding_places] += this_seeding_amounts + states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts total_seeded += this_seeding_amounts times_seeded += 1 # ADD TO cumulative, this is debatable, - states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts + states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts ### Shape @@ -1062,20 +1062,20 @@ def rk4_integration5( seeding_data["day_start_idx"][today + 1], ): this_seeding_amounts = seeding_amounts[seeding_instance_idx] - seeding_places = seeding_data["seeding_places"][seeding_instance_idx] + seeding_subpops = seeding_data["seeding_subpops"][seeding_instance_idx] seeding_sources = seeding_data["seeding_sources"][seeding_instance_idx] seeding_destinations = seeding_data["seeding_destinations"][seeding_instance_idx] # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx] - states_next[seeding_sources][seeding_places] -= this_seeding_amounts - states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * ( - states_next[seeding_sources][seeding_places] > 0 + states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts + states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * ( + states_next[seeding_sources][seeding_subpops] > 0 ) - states_next[seeding_destinations][seeding_places] += this_seeding_amounts + states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts total_seeded += this_seeding_amounts times_seeded += 1 # ADD TO cumulative, this is debatable, - states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts + states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts ### Shape @@ -1379,20 +1379,20 @@ def rk4_integrate(today, x): seeding_data["day_start_idx"][today + 1], ): this_seeding_amounts = seeding_amounts[seeding_instance_idx] - seeding_places = seeding_data["seeding_places"][seeding_instance_idx] + seeding_subpops = seeding_data["seeding_subpops"][seeding_instance_idx] seeding_sources = seeding_data["seeding_sources"][seeding_instance_idx] seeding_destinations = seeding_data["seeding_destinations"][seeding_instance_idx] # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx] - states_next[seeding_sources][seeding_places] -= this_seeding_amounts - states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * ( - states_next[seeding_sources][seeding_places] > 0 + states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts + states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * ( + states_next[seeding_sources][seeding_subpops] > 0 ) - states_next[seeding_destinations][seeding_places] += this_seeding_amounts + states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts total_seeded += this_seeding_amounts times_seeded += 1 # ADD TO cumulative, this is debatable, - states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts + states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts ### Shape @@ -1468,7 +1468,7 @@ def rk4_integrate(today, x): ## Initial Conditions "float64[:,:]," ## initial_conditions [ ncompartments x nspatial_nodes ] ## Seeding - "DictType(unicode_type, int64[:])," # seeding keys: 'seeding_places', 'seeding_destinations', 'seeding_sources' + "DictType(unicode_type, int64[:])," # seeding keys: 'seeding_subpops', 'seeding_destinations', 'seeding_sources' "float64[:]," # seeding_amounts ## Mobility "float64[:]," # mobility_data [ nmobility_instances ] @@ -1635,20 +1635,20 @@ def rk4_integrate(t, x, today): seeding_data["day_start_idx"][today + 1], ): this_seeding_amounts = seeding_amounts[seeding_instance_idx] - seeding_places = seeding_data["seeding_places"][seeding_instance_idx] + seeding_subpops = seeding_data["seeding_subpops"][seeding_instance_idx] seeding_sources = seeding_data["seeding_sources"][seeding_instance_idx] seeding_destinations = seeding_data["seeding_destinations"][seeding_instance_idx] # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx] - states_next[seeding_sources][seeding_places] -= this_seeding_amounts - states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * ( - states_next[seeding_sources][seeding_places] > 0 + states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts + states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * ( + states_next[seeding_sources][seeding_subpops] > 0 ) - states_next[seeding_destinations][seeding_places] += this_seeding_amounts + states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts total_seeded += this_seeding_amounts times_seeded += 1 # ADD TO cumulative, this is debatable, - states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts + states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts ### Shape diff --git a/flepimop/gempyor_pkg/src/gempyor/interface.py b/flepimop/gempyor_pkg/src/gempyor/interface.py index 64df26077..181044b69 100644 --- a/flepimop/gempyor_pkg/src/gempyor/interface.py +++ b/flepimop/gempyor_pkg/src/gempyor/interface.py @@ -10,7 +10,7 @@ import pathlib -from . import seir, setup, file_paths +from . import seir, setup, file_paths, subpopulation_structure from . import outcomes from .utils import config, Timer, read_df, profile import numpy as np @@ -38,7 +38,7 @@ # logger.addHandler(handler) -class InferenceSimulator: +class GempyorSimulator: def __init__( self, config_path, @@ -80,14 +80,14 @@ def __init__( write_parquet = True self.s = setup.Setup( setup_name=config["name"].get() + "_" + str(npi_scenario), - spatial_setup=setup.SpatialSetup( + spatial_setup=subpopulation_structure.SubpopulationStructure( setup_name=config["setup_name"].get(), geodata_file=spatial_base_path / spatial_config["geodata"].get(), mobility_file=spatial_base_path / spatial_config["mobility"].get() if spatial_config["mobility"].exists() else None, - popnodes_key=spatial_config["popnodes"].get(), - nodenames_key=spatial_config["nodenames"].get(), + popnodes_key="population", + subpop_names_key="subpop", ), nslots=nslots, npi_scenario=npi_scenario, @@ -118,7 +118,7 @@ def __init__( f""" gempyor >> prefix: {in_prefix};""" # ti: {s.ti}; tf: {s.tf}; ) - self.already_built = False # whether we have already build the costly object we just build once. + self.already_built = False # whether we have already build the costly objects that need just one build def update_prefix(self, new_prefix, new_out_prefix=None): self.s.in_prefix = new_prefix @@ -156,7 +156,7 @@ def one_simulation_legacy(self, sim_id2write: int, load_ID: bool = False, sim_id sim_id2load=sim_id2load, ) return 0 - + def build_structure(self): ( self.unique_strings, @@ -165,7 +165,6 @@ def build_structure(self): self.proportion_info, ) = self.s.compartments.get_transition_array() self.already_built = True - # @profile() def one_simulation( @@ -228,7 +227,7 @@ def one_simulation( ### Run every time: with Timer("SEIR.parameters"): # Draw or load parameters - + p_draw = self.get_seir_parameters(load_ID=load_ID, sim_id2load=sim_id2load) # reduce them parameters = self.s.parameters.parameters_reduce(p_draw, npi_seir) @@ -247,8 +246,9 @@ def one_simulation( else: initial_conditions = self.s.seedingAndIC.draw_ic(sim_id2write, setup=self.s) seeding_data, seeding_amounts = self.s.seedingAndIC.draw_seeding(sim_id2write, setup=self.s) - self.debug_seeding_date = seeding_data + self.debug_seeding_data = seeding_data self.debug_seeding_amounts = seeding_amounts + self.debug_initial_conditions = initial_conditions with Timer("SEIR.compute"): states = seir.steps_SEIR( @@ -374,13 +374,13 @@ def get_seir_parameter_reduced( parameters = self.s.parameters.parameters_reduce(p_draw, npi_seir) full_df = pd.DataFrame() - for i, geoid in enumerate(self.s.spatset.nodenames): + for i, subpop in enumerate(self.s.spatset.subpop_names): a = pd.DataFrame( parameters[:, :, i].T, columns=self.s.parameters.pnames, index=pd.date_range(self.s.ti, self.s.tf, freq="D"), ) - a["geoid"] = geoid + a["subpop"] = subpop full_df = pd.concat([full_df, a]) # for R, duplicate names are not allowed in index: @@ -389,29 +389,33 @@ def get_seir_parameter_reduced( return full_df - # TODO these function should support bypass - def get_parsed_parameters_seir(self, load_ID=False, + # TODO these function should support bypass + def get_parsed_parameters_seir( + self, + load_ID=False, sim_id2load=None, - #bypass_DF=None, - #bypass_FN=None, + # bypass_DF=None, + # bypass_FN=None, ): if not self.already_built: self.build_structure() - + npi_seir = seir.build_npi_SEIR(s=self.s, load_ID=load_ID, sim_id2load=sim_id2load, config=config) p_draw = self.get_seir_parameters(load_ID=load_ID, sim_id2load=sim_id2load) parameters = self.s.parameters.parameters_reduce(p_draw, npi_seir) parsed_parameters = self.s.compartments.parse_parameters( - parameters, self.s.parameters.pnames, self.unique_strings - ) + parameters, self.s.parameters.pnames, self.unique_strings + ) return parsed_parameters - - def get_reduced_parameters_seir(self, load_ID=False, + + def get_reduced_parameters_seir( + self, + load_ID=False, sim_id2load=None, - #bypass_DF=None, - #bypass_FN=None, + # bypass_DF=None, + # bypass_FN=None, ): npi_seir = seir.build_npi_SEIR(s=self.s, load_ID=load_ID, sim_id2load=sim_id2load, config=config) p_draw = self.get_seir_parameters(load_ID=load_ID, sim_id2load=sim_id2load) @@ -419,15 +423,14 @@ def get_reduced_parameters_seir(self, load_ID=False, parameters = self.s.parameters.parameters_reduce(p_draw, npi_seir) parsed_parameters = self.s.compartments.parse_parameters( - parameters, self.s.parameters.pnames, self.unique_strings - ) + parameters, self.s.parameters.pnames, self.unique_strings + ) return parsed_parameters - def paramred_parallel(run_spec, snpi_fn): config_filepath = run_spec["config"] - gempyor_simulator = InferenceSimulator( + gempyor_simulator = GempyorSimulator( config_path=config_filepath, run_id="test_run_id", prefix="test_prefix/", @@ -453,7 +456,7 @@ def paramred_parallel(run_spec, snpi_fn): def paramred_parallel_config(run_spec, dummy): config_filepath = run_spec["config"] - gempyor_simulator = InferenceSimulator( + gempyor_simulator = GempyorSimulator( config_path=config_filepath, run_id="test_run_id", prefix="test_prefix/", diff --git a/flepimop/gempyor_pkg/src/gempyor/outcomes.py b/flepimop/gempyor_pkg/src/gempyor/outcomes.py index ec455f76b..2760b9bbe 100644 --- a/flepimop/gempyor_pkg/src/gempyor/outcomes.py +++ b/flepimop/gempyor_pkg/src/gempyor/outcomes.py @@ -72,14 +72,14 @@ def build_npi_Outcomes( npi = NPI.NPIBase.execute( npi_config=s.npi_config_outcomes, global_config=config, - geoids=s.spatset.nodenames, + subpops=s.subpop_struct.subpop_names, loaded_df=loaded_df, ) else: npi = NPI.NPIBase.execute( npi_config=s.npi_config_outcomes, global_config=config, - geoids=s.spatset.nodenames, + subpops=s.subpop_struct.subpop_names, ) return npi @@ -124,27 +124,27 @@ def read_parameters_from_config(s: setup.Setup): outcomes_config = s.outcomes_config["settings"][s.outcome_scenario] if s.outcomes_config["param_from_file"].get(): # Load the actual csv file - branching_file = s.outcomes_config["param_place_file"].as_str() + branching_file = s.outcomes_config["param_subpop_file"].as_str() branching_data = pa.parquet.read_table(branching_file).to_pandas() if "relative_probability" not in list(branching_data["quantity"]): raise ValueError(f"No 'relative_probability' quantity in {branching_file}, therefor making it useless") print( - "Loaded geoids in loaded relative probablity file:", - len(branching_data.geoid.unique()), + "Loaded subpops in loaded relative probablity file:", + len(branching_data.subpop.unique()), "", end="", ) - branching_data = branching_data[branching_data["geoid"].isin(s.spatset.nodenames)] + branching_data = branching_data[branching_data["subpop"].isin(s.subpop_struct.subpop_names)] print( "Intersect with seir simulation: ", - len(branching_data.geoid.unique()), + len(branching_data.subpop.unique()), "kept", ) - if len(branching_data.geoid.unique()) != len(s.spatset.nodenames): + if len(branching_data.subpop.unique()) != len(s.subpop_struct.subpop_names): raise ValueError( - f"Places in seir input files does not correspond to places in outcome probability file {branching_file}" + f"Places in seir input files does not correspond to subpops in outcome probability file {branching_file}" ) subclasses = [""] @@ -229,9 +229,9 @@ def read_parameters_from_config(s: setup.Setup): if len(rel_probability) > 0: logging.debug(f"Using 'param_from_file' for relative probability in outcome {class_name}") # Sort it in case the relative probablity file is mispecified - rel_probability.geoid = rel_probability.geoid.astype("category") - rel_probability.geoid = rel_probability.geoid.cat.set_categories(s.spatset.nodenames) - rel_probability = rel_probability.sort_values(["geoid"]) + rel_probability.subpop = rel_probability.subpop.astype("category") + rel_probability.subpop = rel_probability.subpop.cat.set_categories(s.subpop_struct.subpop_names) + rel_probability = rel_probability.sort_values(["subpop"]) parameters[class_name]["rel_probability"] = rel_probability["value"].to_numpy() else: logging.debug( @@ -266,7 +266,7 @@ def postprocess_and_write(sim_id, s, outcomes, hpar, npi): if npi is None: hnpi = pd.DataFrame( columns=[ - "geoid", + "subpop", "npi_name", "start_date", "end_date", @@ -279,16 +279,16 @@ def postprocess_and_write(sim_id, s, outcomes, hpar, npi): s.write_simID(ftype="hnpi", sim_id=sim_id, df=hnpi) -def dataframe_from_array(data, places, dates, comp_name): +def dataframe_from_array(data, subpops, dates, comp_name): """ Produce a dataframe in long form from a numpy matrix of - dimensions: dates * places. This dataframe are merged together + dimensions: dates * subpops. This dataframe are merged together to produce the final output """ - df = pd.DataFrame(data.astype(np.double), columns=places, index=dates) + df = pd.DataFrame(data.astype(np.double), columns=subpops, index=dates) df.index.name = "date" df.reset_index(inplace=True) - df = pd.melt(df, id_vars="date", value_name=comp_name, var_name="geoid") + df = pd.melt(df, id_vars="date", value_name=comp_name, var_name="subpop") return df @@ -300,13 +300,13 @@ def read_seir_sim(s, sim_id): def compute_all_multioutcomes(*, s, sim_id2write, parameters, loaded_values=None, npi=None): """Compute delay frame based on temporally varying input. We load the seir sim corresponding to sim_id to write""" - hpar = pd.DataFrame(columns=["geoid", "quantity", "outcome", "value"]) + hpar = pd.DataFrame(columns=["subpop", "quantity", "outcome", "value"]) all_data = {} dates = pd.date_range(s.ti, s.tf, freq="D") outcomes = dataframe_from_array( - np.zeros((len(dates), len(s.spatset.nodenames)), dtype=int), - s.spatset.nodenames, + np.zeros((len(dates), len(s.subpop_struct.subpop_names)), dtype=int), + s.subpop_struct.subpop_names, dates, "zeros", ).drop("zeros", axis=1) @@ -323,16 +323,16 @@ def compute_all_multioutcomes(*, s, sim_id2write, parameters, loaded_values=None source_array = get_filtered_incidI( seir_sim, dates, - s.spatset.nodenames, + s.subpop_struct.subpop_names, {"incidence": {"infection_stage": "I1"}}, ) all_data["incidI"] = source_array outcomes = pd.merge( outcomes, - dataframe_from_array(source_array, s.spatset.nodenames, dates, "incidI"), + dataframe_from_array(source_array, s.subpop_struct.subpop_names, dates, "incidI"), ) elif isinstance(source_name, dict): - source_array = get_filtered_incidI(seir_sim, dates, s.spatset.nodenames, source_name) + source_array = get_filtered_incidI(seir_sim, dates, s.subpop_struct.subpop_names, source_name) # we don't keep source in this cases else: # already defined outcomes source_array = all_data[source_name] @@ -347,14 +347,14 @@ def compute_all_multioutcomes(*, s, sim_id2write, parameters, loaded_values=None ].to_numpy() else: probabilities = parameters[new_comp]["probability"].as_random_distribution()( - size=len(s.spatset.nodenames) - ) # one draw per geoid + size=len(s.subpop_struct.subpop_names) + ) # one draw per subpop if "rel_probability" in parameters[new_comp]: probabilities = probabilities * parameters[new_comp]["rel_probability"] delays = parameters[new_comp]["delay"].as_random_distribution()( - size=len(s.spatset.nodenames) - ) # one draw per geoid + size=len(s.subpop_struct.subpop_names) + ) # one draw per subpop probabilities[probabilities > 1] = 1 probabilities[probabilities < 0] = 0 probabilities = np.repeat(probabilities[:, np.newaxis], len(dates), axis=1).T # duplicate in time @@ -366,18 +366,18 @@ def compute_all_multioutcomes(*, s, sim_id2write, parameters, loaded_values=None hpar, pd.DataFrame.from_dict( { - "geoid": s.spatset.nodenames, - "quantity": ["probability"] * len(s.spatset.nodenames), - "outcome": [new_comp] * len(s.spatset.nodenames), - "value": probabilities[0] * np.ones(len(s.spatset.nodenames)), + "subpop": s.subpop_struct.subpop_names, + "quantity": ["probability"] * len(s.subpop_struct.subpop_names), + "outcome": [new_comp] * len(s.subpop_struct.subpop_names), + "value": probabilities[0] * np.ones(len(s.subpop_struct.subpop_names)), } ), pd.DataFrame.from_dict( { - "geoid": s.spatset.nodenames, - "quantity": ["delay"] * len(s.spatset.nodenames), - "outcome": [new_comp] * len(s.spatset.nodenames), - "value": delays[0] * np.ones(len(s.spatset.nodenames)), + "subpop": s.subpop_struct.subpop_names, + "quantity": ["delay"] * len(s.subpop_struct.subpop_names), + "outcome": [new_comp] * len(s.subpop_struct.subpop_names), + "value": delays[0] * np.ones(len(s.subpop_struct.subpop_names)), } ), ], @@ -407,7 +407,7 @@ def compute_all_multioutcomes(*, s, sim_id2write, parameters, loaded_values=None stoch_delay_flag = False all_data[new_comp] = multishift(all_data[new_comp], delays, stoch_delay_flag=stoch_delay_flag) # Produce a dataframe an merge it - df_p = dataframe_from_array(all_data[new_comp], s.spatset.nodenames, dates, new_comp) + df_p = dataframe_from_array(all_data[new_comp], s.subpop_struct.subpop_names, dates, new_comp) outcomes = pd.merge(outcomes, df_p) # Make duration @@ -418,8 +418,8 @@ def compute_all_multioutcomes(*, s, sim_id2write, parameters, loaded_values=None ]["value"].to_numpy() else: durations = parameters[new_comp]["duration"].as_random_distribution()( - size=len(s.spatset.nodenames) - ) # one draw per geoid + size=len(s.subpop_struct.subpop_names) + ) # one draw per subpop durations = np.repeat(durations[:, np.newaxis], len(dates), axis=1).T # duplicate in time durations = np.round(durations).astype(int) @@ -428,10 +428,10 @@ def compute_all_multioutcomes(*, s, sim_id2write, parameters, loaded_values=None hpar, pd.DataFrame.from_dict( { - "geoid": s.spatset.nodenames, - "quantity": ["duration"] * len(s.spatset.nodenames), - "outcome": [new_comp] * len(s.spatset.nodenames), - "value": durations[0] * np.ones(len(s.spatset.nodenames)), + "subpop": s.subpop_struct.subpop_names, + "quantity": ["duration"] * len(s.subpop_struct.subpop_names), + "outcome": [new_comp] * len(s.subpop_struct.subpop_names), + "value": durations[0] * np.ones(len(s.subpop_struct.subpop_names)), } ), ], @@ -465,7 +465,7 @@ def compute_all_multioutcomes(*, s, sim_id2write, parameters, loaded_values=None df_p = dataframe_from_array( all_data[parameters[new_comp]["duration_name"]], - s.spatset.nodenames, + s.subpop_struct.subpop_names, dates, parameters[new_comp]["duration_name"], ) @@ -473,20 +473,20 @@ def compute_all_multioutcomes(*, s, sim_id2write, parameters, loaded_values=None elif "sum" in parameters[new_comp]: sum_outcome = np.zeros( - (len(dates), len(s.spatset.nodenames)), + (len(dates), len(s.subpop_struct.subpop_names)), dtype=all_data[parameters[new_comp]["sum"][0]].dtype, ) # Sum all concerned compartment. for cmp in parameters[new_comp]["sum"]: sum_outcome += all_data[cmp] all_data[new_comp] = sum_outcome - df_p = dataframe_from_array(sum_outcome, s.spatset.nodenames, dates, new_comp) + df_p = dataframe_from_array(sum_outcome, s.subpop_struct.subpop_names, dates, new_comp) outcomes = pd.merge(outcomes, df_p) return outcomes, hpar -def get_filtered_incidI(diffI, dates, places, filters): +def get_filtered_incidI(diffI, dates, subpops, filters): if list(filters.keys()) == ["incidence"]: vtype = "incidence" @@ -497,7 +497,7 @@ def get_filtered_incidI(diffI, dates, places, filters): diffI.drop(["mc_value_type"], inplace=True, axis=1) filters = filters[vtype] - incidI_arr = np.zeros((len(dates), len(places)), dtype=int) + incidI_arr = np.zeros((len(dates), len(subpops)), dtype=int) df = diffI.copy() for mc_type, mc_value in filters.items(): if isinstance(mc_value, str): diff --git a/flepimop/gempyor_pkg/src/gempyor/parameters.py b/flepimop/gempyor_pkg/src/gempyor/parameters.py index 0e7d25410..7ed1f4ab4 100644 --- a/flepimop/gempyor_pkg/src/gempyor/parameters.py +++ b/flepimop/gempyor_pkg/src/gempyor/parameters.py @@ -20,8 +20,7 @@ def __init__( *, ti: datetime.date, tf: datetime.date, - nodenames: list, - config_version: str = "v2", + subpop_names: list, ): self.pconfig = parameter_config self.pnames = [] @@ -31,149 +30,71 @@ def __init__( self.pnames2pindex = {} self.intervention_overlap_operation = {"sum": [], "prod": []} - if config_version == "v3": - self.pnames = self.pconfig.keys() - self.npar = len(self.pnames) - if self.npar != len(set([name.lower() for name in self.pnames])): - raise ValueError( - "Parameters of the SEIR model have the same name (remember that case is not sufficient!)" - #NOTE: should this lines be eliminated? - ) - - # Attributes of dictionary - for idx, pn in enumerate(self.pnames): - self.pnames2pindex[pn] = idx - self.pdata[pn] = {} - self.pdata[pn]["idx"] = idx - - # Parameter characterized by it's distribution - if self.pconfig[pn]["value"].exists(): - self.pdata[pn]["dist"] = self.pconfig[pn]["value"].as_random_distribution() - - # Parameter given as a file - elif self.pconfig[pn]["timeserie"].exists(): - fn_name = self.pconfig[pn]["timeserie"].get() - df = utils.read_df(fn_name).set_index("date") - df.index = pd.to_datetime(df.index) - if len(df.columns) >= len(nodenames): # one ts per geoid - df = df[nodenames] # make sure the order of geoids is the same as the reference - # (nodenames from spatial setup) and select the columns - elif len(df.columns) == 1: - df = pd.DataFrame( - pd.concat([df] * len(nodenames), axis=1).values, index=df.index, columns=nodenames - ) - else: - print("loaded col :", sorted(list(df.columns))) - print("geodata col:", sorted(nodenames)) - raise ValueError( - f"""ERROR loading file {fn_name} for parameter {pn}: the number of non 'date' - columns are {len(df.columns)}, expected {len(nodenames)} (the number of geoids) or one.""" - ) - - df = df[str(ti) : str(tf)] - if not (len(df.index) == len(pd.date_range(ti, tf))): - print("config dates:", pd.date_range(ti, tf)) - print("loaded dates:", df.index) - raise ValueError( - f"""ERROR loading file {fn_name} for parameter {pn}: - the 'date' index of the provided file does not cover the whole config time span from - {ti}->{tf}, where we have dates from {str(df.index[0])} to {str(df.index[-1])}""" - ) - # check the date range, need the lenght to be equal - if not (pd.date_range(ti, tf) == df.index).all(): - print("config dates:", pd.date_range(ti, tf)) - print("loaded dates:", df.index) - raise ValueError( - f"""ERROR loading file {fn_name} for parameter {pn}: - the 'date' index of the provided file does not cover the whole config time span from - {ti}->{tf}""" - ) + self.pnames = self.pconfig.keys() + self.npar = len(self.pnames) + if self.npar != len(set([name.lower() for name in self.pnames])): + raise ValueError("Parameters of the SEIR model have the same name (remember that case is not sufficient!)") + #NOTE: this lines was not eliminated so been targeted in test - self.pdata[pn]["ts"] = df - if self.pconfig[pn]["intervention_overlap_operation"].exists(): - self.pdata[pn]["intervention_overlap_operation"] = self.pconfig[pn][ - "intervention_overlap_operation" - ].as_str() + # Attributes of dictionary + for idx, pn in enumerate(self.pnames): + self.pnames2pindex[pn] = idx + self.pdata[pn] = {} + self.pdata[pn]["idx"] = idx + + # Parameter characterized by it's distribution + if self.pconfig[pn]["value"].exists(): + self.pdata[pn]["dist"] = self.pconfig[pn]["value"].as_random_distribution() + + # Parameter given as a file + elif self.pconfig[pn]["timeserie"].exists(): + fn_name = self.pconfig[pn]["timeserie"].get() + df = utils.read_df(fn_name).set_index("date") + df.index = pd.to_datetime(df.index) + if len(df.columns) >= len(subpop_names): # one ts per subpop + df = df[subpop_names] # make sure the order of subpops is the same as the reference + # (subpop_names from spatial setup) and select the columns + elif len(df.columns) == 1: + df = pd.DataFrame( + pd.concat([df] * len(subpop_names), axis=1).values, index=df.index, columns=subpop_names + ) else: - self.pdata[pn]["intervention_overlap_operation"] = "prod" - logging.debug( - f"No 'intervention_overlap_operation' for parameter {pn}, assuming multiplicative NPIs" + print("loaded col :", sorted(list(df.columns))) + print("geodata col:", sorted(subpop_names)) + raise ValueError( + f"""ERROR loading file {fn_name} for parameter {pn}: the number of non 'date' + columns are {len(df.columns)}, expected {len(subpop_names)} (the number of subpops) or one.""" ) - self.intervention_overlap_operation[self.pdata[pn]["intervention_overlap_operation"]].append(pn.lower()) - elif config_version == "old": - n_parallel_compartments = 1 - n_parallel_transitions = 0 - compartments_dict = {} - compartments_map = {} - transition_map = {} - if "parallel_structure" in self.pconfig: - if "compartments" not in self.pconfig["parallel_structure"]: + df = df[str(ti) : str(tf)] + if not (len(df.index) == len(pd.date_range(ti, tf))): + print("config dates:", pd.date_range(ti, tf)) + print("loaded dates:", df.index) raise ValueError( - f"A config specifying a parallel structure should assign compartments to that structure" + f"""ERROR loading file {fn_name} for parameter {pn}: + the 'date' index of the provided file does not cover the whole config time span from + {ti}->{tf}, where we have dates from {str(df.index[0])} to {str(df.index[-1])}""" ) - compartments_map = self.pconfig["parallel_structure"]["compartments"] - n_parallel_compartments = len(compartments_map.get()) - compartments_dict = {k: v for v, k in enumerate(compartments_map.get())} - if not "transitions" in self.pconfig["parallel_structure"]: + # check the date range, need the lenght to be equal + if not (pd.date_range(ti, tf) == df.index).all(): + print("config dates:", pd.date_range(ti, tf)) + print("loaded dates:", df.index) raise ValueError( - f"A config specifying a parallel structure should assign transitions to that structure" + f"""ERROR loading file {fn_name} for parameter {pn}: + the 'date' index of the provided file does not cover the whole config time span from + {ti}->{tf}""" ) - transitions_map = self.pconfig["parallel_structure"]["transitions"] - n_parallel_transitions = len(transitions_map.get()) - transition_map = transitions_map - - self.alpha_val = 1.0 - if "alpha" in self.pconfig: - self.alpha_val = self.pconfig["alpha"].as_evaled_expression() - self.sigma_val = self.pconfig["sigma"].as_evaled_expression() - gamma_dist = self.pconfig["gamma"].as_random_distribution() - R0s_dist = self.pconfig["R0s"].as_random_distribution() - ### Do some conversions - # Convert numbers to distribution like object that can be called - p_dists = { - "alpha": self.picklable_lamda_alpha, - "sigma": self.picklable_lamda_sigma, - "gamma": gamma_dist, - "R0": R0s_dist, - } - for key in p_dists: - self.intervention_overlap_operation["prod"].append(key.lower()) - - if n_parallel_compartments > 1.5: - for compartment, index in compartments_dict.items(): - if "susceptibility_reduction" in compartments_map[compartment]: - pn = f"susceptibility_reduction{index}" - p_dists[pn] = compartments_map[compartment]["susceptibility_reduction"].as_random_distribution() - self.intervention_overlap_operation["prod"].append(pn.lower()) - else: - raise ValueError(f"Susceptibility Reduction not found for comp {compartment}") - if "transmissibility_reduction" in compartments_map[compartment]: - pn = f"transmissibility_reduction{index}" - p_dists[pn] = compartments_map[compartment][ - "transmissibility_reduction" - ].as_random_distribution() - self.intervention_overlap_operation["prod"].append(pn.lower()) - else: - raise ValueError(f"Transmissibility Reduction not found for comp {compartment}") - for transition in range(n_parallel_transitions): - pn = f"transition_rate{transition}" - p_dists[pn] = transition_map[transition]["rate"].as_random_distribution() - self.intervention_overlap_operation["sum"].append(pn.lower()) + self.pdata[pn]["ts"] = df + if self.pconfig[pn]["intervention_overlap_operation"].exists(): + self.pdata[pn]["intervention_overlap_operation"] = self.pconfig[pn][ + "intervention_overlap_operation" + ].as_str() + else: + self.pdata[pn]["intervention_overlap_operation"] = "prod" + logging.debug(f"No 'intervention_overlap_operation' for parameter {pn}, assuming multiplicative NPIs") + self.intervention_overlap_operation[self.pdata[pn]["intervention_overlap_operation"]].append(pn.lower()) - ### Build the new structure - for idx, pn in enumerate(p_dists): - self.pnames.append(pn) - self.pnames2pindex[pn] = idx - self.pdata[pn] = {} - self.pdata[pn]["idx"] = idx - self.pdata[pn]["dist"] = p_dists[pn] - if "transition_rate" not in pn: - self.pdata[pn]["intervention_overlap_operation"] = "prod" - else: - self.pdata[pn]["intervention_overlap_operation"] = "sum" - self.npar = len(self.pnames) logging.debug(f"We have {self.npar} parameter: {self.pnames}") logging.debug(f"Data to sample is: {self.pdata}") logging.debug(f"Index in arrays are: {self.pnames2pindex}") diff --git a/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py b/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py index d1b46c2c6..61a650908 100644 --- a/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py +++ b/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py @@ -27,7 +27,7 @@ def _DataFrame2NumbaDict(df, amounts, setup) -> nb.typed.Dict: ) seeding_dict["seeding_sources"] = np.zeros(len(amounts), dtype=np.int64) seeding_dict["seeding_destinations"] = np.zeros(len(amounts), dtype=np.int64) - seeding_dict["seeding_places"] = np.zeros(len(amounts), dtype=np.int64) + seeding_dict["seeding_subpops"] = np.zeros(len(amounts), dtype=np.int64) seeding_amounts = np.zeros(len(amounts), dtype=np.float64) nb_seed_perday = np.zeros(setup.n_days, dtype=np.int64) @@ -35,9 +35,9 @@ def _DataFrame2NumbaDict(df, amounts, setup) -> nb.typed.Dict: n_seeding_ignored_before = 0 n_seeding_ignored_after = 0 for idx, (row_index, row) in enumerate(df.iterrows()): - if row["place"] not in setup.spatset.nodenames: + if row["subpop"] not in setup.subpop_struct.subpop_names: raise ValueError( - f"Invalid place '{row['place']}' in row {row_index + 1} of seeding::lambda_file. Not found in geodata." + f"Invalid subpop '{row['subpop']}' in row {row_index + 1} of seeding::lambda_file. Not found in geodata." ) if (row["date"].date() - setup.ti).days >= 0: @@ -49,7 +49,7 @@ def _DataFrame2NumbaDict(df, amounts, setup) -> nb.typed.Dict: destination_dict = {grp_name: row[f"destination_{grp_name}"] for grp_name in cmp_grp_names} seeding_dict["seeding_sources"][idx] = setup.compartments.get_comp_idx(source_dict) seeding_dict["seeding_destinations"][idx] = setup.compartments.get_comp_idx(destination_dict) - seeding_dict["seeding_places"][idx] = setup.spatset.nodenames.index(row["place"]) + seeding_dict["seeding_subpops"][idx] = setup.subpop_struct.subpop_names.index(row["subpop"]) seeding_amounts[idx] = amounts[idx] else: n_seeding_ignored_after += 1 @@ -90,38 +90,73 @@ def draw_ic(self, sim_id: int, setup) -> np.ndarray: allow_missing_compartments = False if "allow_missing_nodes" in self.initial_conditions_config.keys(): if self.initial_conditions_config["allow_missing_nodes"].get(): - allow_missing_nodes=True + allow_missing_nodes = True if "allow_missing_compartments" in self.initial_conditions_config.keys(): if self.initial_conditions_config["allow_missing_compartments"].get(): - allow_missing_compartments=True + allow_missing_compartments = True + + # Places to allocate the rest of the population + rests = [] if method == "Default": ## JK : This could be specified in the config y0 = np.zeros((setup.compartments.compartments.shape[0], setup.nnodes)) y0[0, :] = setup.popnodes - elif method == "SetInitialConditions": - # TODO: this format should allow not complete configurations - # - Does not support the new way of doing compartiment indexing - logger.critical("Untested method SetInitialConditions !!! Please report this messsage.") - ic_df = pd.read_csv( - self.initial_conditions_config["states_file"].as_str(), - converters={"place": lambda x: str(x)}, - skipinitialspace=True, - ) - if ic_df.empty: - raise ValueError(f"There is no entry for initial time ti in the provided initial_conditions::states_file.") + + elif method == "SetInitialConditions" or method == "SetInitialConditionsFolderDraw": + # TODO Think about - Does not support the new way of doing compartment indexing + if method == "SetInitialConditionsFolderDraw": + ic_df = setup.read_simID(ftype=self.initial_conditions_config["initial_file_type"], sim_id=sim_id) + else: + ic_df = read_df( + self.initial_conditions_config["initial_conditions_file"].get(), + ) + y0 = np.zeros((setup.compartments.compartments.shape[0], setup.nnodes)) - for pl_idx, pl in enumerate(setup.spatset.nodenames): # - if pl in list(ic_df["place"]): - states_pl = ic_df[ic_df["place"] == pl] + for pl_idx, pl in enumerate(setup.subpop_struct.subpop_names): # + if pl in list(ic_df["subpop"]): + states_pl = ic_df[ic_df["subpop"] == pl] for comp_idx, comp_name in setup.compartments.compartments["name"].items(): - y0[comp_idx, pl_idx] = float(states_pl[states_pl["comp"] == comp_name]["amount"]) + + if "mc_name" in states_pl.columns: + ic_df_compartment_val = states_pl[states_pl["mc_name"] == comp_name]["amount"] + else: + filters = setup.compartments.compartments.iloc[comp_idx].drop("name") + ic_df_compartment = states_pl.copy() + for mc_name, mc_value in filters.items(): + ic_df_compartment = ic_df_compartment[ic_df_compartment["mc_" + mc_name] == mc_value][ + "amount" + ] + if len(ic_df_compartment_val) > 1: + raise ValueError( + f"ERROR: Several ({len(ic_df_compartment_val)}) rows are matches for compartment {comp_name} in init file: filters returned {ic_df_compartment_val}" + ) + elif ic_df_compartment_val.empty: + if allow_missing_compartments: + ic_df_compartment_val = 0.0 + else: + raise ValueError( + f"Initial Conditions: Could not set compartment {comp_name} (id: {comp_idx}) in node {pl} (id: {pl_idx}). The data from the init file is {states_pl}. \n \ + Use 'allow_missing_compartments' to default to 0 for compartments without initial conditions" + ) + if "rest" in ic_df_compartment_val: + rests.append([comp_idx, pl_idx]) + else: + y0[comp_idx, pl_idx] = float(ic_df_compartment_val) elif allow_missing_nodes: - print(f"WARNING: State load does not exist for node {pl}, assuming fully susceptible population") - y0[0, pl_idx] = setup.popnodes[pl_idx] + logger.critical( + f"No initial conditions for for node {pl}, assuming everyone (n={setup.popnodes[pl_idx]}) in the first metacompartment ({setup.compartments.compartments['name'].iloc[0]})" + ) + if "proportional" in self.initial_conditions_config.keys(): + if self.initial_conditions_config["proportional"].get(): + y0[0, pl_idx] = 1.0 + else: + y0[0, pl_idx] = setup.popnodes[pl_idx] + else: + y0[0, pl_idx] = setup.popnodes[pl_idx] else: raise ValueError( - f"place {pl} does not exist in initial_conditions::states_file. You can set allow_missing_nodes=TRUE to bypass this error" + f"subpop {pl} does not exist in initial_conditions::states_file. You can set allow_missing_nodes=TRUE to bypass this error" ) elif method == "InitialConditionsFolderDraw" or method == "FromFile": if method == "InitialConditionsFolderDraw": @@ -132,61 +167,85 @@ def draw_ic(self, sim_id: int, setup) -> np.ndarray: ) # annoying conversion because sometime the parquet columns get attributed a timezone... - ic_df["date"] = pd.to_datetime(ic_df["date"], utc=True) # force date to be UTC + ic_df["date"] = pd.to_datetime(ic_df["date"], utc=True) # force date to be UTC ic_df["date"] = ic_df["date"].dt.date ic_df["date"] = ic_df["date"].astype(str) ic_df = ic_df[(ic_df["date"] == str(setup.ti)) & (ic_df["mc_value_type"] == "prevalence")] if ic_df.empty: - raise ValueError(f"There is no entry for initial time ti in the provided initial_conditions::states_file.") + raise ValueError( + f"There is no entry for initial time ti in the provided initial_conditions::states_file." + ) y0 = np.zeros((setup.compartments.compartments.shape[0], setup.nnodes)) for comp_idx, comp_name in setup.compartments.compartments["name"].items(): # rely on all the mc's instead of mc_name to avoid errors due to e.g order. - # before: only + # before: only # ic_df_compartment = ic_df[ic_df["mc_name"] == comp_name] filters = setup.compartments.compartments.iloc[comp_idx].drop("name") ic_df_compartment = ic_df.copy() for mc_name, mc_value in filters.items(): - ic_df_compartment = ic_df_compartment[ic_df_compartment["mc_"+mc_name] == mc_value] - + ic_df_compartment = ic_df_compartment[ic_df_compartment["mc_" + mc_name] == mc_value] if len(ic_df_compartment) > 1: - #ic_df_compartment = ic_df_compartment.iloc[0] - raise ValueError(f"ERROR: Several ({len(ic_df_compartment)}) rows are matches for compartment {mc_name} in init file: filter {filters} returned {ic_df_compartment}") + # ic_df_compartment = ic_df_compartment.iloc[0] + raise ValueError( + f"ERROR: Several ({len(ic_df_compartment)}) rows are matches for compartment {mc_name} in init file: filter {filters} returned {ic_df_compartment}" + ) elif ic_df_compartment.empty: if allow_missing_compartments: - ic_df_compartment = pd.DataFrame(0, columns=ic_df_compartment.columns, index = [0]) + ic_df_compartment = pd.DataFrame(0, columns=ic_df_compartment.columns, index=[0]) else: - raise ValueError(f"Initial Conditions: Could not set compartment {comp_name} (id: {comp_idx}) in node {pl} (id: {pl_idx}). The data from the init file is {ic_df_compartment[pl]}.") - elif (ic_df_compartment["mc_name"].iloc[0] != comp_name): - print(f"WARNING: init file mc_name {ic_df_compartment['mc_name'].iloc[0]} does not match compartment mc_name {comp_name}") - + raise ValueError( + f"Initial Conditions: Could not set compartment {comp_name} (id: {comp_idx}) in node {pl} (id: {pl_idx}). The data from the init file is {ic_df_compartment[pl]}." + ) + elif ic_df_compartment["mc_name"].iloc[0] != comp_name: + print( + f"WARNING: init file mc_name {ic_df_compartment['mc_name'].iloc[0]} does not match compartment mc_name {comp_name}" + ) - for pl_idx, pl in enumerate(setup.spatset.nodenames): + for pl_idx, pl in enumerate(setup.subpop_struct.subpop_names): if pl in ic_df.columns: - y0[comp_idx, pl_idx] = float(ic_df_compartment[pl]) + y0[comp_idx, pl_idx] = float(ic_df_compartment[pl]) elif allow_missing_nodes: - logging.warning( - f"WARNING: State load does not exist for node {pl}, assuming fully susceptible population" + logger.critical( + f"No initial conditions for for node {pl}, assuming everyone (n={setup.popnodes[pl_idx]}) in the first metacompartments ({setup.compartments.compartments['name'].iloc[0]})" ) + if "proportion" in self.initial_conditions_config.keys(): + if self.initial_conditions_config["proportion"].get(): + y0[0, pl_idx] = 1.0 y0[0, pl_idx] = setup.popnodes[pl_idx] else: raise ValueError( - f"place {pl} does not exist in initial_conditions::states_file. You can set allow_missing_nodes=TRUE to bypass this error" + f"subpop {pl} does not exist in initial_conditions::states_file. You can set allow_missing_nodes=TRUE to bypass this error" ) else: raise NotImplementedError(f"unknown initial conditions method [got: {method}]") - + + # rest + if rests: # not empty + for comp_idx, pl_idx in rests: + total = setup.popnodes[pl_idx] + if "proportional" in self.initial_conditions_config.keys(): + if self.initial_conditions_config["proportional"].get(): + total = 1.0 + y0[comp_idx, pl_idx] = total - y0[:, pl_idx].sum() + + if "proportional" in self.initial_conditions_config.keys(): + if self.initial_conditions_config["proportional"].get(): + y0 = y0 * setup.popnodes[pl_idx] + # check that the inputed values sums to the node_population: error = False - for pl_idx, pl in enumerate(setup.spatset.nodenames): + for pl_idx, pl in enumerate(setup.subpop_struct.subpop_names): n_y0 = y0[:, pl_idx].sum() n_pop = setup.popnodes[pl_idx] - if abs(n_y0-n_pop) > 100: + if abs(n_y0 - n_pop) > 1: error = True - print(f"ERROR: place {pl} (idx: pl_idx) has a population from initial condition of {n_y0} while population from geodata is {n_pop} (absolute difference should be < 1, here is {abs(n_y0-n_pop)})") - if False: + print( + f"ERROR: subpop_names {pl} (idx: pl_idx) has a population from initial condition of {n_y0} while population from geodata is {n_pop} (absolute difference should be < 1, here is {abs(n_y0-n_pop)})" + ) + if error: raise ValueError() return y0 @@ -198,13 +257,13 @@ def draw_seeding(self, sim_id: int, setup) -> nb.typed.Dict: if method == "NegativeBinomialDistributed" or method == "PoissonDistributed": seeding = pd.read_csv( self.seeding_config["lambda_file"].as_str(), - converters={"place": lambda x: str(x)}, + converters={"subpop": lambda x: str(x)}, parse_dates=["date"], skipinitialspace=True, ) - dupes = seeding[seeding.duplicated(["place", "date"])].index + 1 + dupes = seeding[seeding.duplicated(["subpop", "date"])].index + 1 if not dupes.empty: - raise ValueError(f"Repeated place-date in rows {dupes.tolist()} of seeding::lambda_file.") + raise ValueError(f"Repeated subpop-date in rows {dupes.tolist()} of seeding::lambda_file.") elif method == "FolderDraw": seeding = pd.read_csv( setup.get_input_filename( @@ -212,19 +271,19 @@ def draw_seeding(self, sim_id: int, setup) -> nb.typed.Dict: sim_id=sim_id, extension_override="csv", ), - converters={"place": lambda x: str(x)}, + converters={"subpop": lambda x: str(x)}, parse_dates=["date"], skipinitialspace=True, ) elif method == "FromFile": seeding = pd.read_csv( self.seeding_config["seeding_file"].get(), - converters={"place": lambda x: str(x)}, + converters={"subpop": lambda x: str(x)}, parse_dates=["date"], skipinitialspace=True, ) elif method == "NoSeeding": - seeding = pd.DataFrame(columns=["date", "place"]) + seeding = pd.DataFrame(columns=["date", "subpop"]) return _DataFrame2NumbaDict(df=seeding, amounts=[], setup=setup) else: raise NotImplementedError(f"unknown seeding method [got: {method}]") @@ -236,6 +295,7 @@ def draw_seeding(self, sim_id: int, setup) -> nb.typed.Dict: if method == "PoissonDistributed": amounts = np.random.poisson(seeding["amount"]) elif method == "NegativeBinomialDistributed": + raise ValueError("Seeding method 'NegativeBinomialDistributed' is not supported by flepiMoP anymore.") amounts = np.random.negative_binomial(n=5, p=5 / (seeding["amount"] + 5)) elif method == "FolderDraw" or method == "FromFile": amounts = seeding["amount"] diff --git a/flepimop/gempyor_pkg/src/gempyor/seir.py b/flepimop/gempyor_pkg/src/gempyor/seir.py index 44e1e6bf2..3edadb0c4 100644 --- a/flepimop/gempyor_pkg/src/gempyor/seir.py +++ b/flepimop/gempyor_pkg/src/gempyor/seir.py @@ -13,7 +13,7 @@ logger = logging.getLogger(__name__) -def steps_SEIR( +def build_step_source_arg( s, parsed_parameters, transition_array, @@ -42,7 +42,7 @@ def steps_SEIR( keys_ref = [ "seeding_sources", "seeding_destinations", - "seeding_places", + "seeding_subpops", "day_start_idx", ] for key, item in seeding_data.items(): @@ -84,6 +84,30 @@ def steps_SEIR( "population": s.popnodes, "stochastic_p": s.stoch_traj_flag, } + return fnct_args + + +def steps_SEIR( + s, + parsed_parameters, + transition_array, + proportion_array, + proportion_info, + initial_conditions, + seeding_data, + seeding_amounts, +): + + fnct_args = build_step_source_arg( + s, + parsed_parameters, + transition_array, + proportion_array, + proportion_info, + initial_conditions, + seeding_data, + seeding_amounts, + ) logging.info(f"Integrating with method {s.integration_method}") @@ -147,7 +171,7 @@ def build_npi_SEIR(s, load_ID, sim_id2load, config, bypass_DF=None, bypass_FN=No npi = NPI.NPIBase.execute( npi_config=s.npi_config_seir, global_config=config, - geoids=s.spatset.nodenames, + subpops=s.subpop_struct.subpop_names, loaded_df=loaded_df, pnames_overlap_operation_sum=s.parameters.intervention_overlap_operation["sum"], ) @@ -155,7 +179,7 @@ def build_npi_SEIR(s, load_ID, sim_id2load, config, bypass_DF=None, bypass_FN=No npi = NPI.NPIBase.execute( npi_config=s.npi_config_seir, global_config=config, - geoids=s.spatset.nodenames, + subpops=s.subpop_struct.subpop_names, pnames_overlap_operation_sum=s.parameters.intervention_overlap_operation["sum"], ) return npi @@ -269,7 +293,7 @@ def states2Df(s, states): prev_df = pd.DataFrame( data=states_prev.reshape(s.n_days * s.compartments.get_ncomp(), s.nnodes), index=ts_index, - columns=s.spatset.nodenames, + columns=s.subpop_struct.subpop_names, ).reset_index() prev_df = pd.merge( left=s.compartments.get_compartments_explicitDF(), @@ -287,7 +311,7 @@ def states2Df(s, states): incid_df = pd.DataFrame( data=states_incid.reshape(s.n_days * s.compartments.get_ncomp(), s.nnodes), index=ts_index, - columns=s.spatset.nodenames, + columns=s.subpop_struct.subpop_names, ).reset_index() incid_df = pd.merge( left=s.compartments.get_compartments_explicitDF(), diff --git a/flepimop/gempyor_pkg/src/gempyor/setup.py b/flepimop/gempyor_pkg/src/gempyor/setup.py index 25d5ca01f..f4be77e81 100644 --- a/flepimop/gempyor_pkg/src/gempyor/setup.py +++ b/flepimop/gempyor_pkg/src/gempyor/setup.py @@ -12,6 +12,7 @@ from . import compartments from . import parameters from . import seeding_ic +from .subpopulation_structure import SubpopulationStructure from .utils import config, read_df, write_df from . import file_paths import logging @@ -21,7 +22,7 @@ class Setup: """ - This class hold a setup model setup. + This class hold a full model setup. """ def __init__( @@ -33,7 +34,6 @@ def __init__( ti, # time to start tf, # time to finish npi_scenario=None, - config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, @@ -61,6 +61,7 @@ def __init__( self.tf = tf ## we end on 23:59 on tf if self.tf <= self.ti: raise ValueError("tf (time to finish) is less than or equal to ti (time to start)") + self.npi_scenario = npi_scenario self.npi_config_seir = npi_config_seir self.seeding_config = seeding_config @@ -75,11 +76,11 @@ def __init__( self.first_sim_index = first_sim_index self.outcome_scenario = outcome_scenario - self.spatset = spatial_setup + self.subpop_struct = spatial_setup self.n_days = (self.tf - self.ti).days + 1 # because we include s.ti and s.tf - self.nnodes = self.spatset.nnodes - self.popnodes = self.spatset.popnodes - self.mobility = self.spatset.mobility + self.nnodes = self.subpop_struct.nnodes + self.popnodes = self.subpop_struct.popnodes + self.mobility = self.subpop_struct.mobility self.stoch_traj_flag = stoch_traj_flag @@ -106,7 +107,6 @@ def __init__( if "integration" in self.seir_config.keys(): if "method" in self.seir_config["integration"].keys(): self.integration_method = self.seir_config["integration"]["method"].get() - print(self.integration_method) if self.integration_method == "best.current": self.integration_method = "rk4.jit" if self.integration_method == "rk4": @@ -129,39 +129,23 @@ def __init__( if self.dt is not None: self.dt = float(self.dt) - if config_version is None: - config_version = "v3" - logging.debug(f"Config version not provided, infering type {config_version}") - - if config_version not in ["old", "v2", "v3"]: - raise ValueError( - f"Configuration version unknown: {config_version}. \n" - f"Should be either non-specified (default: 'v3'), or set to 'old' or 'v2'." - ) - elif config_version == "old" or config_version == "v2": - # NOTE: even behaved as old, "v2" seems by default in parameter.py - raise ValueError( - f"Configuration version 'old' and 'v2' are no longer supported by flepiMoP\n" - f"Please use a 'v3' instead, or use the COVIDScenarioPipeline package. " + # Think if we really want to hold this up. + self.parameters = parameters.Parameters( + parameter_config=self.parameters_config, + # NOTE: 'config_version' was gone, no longer needed? + ti=self.ti, + tf=self.tf, + subpop_names=self.subpop_struct.subpop_names, ) - - # Think if we really want to hold this up. - self.parameters = parameters.Parameters( - parameter_config=self.parameters_config, - config_version=config_version, - ti=self.ti, - tf=self.tf, - nodenames=self.spatset.nodenames, - ) - self.seedingAndIC = seeding_ic.SeedingAndIC( - seeding_config=self.seeding_config, - initial_conditions_config=self.initial_conditions_config, - ) - # really ugly references to the config globally here. - if config["compartments"].exists() and self.seir_config is not None: - self.compartments = compartments.Compartments( - seir_config=self.seir_config, compartments_config=config["compartments"] + self.seedingAndIC = seeding_ic.SeedingAndIC( + seeding_config=self.seeding_config, + initial_conditions_config=self.initial_conditions_config, ) + # really ugly references to the config globally here. + if config["compartments"].exists() and self.seir_config is not None: + self.compartments = compartments.Compartments( + seir_config=self.seir_config, compartments_config=config["compartments"] + ) # 3. Outcomes self.npi_config_outcomes = None @@ -281,98 +265,3 @@ def write_simID( df=df, ) return fname - - -class SpatialSetup: - def __init__(self, *, setup_name, geodata_file, mobility_file, popnodes_key, nodenames_key): - self.setup_name = setup_name - self.data = pd.read_csv( - geodata_file, converters={nodenames_key: lambda x: str(x).strip()}, skipinitialspace=True - ) # geoids and populations, strip whitespaces - self.nnodes = len(self.data) # K = # of locations - - # popnodes_key is the name of the column in geodata_file with populations - if popnodes_key not in self.data: - raise ValueError( - f"popnodes_key: {popnodes_key} does not correspond to a column in geodata: {self.data.columns}" - ) - self.popnodes = self.data[popnodes_key].to_numpy() # population - if len(np.argwhere(self.popnodes == 0)): - raise ValueError( - f"There are {len(np.argwhere(self.popnodes == 0))} nodes with population zero, this is not supported." - ) - - # nodenames_key is the name of the column in geodata_file with geoids - if nodenames_key not in self.data: - raise ValueError(f"nodenames_key: {nodenames_key} does not correspond to a column in geodata.") - self.nodenames = self.data[nodenames_key].tolist() - if len(self.nodenames) != len(set(self.nodenames)): - raise ValueError(f"There are duplicate nodenames in geodata.") - - if mobility_file is not None: - mobility_file = pathlib.Path(mobility_file) - if mobility_file.suffix == ".txt": - print("Mobility files as matrices are not recommended. Please switch soon to long form csv files.") - self.mobility = scipy.sparse.csr_matrix( - np.loadtxt(mobility_file), dtype=int - ) # K x K matrix of people moving - # Validate mobility data - if self.mobility.shape != (self.nnodes, self.nnodes): - raise ValueError( - f"mobility data must have dimensions of length of geodata ({self.nnodes}, {self.nnodes}). Actual: {self.mobility.shape}" - ) - - elif mobility_file.suffix == ".csv": - mobility_data = pd.read_csv(mobility_file, converters={"ori": str, "dest": str}, skipinitialspace=True) - nn_dict = {v: k for k, v in enumerate(self.nodenames)} - mobility_data["ori_idx"] = mobility_data["ori"].apply(nn_dict.__getitem__) - mobility_data["dest_idx"] = mobility_data["dest"].apply(nn_dict.__getitem__) - if any(mobility_data["ori_idx"] == mobility_data["dest_idx"]): - raise ValueError( - f"Mobility fluxes with same origin and destination in long form matrix. This is not supported" - ) - - self.mobility = scipy.sparse.coo_matrix( - (mobility_data.amount, (mobility_data.ori_idx, mobility_data.dest_idx)), - shape=(self.nnodes, self.nnodes), - dtype=int, - ).tocsr() - - elif mobility_file.suffix == ".npz": - self.mobility = scipy.sparse.load_npz(mobility_file).astype(int) - # Validate mobility data - # data valication/arrangement is needed - if self.mobility.shape != (self.nnodes, self.nnodes): - raise ValueError( - f"mobility data must have dimensions of length of geodata ({self.nnodes}, {self.nnodes}). Actual: {self.mobility.shape}" - ) - else: - raise ValueError( - f"Mobility data must either be a .csv file in longform (recommended) or a .txt matrix file. Got {mobility_file}" - ) - - # Make sure mobility values <= the population of src node - tmp = (self.mobility.T - self.popnodes).T - tmp[tmp < 0] = 0 - if tmp.any(): - rows, cols, values = scipy.sparse.find(tmp) - errmsg = "" - for r, c, v in zip(rows, cols, values): - errmsg += f"\n({r}, {c}) = {self.mobility[r, c]} > population of '{self.nodenames[r]}' = {self.popnodes[r]}" - raise ValueError( - f"The following entries in the mobility data exceed the source node populations in geodata:{errmsg}" - ) - - tmp = self.popnodes - np.squeeze(np.asarray(self.mobility.sum(axis=1))) - tmp[tmp > 0] = 0 - if tmp.any(): - (row,) = np.where(tmp) - errmsg = "" - for r in row: - errmsg += f"\n sum accross row {r} exceed population of node '{self.nodenames[r]}' ({self.popnodes[r]}), by {-tmp[r]}" - raise ValueError( - f"The following rows in the mobility data exceed the source node populations in geodata:{errmsg}" - ) - else: - logging.critical("No mobility matrix specified -- assuming no one moves") - self.mobility = scipy.sparse.csr_matrix(np.zeros((self.nnodes, self.nnodes)), dtype=int) diff --git a/flepimop/gempyor_pkg/src/gempyor/simulate.py b/flepimop/gempyor_pkg/src/gempyor/simulate.py new file mode 100644 index 000000000..102d6422c --- /dev/null +++ b/flepimop/gempyor_pkg/src/gempyor/simulate.py @@ -0,0 +1,417 @@ +#!/usr/bin/env python + +## +# @file +# @brief Runs hospitalization model +# +# @details +# +# ## Configuration Items +# +# ```yaml +# name: +# setup_name: +# start_date: +# end_date: +# dt: float +# nslots: overridden by the -n/--nslots script parameter +# data_path: +# spatial_setup: +# geodata: +# mobility: +# +# seir: +# parameters +# alpha: +# sigma: +# gamma: +# R0s: +# +# interventions: +# scenarios: +# - +# - +# - ... +# settings: +# : +# template: choose one - "SinglePeriodModifier", ", "StackedModifier" +# ... +# : +# template: choose one - "SinglePeriodModifier", "", "StackedModifier" +# ... +# +# seeding: +# method: choose one - "PoissonDistributed", "FolderDraw" +# ``` +# +# ### interventions::scenarios::settings:: +# +# If {template} is +# ```yaml +# interventions: +# scenarios: +# : +# template: SinglePeriodModifier +# parameter: choose one - "alpha, sigma, gamma, r0" +# period_start_date: +# period_end_date: +# value: +# subpop: optional +# ``` +# +# If {template} is +# ```yaml +# interventions: +# scenarios: +# : +# template: +# period_start_date: +# period_end_date: +# value: +# subpop: optional +# ``` +# +# If {template} is StackedModifier +# ```yaml +# interventions: +# scenarios: +# : +# template: StackedModifier +# scenarios: +# ``` +# +# ### seeding +# +# If {seeding::method} is PoissonDistributed +# ```yaml +# seeding: +# method: PoissonDistributed +# lambda_file: +# ``` +# +# If {seeding::method} is FolderDraw +# ```yaml +# seeding: +# method: FolderDraw +# folder_path: \; make sure this ends in a '/' +# ``` +# +# ## Input Data +# +# * {data_path}/{spatial_setup::geodata} is a csv with columns {spatial_setup::subpop_names} and {spatial_setup::popnodes} +# * {data_path}/{spatial_setup::mobility} +# +# If {seeding::method} is PoissonDistributed +# * {seeding::lambda_file} +# +# If {seeding::method} is FolderDraw +# * {seeding::folder_path}/[simulation ID].impa.csv +# +# ## Output Data +# +# * model_output/{setup_name}_[scenario]/[simulation ID].seir.[csv/parquet] +# * model_parameters/{setup_name}_[scenario]/[simulation ID].spar.[csv/parquet] +# * model_parameters/{setup_name}_[scenario]/[simulation ID].snpi.[csv/parquet] +# ## Configuration Items +# +# ```yaml +# outcomes: +# method: delayframe # Only fast is supported atm. Makes fast delay_table computations. Later agent-based method ? +# paths: +# param_from_file: TRUE # +# param_subpop_file: # OPTIONAL: File with param per csv. For each param in this file +# scenarios: # Outcomes scenarios to run +# - low_death_rate +# - mid_death_rate +# settings: # Setting for each scenario +# low_death_rate: +# new_comp1: # New compartement name +# source: incidence # Source of the new compartement: either an previously defined compartement or "incidence" for diffI of the SEIR +# probability: # Branching probability from source +# delay: # Delay from incidence of source to incidence of new_compartement +# duration: # OPTIONAL ! Duration in new_comp. If provided, the model add to it's +# #output "new_comp1_curr" with current amount in new_comp1 +# new_comp2: # Example for a second compatiment +# source: new_comp1 +# probability: +# delay: +# duration: +# death_tot: # Possibility to combine compartements for death. +# sum: ['death_hosp', 'death_ICU', 'death_incid'] +# +# mid_death_rate: +# ... +# +# ## Input Data +# +# * {param_subpop_file} is a csv with columns subpop, parameter, value. Parameter is constructed as, e.g for comp1: +# probability: Pnew_comp1|source +# delay: Dnew_comp1 +# duration: Lnew_comp1 + + +# ## Output Data +# * {output_path}/model_output/{setup_name}_[scenario]/[simulation ID].hosp.parquet + + +## @cond + +import multiprocessing +import pathlib +import time, os + +import click + +from gempyor import seir, outcomes, setup, file_paths +from gempyor.utils import config + +# from .profile import profile_options + + +@click.command() +@click.option( + "-c", + "--config", + "config_file", + envvar=["CONFIG_PATH", "CONFIG_PATH"], + type=click.Path(exists=True), + required=True, + help="configuration file for this simulation", +) +@click.option( + "-s", + "--npi_scenario", + "npi_scenarios", + envvar="FLEPI_NPI_SCENARIOS", + type=str, + default=[], + multiple=True, + help="override the NPI scenario(s) run for this simulation [supports multiple NPI scenarios: `-s Wuhan -s None`]", +) +@click.option( + "-d", + "--scenarios_outcomes", + "scenarios_outcomes", + envvar="FLEPI_DEATHRATES", + type=str, + default=[], + multiple=True, + help="Scenario of outcomes to run", +) +@click.option( + "-n", + "--nslots", + envvar="FLEPI_NUM_SLOTS", + type=click.IntRange(min=1), + help="override the # of simulation runs in the config file", +) +@click.option( + "-i", + "--first_sim_index", + envvar="FIRST_SIM_INDEX", + type=click.IntRange(min=1), + default=1, + show_default=True, + help="The index of the first simulation", +) +@click.option( + "-j", + "--jobs", + envvar="FLEPI_NJOBS", + type=click.IntRange(min=1), + default=multiprocessing.cpu_count(), + show_default=True, + help="the parallelization factor", +) +@click.option( + "--stochastic/--non-stochastic", + "--stochastic/--non-stochastic", + "stoch_traj_flag", + envvar="FLEPI_STOCHASTIC_RUN", + type=bool, + default=False, + help="Flag determining whether to run stochastic simulations or not", +) +@click.option( + "--in-id", + "--in-id", + "in_run_id", + envvar="FLEPI_RUN_INDEX", + type=str, + default=file_paths.run_id(), + show_default=True, + help="Unique identifier for the run", +) # Default does not make sense here +@click.option( + "--out-id", + "--out-id", + "out_run_id", + envvar="FLEPI_RUN_INDEX", + type=str, + default=file_paths.run_id(), + show_default=True, + help="Unique identifier for the run", +) +@click.option( + "--in-prefix", + "--in-prefix", + "in_prefix", + envvar="FLEPI_PREFIX", + type=str, + default=None, + show_default=True, + help="unique identifier for the run", +) +@click.option( + "--interactive/--batch", + default=False, + help="run in interactive or batch mode [default: batch]", +) +@click.option( + "--write-csv/--no-write-csv", + default=False, + show_default=True, + help="write CSV output at end of simulation", +) +@click.option( + "--write-parquet/--no-write-parquet", + default=True, + show_default=True, + help="write parquet file output at end of simulation", +) +# @profile_options +def simulate( + config_file, + in_run_id, + out_run_id, + npi_scenarios, + scenarios_outcomes, + in_prefix, + nslots, + jobs, + interactive, + write_csv, + write_parquet, + first_sim_index, + stoch_traj_flag, +): + + spatial_path_prefix = "" + config.clear() + config.read(user=False) + config.set_file(config_file) + spatial_config = config["spatial_setup"] + spatial_base_path = config["data_path"].get() + spatial_base_path = pathlib.Path(spatial_path_prefix + spatial_base_path) + + if not npi_scenarios: + npi_scenarios = config["interventions"]["scenarios"].as_str_seq() + print(f"NPI Scenarios to be run: {', '.join(npi_scenarios)}") + + print(f"Outcomes scenarios to be run: {', '.join(scenarios_outcomes)}") + + if in_prefix is None: + in_prefix = config["name"].get() + "/" + + if not nslots: + nslots = config["nslots"].as_number() + print(f"Simulations to be run: {nslots}") + + spatial_setup = subpopulation_structure.SubpopulationStructure( + setup_name=config["setup_name"].get(), + geodata_file=spatial_base_path / spatial_config["geodata"].get(), + mobility_file=spatial_base_path / spatial_config["mobility"].get() + if spatial_config["mobility"].exists() + else None, + popnodes_key="population", + subpop_names_key="subpop", + ) + + start = time.monotonic() + for npi_scenario in npi_scenarios: + + s = setup.Setup( + setup_name=config["name"].get() + "/" + str(npi_scenario) + "/", + spatial_setup=spatial_setup, + nslots=nslots, + npi_scenario=npi_scenario, + npi_config_seir=config["interventions"]["settings"][npi_scenario], + seeding_config=config["seeding"], + initial_conditions_config=config["initial_conditions"], + parameters_config=config["seir"]["parameters"], + seir_config=config["seir"], + ti=config["start_date"].as_date(), + tf=config["end_date"].as_date(), + interactive=interactive, + write_csv=write_csv, + write_parquet=write_parquet, + first_sim_index=first_sim_index, + in_run_id=in_run_id, + in_prefix=config["name"].get() + "/", + out_run_id=out_run_id, + out_prefix=config["name"].get() + "/" + str(npi_scenario) + "/" + out_run_id + "/", + stoch_traj_flag=stoch_traj_flag, + ) + + print( + f""" +>> Scenario: {npi_scenario} from config {config_file} +>> Starting {s.nslots} model runs beginning from {s.first_sim_index} on {jobs} processes +>> Setup *** {s.setup_name} *** from {s.ti} to {s.tf} + """ + ) + seir.run_parallel_SEIR(s, config=config, n_jobs=jobs) + print(f">> All SEIR runs completed in {time.monotonic() - start:.1f} seconds") + + if config["outcomes"].exists(): + if not scenarios_outcomes: + scenarios_outcomes = config["outcomes"]["scenarios"].as_str_seq() + start = time.monotonic() + for scenario_outcomes in scenarios_outcomes: + print(f"outcome {scenario_outcomes}") + + out_prefix = config["name"].get() + "/" + str(scenario_outcomes) + "/" + + s = setup.Setup( + setup_name=config["name"].get() + "/" + str(scenarios_outcomes) + "/", + spatial_setup=spatial_setup, + nslots=nslots, + outcomes_config=config["outcomes"], + outcomes_scenario=scenario_outcomes, + ti=config["start_date"].as_date(), + tf=config["end_date"].as_date(), + write_csv=write_csv, + write_parquet=write_parquet, + first_sim_index=first_sim_index, + in_run_id=in_run_id, + in_prefix=in_prefix, + out_run_id=out_run_id, + out_prefix=out_prefix, + stoch_traj_flag=stoch_traj_flag, + ) + + outdir = file_paths.create_dir_name(out_run_id, out_prefix, "hosp") + os.makedirs(outdir, exist_ok=True) + + print( + f""" + >> Starting {nslots} model runs beginning from {first_sim_index} on {jobs} processes + >> Scenario: {scenario_outcomes} + >> writing to folder : {out_prefix} + >> running ***{'STOCHASTIC' if stoch_traj_flag else 'DETERMINISTIC'}*** trajectories""" + ) + + if config["outcomes"]["method"].get() == "delayframe": + outcomes.run_parallel_outcomes(sim_id2write=first_sim_index, s=s, nslots=nslots, n_jobs=jobs) + else: + raise ValueError(f"Only method 'delayframe' is supported at the moment.") + + print(f">> All Outcomes runs completed in {time.monotonic() - start:.1f} seconds") + else: + print("No observable found in config") + + +if __name__ == "__main__": + simulate() + +## @endcond diff --git a/flepimop/gempyor_pkg/src/gempyor/simulate_outcome.py b/flepimop/gempyor_pkg/src/gempyor/simulate_outcome.py index e0ec8b96b..41f8f4c75 100755 --- a/flepimop/gempyor_pkg/src/gempyor/simulate_outcome.py +++ b/flepimop/gempyor_pkg/src/gempyor/simulate_outcome.py @@ -13,7 +13,7 @@ # method: delayframe # Only fast is supported atm. Makes fast delay_table computations. Later agent-based method ? # paths: # param_from_file: TRUE # -# param_place_file: # OPTIONAL: File with param per csv. For each param in this file +# param_subpop_file: # OPTIONAL: File with param per csv. For each param in this file # scenarios: # Outcomes scenarios to run # - low_death_rate # - mid_death_rate @@ -38,7 +38,7 @@ # # ## Input Data # -# * {param_place_file} is a csv with columns place, parameter, value. Parameter is constructed as, e.g for comp1: +# * {param_subpop_file} is a csv with columns subpop, parameter, value. Parameter is constructed as, e.g for comp1: # probability: Pnew_comp1|source # delay: Dnew_comp1 # duration: Lnew_comp1 @@ -197,14 +197,14 @@ def simulate( nslots = config["nslots"].as_number() print(f"Simulations to be run: {nslots}") - spatial_setup = setup.SpatialSetup( + spatial_setup = subpopulation_structure.SubpopulationStructure( setup_name=config["setup_name"].get(), geodata_file=spatial_base_path / spatial_config["geodata"].get(), mobility_file=spatial_base_path / spatial_config["mobility"].get() if spatial_config["mobility"].exists() else None, - popnodes_key=spatial_config["popnodes"].get(), - nodenames_key=spatial_config["nodenames"].get(), + popnodes_key="population", + subpop_names_key="subpop", ) start = time.monotonic() diff --git a/flepimop/gempyor_pkg/src/gempyor/simulate_seir.py b/flepimop/gempyor_pkg/src/gempyor/simulate_seir.py index d4d523fc0..5995bde77 100755 --- a/flepimop/gempyor_pkg/src/gempyor/simulate_seir.py +++ b/flepimop/gempyor_pkg/src/gempyor/simulate_seir.py @@ -19,8 +19,6 @@ # spatial_setup: # geodata: # mobility: -# nodenames: -# popnodes: # # seir: # parameters @@ -36,10 +34,10 @@ # - ... # settings: # : -# template: choose one - "Reduce", ReduceR0", "Stacked" +# template: choose one - "SinglePeriodModifier", ", "StackedModifier" # ... # : -# template: choose one - "Reduce", "ReduceR0", "Stacked" +# template: choose one - "SinglePeriodModifier", "", "StackedModifier" # ... # # seeding: @@ -48,37 +46,37 @@ # # ### interventions::scenarios::settings:: # -# If {template} is ReduceR0 +# If {template} is # ```yaml # interventions: # scenarios: # : -# template: Reduce +# template: SinglePeriodModifier # parameter: choose one - "alpha, sigma, gamma, r0" # period_start_date: # period_end_date: # value: -# affected_geoids: optional +# subpop: optional # ``` # -# If {template} is ReduceR0 +# If {template} is # ```yaml # interventions: # scenarios: # : -# template: ReduceR0 +# template: # period_start_date: # period_end_date: # value: -# affected_geoids: optional +# subpop: optional # ``` # -# If {template} is Stacked +# If {template} is StackedModifier # ```yaml # interventions: # scenarios: # : -# template: Stacked +# template: StackedModifier # scenarios: # ``` # @@ -100,7 +98,7 @@ # # ## Input Data # -# * {data_path}/{spatial_setup::geodata} is a csv with columns {spatial_setup::nodenames} and {spatial_setup::popnodes} +# * {data_path}/{spatial_setup::geodata} is a csv with columns {spatial_setup::subpop_names} and {spatial_setup::popnodes} # * {data_path}/{spatial_setup::mobility} # # If {seeding::method} is PoissonDistributed @@ -251,14 +249,14 @@ def simulate( if not nslots: nslots = config["nslots"].as_number() - spatial_setup = setup.SpatialSetup( + spatial_setup = subpopulation_structure.SubpopulationStructure( setup_name=config["setup_name"].get(), geodata_file=spatial_base_path / spatial_config["geodata"].get(), mobility_file=spatial_base_path / spatial_config["mobility"].get() if spatial_config["mobility"].exists() else None, - popnodes_key=spatial_config["popnodes"].get(), - nodenames_key=spatial_config["nodenames"].get(), + popnodes_key="population", + subpop_names_key="subpop", ) start = time.monotonic() diff --git a/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py b/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py index 2789e9342..e20b4e930 100644 --- a/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py +++ b/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py @@ -226,18 +226,18 @@ def rk4_integrate(t, x, today): seeding_data["day_start_idx"][min(today + int(np.ceil(dt)), len(seeding_data["day_start_idx"]) - 1)], ): this_seeding_amounts = seeding_amounts[seeding_instance_idx] - seeding_places = seeding_data["seeding_places"][seeding_instance_idx] + seeding_subpops = seeding_data["seeding_subpops"][seeding_instance_idx] seeding_sources = seeding_data["seeding_sources"][seeding_instance_idx] seeding_destinations = seeding_data["seeding_destinations"][seeding_instance_idx] # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx] - states_next[seeding_sources][seeding_places] -= this_seeding_amounts - states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * ( - states_next[seeding_sources][seeding_places] > 0 + states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts + states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * ( + states_next[seeding_sources][seeding_subpops] > 0 ) - states_next[seeding_destinations][seeding_places] += this_seeding_amounts + states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts # ADD TO cumulative, this is debatable, - states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts + states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts x_ = np.zeros((2, ncompartments, nspatial_nodes)) x_[0] = states_next diff --git a/flepimop/gempyor_pkg/src/gempyor/steps_source.py b/flepimop/gempyor_pkg/src/gempyor/steps_source.py index 9e52cb830..b8af1d493 100644 --- a/flepimop/gempyor_pkg/src/gempyor/steps_source.py +++ b/flepimop/gempyor_pkg/src/gempyor/steps_source.py @@ -40,7 +40,7 @@ ## Initial Conditions "float64[:,:]," ## initial_conditions [ ncompartments x nspatial_nodes ] ## Seeding - "DictType(unicode_type, int64[:])," # seeding keys: 'seeding_places', 'seeding_destinations', 'seeding_sources' + "DictType(unicode_type, int64[:])," # seeding keys: 'seeding_subpops', 'seeding_destinations', 'seeding_sources' "float64[:]," # seeding_amounts ## Mobility "float64[:]," # mobility_data [ nmobility_instances ] @@ -109,20 +109,20 @@ def steps_SEIR_nb( seeding_data["day_start_idx"][today + 1], ): this_seeding_amounts = seeding_amounts[seeding_instance_idx] - seeding_places = seeding_data["seeding_places"][seeding_instance_idx] + seeding_subpops = seeding_data["seeding_subpops"][seeding_instance_idx] seeding_sources = seeding_data["seeding_sources"][seeding_instance_idx] seeding_destinations = seeding_data["seeding_destinations"][seeding_instance_idx] # this_seeding_amounts = this_seeding_amounts < states_next[seeding_sources] ? this_seeding_amounts : states_next[seeding_instance_idx] - states_next[seeding_sources][seeding_places] -= this_seeding_amounts - states_next[seeding_sources][seeding_places] = states_next[seeding_sources][seeding_places] * ( - states_next[seeding_sources][seeding_places] > 0 + states_next[seeding_sources][seeding_subpops] -= this_seeding_amounts + states_next[seeding_sources][seeding_subpops] = states_next[seeding_sources][seeding_subpops] * ( + states_next[seeding_sources][seeding_subpops] > 0 ) - states_next[seeding_destinations][seeding_places] += this_seeding_amounts + states_next[seeding_destinations][seeding_subpops] += this_seeding_amounts total_seeded += this_seeding_amounts times_seeded += 1 # ADD TO cumulative, this is debatable, - states_daily_incid[today][seeding_destinations][seeding_places] += this_seeding_amounts + states_daily_incid[today][seeding_destinations][seeding_subpops] += this_seeding_amounts total_infected = 0 for transition_index in range(ntransitions): diff --git a/flepimop/gempyor_pkg/src/gempyor/subpopulation_structure.py b/flepimop/gempyor_pkg/src/gempyor/subpopulation_structure.py new file mode 100644 index 000000000..083b6111d --- /dev/null +++ b/flepimop/gempyor_pkg/src/gempyor/subpopulation_structure.py @@ -0,0 +1,103 @@ +import pathlib +import numpy as np +import pandas as pd +import scipy.sparse +from .utils import read_df, write_df +import logging + + +logger = logging.getLogger(__name__) + + +class SubpopulationStructure: + def __init__(self, *, setup_name, geodata_file, mobility_file, popnodes_key, subpop_names_key): + self.setup_name = setup_name + self.data = pd.read_csv( + geodata_file, converters={subpop_names_key: lambda x: str(x).strip()}, skipinitialspace=True + ) # subpops and populations, strip whitespaces + self.nnodes = len(self.data) # K = # of locations + + # popnodes_key is the name of the column in geodata_file with populations + if popnodes_key not in self.data: + raise ValueError( + f"popnodes_key: {popnodes_key} does not correspond to a column in geodata: {self.data.columns}" + ) + self.popnodes = self.data[popnodes_key].to_numpy() # population + if len(np.argwhere(self.popnodes == 0)): + raise ValueError( + f"There are {len(np.argwhere(self.popnodes == 0))} nodes with population zero, this is not supported." + ) + + # subpop_names_key is the name of the column in geodata_file with subpops + if subpop_names_key not in self.data: + raise ValueError(f"subpop_names_key: {subpop_names_key} does not correspond to a column in geodata.") + self.subpop_names = self.data[subpop_names_key].tolist() + if len(self.subpop_names) != len(set(self.subpop_names)): + raise ValueError(f"There are duplicate subpop_names in geodata.") + + if mobility_file is not None: + mobility_file = pathlib.Path(mobility_file) + if mobility_file.suffix == ".txt": + print("Mobility files as matrices are not recommended. Please switch soon to long form csv files.") + self.mobility = scipy.sparse.csr_matrix( + np.loadtxt(mobility_file), dtype=int + ) # K x K matrix of people moving + # Validate mobility data + if self.mobility.shape != (self.nnodes, self.nnodes): + raise ValueError( + f"mobility data must have dimensions of length of geodata ({self.nnodes}, {self.nnodes}). Actual: {self.mobility.shape}" + ) + + elif mobility_file.suffix == ".csv": + mobility_data = pd.read_csv(mobility_file, converters={"ori": str, "dest": str}, skipinitialspace=True) + nn_dict = {v: k for k, v in enumerate(self.subpop_names)} + mobility_data["ori_idx"] = mobility_data["ori"].apply(nn_dict.__getitem__) + mobility_data["dest_idx"] = mobility_data["dest"].apply(nn_dict.__getitem__) + if any(mobility_data["ori_idx"] == mobility_data["dest_idx"]): + raise ValueError( + f"Mobility fluxes with same origin and destination in long form matrix. This is not supported" + ) + + self.mobility = scipy.sparse.coo_matrix( + (mobility_data.amount, (mobility_data.ori_idx, mobility_data.dest_idx)), + shape=(self.nnodes, self.nnodes), + dtype=int, + ).tocsr() + + elif mobility_file.suffix == ".npz": + self.mobility = scipy.sparse.load_npz(mobility_file).astype(int) + # Validate mobility data + if self.mobility.shape != (self.nnodes, self.nnodes): + raise ValueError( + f"mobility data must have dimensions of length of geodata ({self.nnodes}, {self.nnodes}). Actual: {self.mobility.shape}" + ) + else: + raise ValueError( + f"Mobility data must either be a .csv file in longform (recommended) or a .txt matrix file. Got {mobility_file}" + ) + + # Make sure mobility values <= the population of src node + tmp = (self.mobility.T - self.popnodes).T + tmp[tmp < 0] = 0 + if tmp.any(): + rows, cols, values = scipy.sparse.find(tmp) + errmsg = "" + for r, c, v in zip(rows, cols, values): + errmsg += f"\n({r}, {c}) = {self.mobility[r, c]} > population of '{self.subpop_names[r]}' = {self.popnodes[r]}" + raise ValueError( + f"The following entries in the mobility data exceed the source node populations in geodata:{errmsg}" + ) + + tmp = self.popnodes - np.squeeze(np.asarray(self.mobility.sum(axis=1))) + tmp[tmp > 0] = 0 + if tmp.any(): + (row,) = np.where(tmp) + errmsg = "" + for r in row: + errmsg += f"\n sum accross row {r} exceed population of node '{self.subpop_names[r]}' ({self.popnodes[r]}), by {-tmp[r]}" + raise ValueError( + f"The following rows in the mobility data exceed the source node populations in geodata:{errmsg}" + ) + else: + logging.critical("No mobility matrix specified -- assuming no one moves") + self.mobility = scipy.sparse.csr_matrix(np.zeros((self.nnodes, self.nnodes)), dtype=int) diff --git a/flepimop/gempyor_pkg/src/gempyor/utils.py b/flepimop/gempyor_pkg/src/gempyor/utils.py index 41e2d0bea..ecd73c080 100644 --- a/flepimop/gempyor_pkg/src/gempyor/utils.py +++ b/flepimop/gempyor_pkg/src/gempyor/utils.py @@ -36,7 +36,8 @@ def read_df(fname: str, extension: str = "") -> pd.DataFrame: fname = f"{fname}.{extension}" extension = fname.split(".")[-1] if extension == "csv": - df = pd.read_csv(fname) + # The converter prevents e.g leading geoid (0600) to be converted as int; and works when the column is absent + df = pd.read_csv(fname, converters={"subpop": lambda x: str(x)}, skipinitialspace=True) elif extension == "parquet": df = pa.parquet.read_table(fname).to_pandas() else: diff --git a/flepimop/gempyor_pkg/tests/npi/config_npi.yml b/flepimop/gempyor_pkg/tests/npi/config_npi.yml index 9f1e2b0a4..20909f1e9 100644 --- a/flepimop/gempyor_pkg/tests/npi/config_npi.yml +++ b/flepimop/gempyor_pkg/tests/npi/config_npi.yml @@ -69,8 +69,6 @@ spatial_setup: - WY geodata: geodata_2019_statelevel.csv mobility: mobility_2011-2015_statelevel.csv - popnodes: pop2019est - nodenames: geoid include_in_report: include_in_report state_level: TRUE @@ -707,9 +705,9 @@ interventions: - inference settings: local_variance: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: "all" + subpop: "all" period_start_date: 2020-01-01 period_end_date: 2022-09-03 value: @@ -725,9 +723,9 @@ interventions: a: -1 b: 1 local_variance_chi3_NEW: - template: Reduce + template: SinglePeriodModifier parameter: chi3 - affected_geoids: "all" + subpop: "all" period_start_date: 2020-01-01 period_end_date: 2022-09-03 value: @@ -743,310 +741,310 @@ interventions: a: -1 b: 1 school_year: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: ["01000"] + - subpop: ["01000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["02000"] + - subpop: ["02000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["04000"] + - subpop: ["04000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["05000"] + - subpop: ["05000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["06000"] + - subpop: ["06000"] periods: - start_date: 2021-09-30 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["08000"] + - subpop: ["08000"] periods: - start_date: 2021-09-20 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["09000"] + - subpop: ["09000"] periods: - start_date: 2021-10-04 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["10000"] + - subpop: ["10000"] periods: - start_date: 2021-09-30 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["11000"] + - subpop: ["11000"] periods: - start_date: 2021-11-01 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["12000"] + - subpop: ["12000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["13000"] + - subpop: ["13000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["15000"] + - subpop: ["15000"] periods: - start_date: 2021-11-12 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["16000"] + - subpop: ["16000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["17000"] + - subpop: ["17000"] periods: - start_date: 2021-08-30 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["18000"] + - subpop: ["18000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["19000"] + - subpop: ["19000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["20000"] + - subpop: ["20000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["21000"] + - subpop: ["21000"] periods: - start_date: 2021-08-19 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["22000"] + - subpop: ["22000"] periods: - start_date: 2021-10-27 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["23000"] + - subpop: ["23000"] periods: - start_date: 2021-10-29 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["24000"] + - subpop: ["24000"] periods: - start_date: 2021-09-14 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["25000"] + - subpop: ["25000"] periods: - start_date: 2021-08-24 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["26000"] + - subpop: ["26000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["27000"] + - subpop: ["27000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["28000"] + - subpop: ["28000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["29000"] + - subpop: ["29000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["30000"] + - subpop: ["30000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["31000"] + - subpop: ["31000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["32000"] + - subpop: ["32000"] periods: - start_date: 2021-11-01 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["33000"] + - subpop: ["33000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["34000"] + - subpop: ["34000"] periods: - start_date: 2021-11-01 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["35000"] + - subpop: ["35000"] periods: - start_date: 2021-08-20 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["36000"] + - subpop: ["36000"] periods: - start_date: 2021-11-01 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["37000"] + - subpop: ["37000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["38000"] + - subpop: ["38000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["39000"] + - subpop: ["39000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["40000"] + - subpop: ["40000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["41000"] + - subpop: ["41000"] periods: - start_date: 2021-10-18 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["42000"] + - subpop: ["42000"] periods: - start_date: 2021-09-07 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["44000"] + - subpop: ["44000"] periods: - start_date: 2021-10-01 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["45000"] + - subpop: ["45000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["46000"] + - subpop: ["46000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["47000"] + - subpop: ["47000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["48000"] + - subpop: ["48000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["49000"] + - subpop: ["49000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["50000"] + - subpop: ["50000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["51000"] + - subpop: ["51000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["53000"] + - subpop: ["53000"] periods: - start_date: 2021-10-18 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["54000"] + - subpop: ["54000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["55000"] + - subpop: ["55000"] periods: - start_date: 2021-08-19 end_date: 2021-11-23 - start_date: 2022-01-03 end_date: 2022-09-03 - - affected_geoids: ["56000"] + - subpop: ["56000"] periods: - start_date: 2021-08-16 end_date: 2021-11-23 @@ -1065,210 +1063,210 @@ interventions: a: -1 b: 1 holiday_season2021: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: ["01000"] + - subpop: ["01000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["02000"] + - subpop: ["02000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["04000"] + - subpop: ["04000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["05000"] + - subpop: ["05000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["06000"] + - subpop: ["06000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["08000"] + - subpop: ["08000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["09000"] + - subpop: ["09000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["10000"] + - subpop: ["10000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["11000"] + - subpop: ["11000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["12000"] + - subpop: ["12000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["13000"] + - subpop: ["13000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["15000"] + - subpop: ["15000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["16000"] + - subpop: ["16000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["17000"] + - subpop: ["17000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["18000"] + - subpop: ["18000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["19000"] + - subpop: ["19000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["20000"] + - subpop: ["20000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["21000"] + - subpop: ["21000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["22000"] + - subpop: ["22000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["23000"] + - subpop: ["23000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["24000"] + - subpop: ["24000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["25000"] + - subpop: ["25000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["26000"] + - subpop: ["26000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["27000"] + - subpop: ["27000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["28000"] + - subpop: ["28000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["29000"] + - subpop: ["29000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["30000"] + - subpop: ["30000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["31000"] + - subpop: ["31000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["32000"] + - subpop: ["32000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["33000"] + - subpop: ["33000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["34000"] + - subpop: ["34000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["35000"] + - subpop: ["35000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["36000"] + - subpop: ["36000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["37000"] + - subpop: ["37000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["38000"] + - subpop: ["38000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["39000"] + - subpop: ["39000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["40000"] + - subpop: ["40000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["41000"] + - subpop: ["41000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["42000"] + - subpop: ["42000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["44000"] + - subpop: ["44000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["45000"] + - subpop: ["45000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["46000"] + - subpop: ["46000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["47000"] + - subpop: ["47000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["48000"] + - subpop: ["48000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["49000"] + - subpop: ["49000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["50000"] + - subpop: ["50000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["51000"] + - subpop: ["51000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["53000"] + - subpop: ["53000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["54000"] + - subpop: ["54000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["55000"] + - subpop: ["55000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 - - affected_geoids: ["56000"] + - subpop: ["56000"] periods: - start_date: 2021-11-24 end_date: 2022-01-02 @@ -1285,9 +1283,9 @@ interventions: a: -1 b: 1 AL_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2020-04-04 period_end_date: 2020-04-30 value: @@ -1303,9 +1301,9 @@ interventions: a: -1 b: 1 AL_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2020-05-01 period_end_date: 2020-05-21 value: @@ -1321,9 +1319,9 @@ interventions: a: -1 b: 1 AL_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2020-05-22 period_end_date: 2020-07-15 value: @@ -1339,9 +1337,9 @@ interventions: a: -1 b: 1 AL_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2020-07-16 period_end_date: 2021-03-03 value: @@ -1357,9 +1355,9 @@ interventions: a: -1 b: 1 AL_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-03-04 period_end_date: 2021-04-08 value: @@ -1375,9 +1373,9 @@ interventions: a: -1 b: 1 AL_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-04-09 period_end_date: 2021-05-30 value: @@ -1393,9 +1391,9 @@ interventions: a: -1 b: 1 AL_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-05-31 period_end_date: 2021-08-15 value: @@ -1411,9 +1409,9 @@ interventions: a: -1 b: 1 AK_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2020-03-28 period_end_date: 2020-04-23 value: @@ -1429,9 +1427,9 @@ interventions: a: -1 b: 1 AK_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2020-04-24 period_end_date: 2020-05-07 value: @@ -1447,9 +1445,9 @@ interventions: a: -1 b: 1 AK_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2020-05-08 period_end_date: 2020-05-21 value: @@ -1465,9 +1463,9 @@ interventions: a: -1 b: 1 AK_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2020-05-22 period_end_date: 2020-11-15 value: @@ -1483,9 +1481,9 @@ interventions: a: -1 b: 1 AK_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2020-11-16 period_end_date: 2021-02-14 value: @@ -1501,9 +1499,9 @@ interventions: a: -1 b: 1 AK_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-02-15 period_end_date: 2021-08-15 value: @@ -1519,9 +1517,9 @@ interventions: a: -1 b: 1 AZ_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2020-03-31 period_end_date: 2020-05-15 value: @@ -1537,9 +1535,9 @@ interventions: a: -1 b: 1 AZ_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2020-05-16 period_end_date: 2020-06-28 value: @@ -1555,9 +1553,9 @@ interventions: a: -1 b: 1 AZ_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2020-06-29 period_end_date: 2020-10-01 value: @@ -1573,9 +1571,9 @@ interventions: a: -1 b: 1 AZ_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2020-10-02 period_end_date: 2020-12-02 value: @@ -1591,9 +1589,9 @@ interventions: a: -1 b: 1 AZ_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2020-12-03 period_end_date: 2021-03-04 value: @@ -1609,9 +1607,9 @@ interventions: a: -1 b: 1 AZ_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-03-05 period_end_date: 2021-03-24 value: @@ -1627,9 +1625,9 @@ interventions: a: -1 b: 1 AZ_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-03-25 period_end_date: 2021-08-15 value: @@ -1645,9 +1643,9 @@ interventions: a: -1 b: 1 AR_sdA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2020-03-20 period_end_date: 2020-05-03 value: @@ -1663,9 +1661,9 @@ interventions: a: -1 b: 1 AR_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2020-05-04 period_end_date: 2020-06-14 value: @@ -1681,9 +1679,9 @@ interventions: a: -1 b: 1 AR_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2020-06-15 period_end_date: 2020-07-19 value: @@ -1699,9 +1697,9 @@ interventions: a: -1 b: 1 AR_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2020-07-20 period_end_date: 2020-11-18 value: @@ -1717,9 +1715,9 @@ interventions: a: -1 b: 1 AR_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2020-11-19 period_end_date: 2021-01-01 value: @@ -1735,9 +1733,9 @@ interventions: a: -1 b: 1 AR_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-01-02 period_end_date: 2021-02-25 value: @@ -1753,9 +1751,9 @@ interventions: a: -1 b: 1 AR_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-02-26 period_end_date: 2021-03-30 value: @@ -1771,9 +1769,9 @@ interventions: a: -1 b: 1 AR_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-03-31 period_end_date: 2021-08-15 value: @@ -1789,9 +1787,9 @@ interventions: a: -1 b: 1 CA_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2020-03-19 period_end_date: 2020-05-07 value: @@ -1807,9 +1805,9 @@ interventions: a: -1 b: 1 CA_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2020-05-08 period_end_date: 2020-06-11 value: @@ -1825,9 +1823,9 @@ interventions: a: -1 b: 1 CA_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2020-06-12 period_end_date: 2020-07-05 value: @@ -1843,9 +1841,9 @@ interventions: a: -1 b: 1 CA_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2020-07-06 period_end_date: 2020-11-20 value: @@ -1861,9 +1859,9 @@ interventions: a: -1 b: 1 CA_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2020-11-21 period_end_date: 2020-12-05 value: @@ -1879,9 +1877,9 @@ interventions: a: -1 b: 1 CA_lockdownB: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2020-12-06 period_end_date: 2021-01-11 value: @@ -1897,9 +1895,9 @@ interventions: a: -1 b: 1 CA_lockdownC: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-01-12 period_end_date: 2021-01-24 value: @@ -1915,9 +1913,9 @@ interventions: a: -1 b: 1 CA_open_p1C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-01-25 period_end_date: 2021-02-26 value: @@ -1933,9 +1931,9 @@ interventions: a: -1 b: 1 CA_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-02-27 period_end_date: 2021-04-06 value: @@ -1951,9 +1949,9 @@ interventions: a: -1 b: 1 CA_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-04-07 period_end_date: 2021-06-14 value: @@ -1969,9 +1967,9 @@ interventions: a: -1 b: 1 CA_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-06-15 period_end_date: 2021-08-02 value: @@ -1987,9 +1985,9 @@ interventions: a: -1 b: 1 CA_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-08-03 period_end_date: 2021-09-19 value: @@ -2005,9 +2003,9 @@ interventions: a: -1 b: 1 CA_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-09-20 period_end_date: 2021-09-29 value: @@ -2023,9 +2021,9 @@ interventions: a: -1 b: 1 CO_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2020-03-26 period_end_date: 2020-04-26 value: @@ -2041,9 +2039,9 @@ interventions: a: -1 b: 1 CO_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2020-04-27 period_end_date: 2020-06-30 value: @@ -2059,9 +2057,9 @@ interventions: a: -1 b: 1 CO_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2020-07-01 period_end_date: 2020-09-28 value: @@ -2077,9 +2075,9 @@ interventions: a: -1 b: 1 CO_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2020-09-29 period_end_date: 2020-11-04 value: @@ -2095,9 +2093,9 @@ interventions: a: -1 b: 1 CO_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2020-11-05 period_end_date: 2020-11-19 value: @@ -2113,9 +2111,9 @@ interventions: a: -1 b: 1 CO_lockdownB: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2020-11-20 period_end_date: 2021-01-03 value: @@ -2131,9 +2129,9 @@ interventions: a: -1 b: 1 CO_open_p1C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-01-04 period_end_date: 2021-02-05 value: @@ -2149,9 +2147,9 @@ interventions: a: -1 b: 1 CO_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-02-06 period_end_date: 2021-03-14 value: @@ -2167,9 +2165,9 @@ interventions: a: -1 b: 1 CO_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-03-15 period_end_date: 2021-03-23 value: @@ -2185,9 +2183,9 @@ interventions: a: -1 b: 1 CO_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-03-24 period_end_date: 2021-04-15 value: @@ -2203,9 +2201,9 @@ interventions: a: -1 b: 1 CO_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-04-16 period_end_date: 2021-05-13 value: @@ -2221,9 +2219,9 @@ interventions: a: -1 b: 1 CO_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-05-14 period_end_date: 2021-05-31 value: @@ -2239,9 +2237,9 @@ interventions: a: -1 b: 1 CO_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-06-01 period_end_date: 2021-09-19 value: @@ -2257,9 +2255,9 @@ interventions: a: -1 b: 1 CT_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2020-03-23 period_end_date: 2020-05-20 value: @@ -2275,9 +2273,9 @@ interventions: a: -1 b: 1 CT_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2020-05-21 period_end_date: 2020-06-16 value: @@ -2293,9 +2291,9 @@ interventions: a: -1 b: 1 CT_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2020-06-17 period_end_date: 2020-10-07 value: @@ -2311,9 +2309,9 @@ interventions: a: -1 b: 1 CT_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2020-10-08 period_end_date: 2020-11-05 value: @@ -2329,9 +2327,9 @@ interventions: a: -1 b: 1 CT_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2020-11-06 period_end_date: 2021-01-18 value: @@ -2347,9 +2345,9 @@ interventions: a: -1 b: 1 CT_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-01-19 period_end_date: 2021-03-18 value: @@ -2365,9 +2363,9 @@ interventions: a: -1 b: 1 CT_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-03-19 period_end_date: 2021-04-01 value: @@ -2383,9 +2381,9 @@ interventions: a: -1 b: 1 CT_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-04-02 period_end_date: 2021-04-30 value: @@ -2401,9 +2399,9 @@ interventions: a: -1 b: 1 CT_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-05-01 period_end_date: 2021-05-18 value: @@ -2419,9 +2417,9 @@ interventions: a: -1 b: 1 CT_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-05-19 period_end_date: 2021-08-04 value: @@ -2437,9 +2435,9 @@ interventions: a: -1 b: 1 CT_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-08-05 period_end_date: 2021-10-03 value: @@ -2455,9 +2453,9 @@ interventions: a: -1 b: 1 DE_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2020-03-24 period_end_date: 2020-05-31 value: @@ -2473,9 +2471,9 @@ interventions: a: -1 b: 1 DE_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2020-06-01 period_end_date: 2020-06-14 value: @@ -2491,9 +2489,9 @@ interventions: a: -1 b: 1 DE_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2020-06-15 period_end_date: 2020-11-22 value: @@ -2509,9 +2507,9 @@ interventions: a: -1 b: 1 DE_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2020-11-23 period_end_date: 2020-12-13 value: @@ -2527,9 +2525,9 @@ interventions: a: -1 b: 1 DE_open_p1C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2020-12-14 period_end_date: 2021-01-07 value: @@ -2545,9 +2543,9 @@ interventions: a: -1 b: 1 DE_open_p1D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-01-08 period_end_date: 2021-02-11 value: @@ -2563,9 +2561,9 @@ interventions: a: -1 b: 1 DE_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-02-12 period_end_date: 2021-02-18 value: @@ -2581,9 +2579,9 @@ interventions: a: -1 b: 1 DE_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-02-19 period_end_date: 2021-03-31 value: @@ -2599,9 +2597,9 @@ interventions: a: -1 b: 1 DE_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-04-01 period_end_date: 2021-05-20 value: @@ -2617,9 +2615,9 @@ interventions: a: -1 b: 1 DE_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-05-21 period_end_date: 2021-08-15 value: @@ -2635,9 +2633,9 @@ interventions: a: -1 b: 1 DE_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-08-16 period_end_date: 2021-09-29 value: @@ -2653,9 +2651,9 @@ interventions: a: -1 b: 1 DC_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2020-04-01 period_end_date: 2020-05-29 value: @@ -2671,9 +2669,9 @@ interventions: a: -1 b: 1 DC_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2020-05-30 period_end_date: 2020-06-21 value: @@ -2689,9 +2687,9 @@ interventions: a: -1 b: 1 DC_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2020-06-22 period_end_date: 2020-11-24 value: @@ -2707,9 +2705,9 @@ interventions: a: -1 b: 1 DC_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2020-11-25 period_end_date: 2020-12-13 value: @@ -2725,9 +2723,9 @@ interventions: a: -1 b: 1 DC_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2020-12-14 period_end_date: 2020-12-22 value: @@ -2743,9 +2741,9 @@ interventions: a: -1 b: 1 DC_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2020-12-23 period_end_date: 2021-01-21 value: @@ -2761,9 +2759,9 @@ interventions: a: -1 b: 1 DC_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-01-22 period_end_date: 2021-03-21 value: @@ -2779,9 +2777,9 @@ interventions: a: -1 b: 1 DC_open_p2E: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-03-22 period_end_date: 2021-04-30 value: @@ -2797,9 +2795,9 @@ interventions: a: -1 b: 1 DC_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-05-01 period_end_date: 2021-05-16 value: @@ -2815,9 +2813,9 @@ interventions: a: -1 b: 1 DC_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-05-17 period_end_date: 2021-05-20 value: @@ -2833,9 +2831,9 @@ interventions: a: -1 b: 1 DC_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-05-21 period_end_date: 2021-06-10 value: @@ -2851,9 +2849,9 @@ interventions: a: -1 b: 1 DC_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-06-11 period_end_date: 2021-07-30 value: @@ -2869,9 +2867,9 @@ interventions: a: -1 b: 1 DC_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-07-31 period_end_date: 2021-09-29 value: @@ -2887,9 +2885,9 @@ interventions: a: -1 b: 1 DC_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-09-30 period_end_date: 2021-10-31 value: @@ -2905,9 +2903,9 @@ interventions: a: -1 b: 1 FL_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2020-04-03 period_end_date: 2020-05-04 value: @@ -2923,9 +2921,9 @@ interventions: a: -1 b: 1 FL_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2020-05-05 period_end_date: 2020-05-17 value: @@ -2941,9 +2939,9 @@ interventions: a: -1 b: 1 FL_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2020-05-18 period_end_date: 2020-06-04 value: @@ -2959,9 +2957,9 @@ interventions: a: -1 b: 1 FL_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2020-06-05 period_end_date: 2020-06-25 value: @@ -2977,9 +2975,9 @@ interventions: a: -1 b: 1 FL_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2020-06-26 period_end_date: 2020-09-13 value: @@ -2995,9 +2993,9 @@ interventions: a: -1 b: 1 FL_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2020-09-14 period_end_date: 2020-09-24 value: @@ -3013,9 +3011,9 @@ interventions: a: -1 b: 1 FL_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2020-09-25 period_end_date: 2021-05-02 value: @@ -3031,9 +3029,9 @@ interventions: a: -1 b: 1 FL_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2021-05-03 period_end_date: 2021-08-15 value: @@ -3049,9 +3047,9 @@ interventions: a: -1 b: 1 GA_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2020-04-03 period_end_date: 2020-04-27 value: @@ -3067,9 +3065,9 @@ interventions: a: -1 b: 1 GA_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2020-04-28 period_end_date: 2020-05-31 value: @@ -3085,9 +3083,9 @@ interventions: a: -1 b: 1 GA_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2020-06-01 period_end_date: 2020-06-30 value: @@ -3103,9 +3101,9 @@ interventions: a: -1 b: 1 GA_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2020-07-01 period_end_date: 2020-09-10 value: @@ -3121,9 +3119,9 @@ interventions: a: -1 b: 1 GA_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2020-09-11 period_end_date: 2020-12-14 value: @@ -3139,9 +3137,9 @@ interventions: a: -1 b: 1 GA_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2020-12-15 period_end_date: 2021-04-07 value: @@ -3157,9 +3155,9 @@ interventions: a: -1 b: 1 GA_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-04-08 period_end_date: 2021-04-30 value: @@ -3175,9 +3173,9 @@ interventions: a: -1 b: 1 GA_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-05-01 period_end_date: 2021-05-30 value: @@ -3193,9 +3191,9 @@ interventions: a: -1 b: 1 GA_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-05-31 period_end_date: 2021-08-15 value: @@ -3211,9 +3209,9 @@ interventions: a: -1 b: 1 HI_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2020-03-25 period_end_date: 2020-05-06 value: @@ -3229,9 +3227,9 @@ interventions: a: -1 b: 1 HI_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2020-05-07 period_end_date: 2020-05-31 value: @@ -3247,9 +3245,9 @@ interventions: a: -1 b: 1 HI_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2020-06-01 period_end_date: 2020-08-07 value: @@ -3265,9 +3263,9 @@ interventions: a: -1 b: 1 HI_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2020-08-08 period_end_date: 2020-09-23 value: @@ -3283,9 +3281,9 @@ interventions: a: -1 b: 1 HI_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2020-09-24 period_end_date: 2020-10-26 value: @@ -3301,9 +3299,9 @@ interventions: a: -1 b: 1 HI_open_p1C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2020-10-27 period_end_date: 2020-11-10 value: @@ -3319,9 +3317,9 @@ interventions: a: -1 b: 1 HI_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2020-11-11 period_end_date: 2021-01-18 value: @@ -3337,9 +3335,9 @@ interventions: a: -1 b: 1 HI_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-01-19 period_end_date: 2021-02-24 value: @@ -3355,9 +3353,9 @@ interventions: a: -1 b: 1 HI_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-02-25 period_end_date: 2021-03-10 value: @@ -3373,9 +3371,9 @@ interventions: a: -1 b: 1 HI_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-03-11 period_end_date: 2021-05-09 value: @@ -3391,9 +3389,9 @@ interventions: a: -1 b: 1 HI_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-05-10 period_end_date: 2021-05-24 value: @@ -3409,9 +3407,9 @@ interventions: a: -1 b: 1 HI_open_p3D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-05-25 period_end_date: 2021-06-10 value: @@ -3427,9 +3425,9 @@ interventions: a: -1 b: 1 HI_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-06-11 period_end_date: 2021-07-07 value: @@ -3445,9 +3443,9 @@ interventions: a: -1 b: 1 HI_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-07-08 period_end_date: 2021-08-10 value: @@ -3463,9 +3461,9 @@ interventions: a: -1 b: 1 HI_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-08-11 period_end_date: 2021-09-14 value: @@ -3481,9 +3479,9 @@ interventions: a: -1 b: 1 HI_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-09-15 period_end_date: 2021-11-07 value: @@ -3499,9 +3497,9 @@ interventions: a: -1 b: 1 HI_open_p6B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-11-08 period_end_date: 2021-11-11 value: @@ -3517,9 +3515,9 @@ interventions: a: -1 b: 1 ID_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2020-03-25 period_end_date: 2020-04-30 value: @@ -3535,9 +3533,9 @@ interventions: a: -1 b: 1 ID_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2020-05-01 period_end_date: 2020-05-15 value: @@ -3553,9 +3551,9 @@ interventions: a: -1 b: 1 ID_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2020-05-16 period_end_date: 2020-05-29 value: @@ -3571,9 +3569,9 @@ interventions: a: -1 b: 1 ID_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2020-05-30 period_end_date: 2020-06-12 value: @@ -3589,9 +3587,9 @@ interventions: a: -1 b: 1 ID_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2020-06-13 period_end_date: 2020-10-26 value: @@ -3607,9 +3605,9 @@ interventions: a: -1 b: 1 ID_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2020-10-27 period_end_date: 2020-11-12 value: @@ -3625,9 +3623,9 @@ interventions: a: -1 b: 1 ID_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2020-11-13 period_end_date: 2020-12-29 value: @@ -3643,9 +3641,9 @@ interventions: a: -1 b: 1 ID_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2020-12-30 period_end_date: 2021-02-01 value: @@ -3661,9 +3659,9 @@ interventions: a: -1 b: 1 ID_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2021-02-02 period_end_date: 2021-05-10 value: @@ -3679,9 +3677,9 @@ interventions: a: -1 b: 1 ID_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2021-05-11 period_end_date: 2021-08-15 value: @@ -3697,9 +3695,9 @@ interventions: a: -1 b: 1 IL_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2020-03-21 period_end_date: 2020-05-29 value: @@ -3715,9 +3713,9 @@ interventions: a: -1 b: 1 IL_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2020-05-30 period_end_date: 2020-06-25 value: @@ -3733,9 +3731,9 @@ interventions: a: -1 b: 1 IL_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2020-06-26 period_end_date: 2020-07-23 value: @@ -3751,9 +3749,9 @@ interventions: a: -1 b: 1 IL_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2020-07-24 period_end_date: 2020-09-30 value: @@ -3769,9 +3767,9 @@ interventions: a: -1 b: 1 IL_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2020-10-01 period_end_date: 2020-10-29 value: @@ -3787,9 +3785,9 @@ interventions: a: -1 b: 1 IL_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2020-10-30 period_end_date: 2020-11-19 value: @@ -3805,9 +3803,9 @@ interventions: a: -1 b: 1 IL_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2020-11-20 period_end_date: 2021-01-17 value: @@ -3823,9 +3821,9 @@ interventions: a: -1 b: 1 IL_open_p3D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-01-18 period_end_date: 2021-01-31 value: @@ -3841,9 +3839,9 @@ interventions: a: -1 b: 1 IL_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-02-01 period_end_date: 2021-05-16 value: @@ -3859,9 +3857,9 @@ interventions: a: -1 b: 1 IL_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-05-17 period_end_date: 2021-06-10 value: @@ -3877,9 +3875,9 @@ interventions: a: -1 b: 1 IL_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-06-11 period_end_date: 2021-07-26 value: @@ -3895,9 +3893,9 @@ interventions: a: -1 b: 1 IL_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-07-27 period_end_date: 2021-08-03 value: @@ -3913,9 +3911,9 @@ interventions: a: -1 b: 1 IL_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-08-04 period_end_date: 2021-08-29 value: @@ -3931,9 +3929,9 @@ interventions: a: -1 b: 1 IN_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2020-03-24 period_end_date: 2020-05-03 value: @@ -3949,9 +3947,9 @@ interventions: a: -1 b: 1 IN_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2020-05-04 period_end_date: 2020-05-21 value: @@ -3967,9 +3965,9 @@ interventions: a: -1 b: 1 IN_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2020-05-22 period_end_date: 2020-06-11 value: @@ -3985,9 +3983,9 @@ interventions: a: -1 b: 1 IN_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2020-06-12 period_end_date: 2020-07-03 value: @@ -4003,9 +4001,9 @@ interventions: a: -1 b: 1 IN_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2020-07-04 period_end_date: 2020-09-25 value: @@ -4021,9 +4019,9 @@ interventions: a: -1 b: 1 IN_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2020-09-26 period_end_date: 2020-11-10 value: @@ -4039,9 +4037,9 @@ interventions: a: -1 b: 1 IN_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2020-11-11 period_end_date: 2021-01-10 value: @@ -4057,9 +4055,9 @@ interventions: a: -1 b: 1 IN_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-01-11 period_end_date: 2021-01-31 value: @@ -4075,9 +4073,9 @@ interventions: a: -1 b: 1 IN_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-02-01 period_end_date: 2021-02-14 value: @@ -4093,9 +4091,9 @@ interventions: a: -1 b: 1 IN_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-02-15 period_end_date: 2021-03-01 value: @@ -4111,9 +4109,9 @@ interventions: a: -1 b: 1 IN_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-03-02 period_end_date: 2021-04-05 value: @@ -4129,9 +4127,9 @@ interventions: a: -1 b: 1 IN_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-04-06 period_end_date: 2021-06-30 value: @@ -4147,9 +4145,9 @@ interventions: a: -1 b: 1 IN_open_p5C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-07-01 period_end_date: 2021-08-15 value: @@ -4165,9 +4163,9 @@ interventions: a: -1 b: 1 IA_sdA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2020-04-02 period_end_date: 2020-05-14 value: @@ -4183,9 +4181,9 @@ interventions: a: -1 b: 1 IA_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2020-05-15 period_end_date: 2020-05-27 value: @@ -4201,9 +4199,9 @@ interventions: a: -1 b: 1 IA_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2020-05-28 period_end_date: 2020-06-11 value: @@ -4219,9 +4217,9 @@ interventions: a: -1 b: 1 IA_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2020-06-12 period_end_date: 2020-08-26 value: @@ -4237,9 +4235,9 @@ interventions: a: -1 b: 1 IA_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2020-08-27 period_end_date: 2020-10-03 value: @@ -4255,9 +4253,9 @@ interventions: a: -1 b: 1 IA_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2020-10-04 period_end_date: 2020-11-10 value: @@ -4273,9 +4271,9 @@ interventions: a: -1 b: 1 IA_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2020-11-11 period_end_date: 2020-12-16 value: @@ -4291,9 +4289,9 @@ interventions: a: -1 b: 1 IA_open_p3D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2020-12-17 period_end_date: 2021-01-07 value: @@ -4309,9 +4307,9 @@ interventions: a: -1 b: 1 IA_open_p3E: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2021-01-08 period_end_date: 2021-02-06 value: @@ -4327,9 +4325,9 @@ interventions: a: -1 b: 1 IA_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2021-02-07 period_end_date: 2021-08-15 value: @@ -4345,9 +4343,9 @@ interventions: a: -1 b: 1 KS_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2020-03-30 period_end_date: 2020-05-04 value: @@ -4363,9 +4361,9 @@ interventions: a: -1 b: 1 KS_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2020-05-05 period_end_date: 2020-05-21 value: @@ -4381,9 +4379,9 @@ interventions: a: -1 b: 1 KS_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2020-05-22 period_end_date: 2020-06-07 value: @@ -4399,9 +4397,9 @@ interventions: a: -1 b: 1 KS_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2020-06-08 period_end_date: 2020-07-02 value: @@ -4417,9 +4415,9 @@ interventions: a: -1 b: 1 KS_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2020-07-03 period_end_date: 2021-03-30 value: @@ -4435,9 +4433,9 @@ interventions: a: -1 b: 1 KS_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-03-31 period_end_date: 2021-04-05 value: @@ -4453,9 +4451,9 @@ interventions: a: -1 b: 1 KS_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-04-06 period_end_date: 2021-05-13 value: @@ -4471,9 +4469,9 @@ interventions: a: -1 b: 1 KS_open_p4C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-05-14 period_end_date: 2021-08-15 value: @@ -4489,9 +4487,9 @@ interventions: a: -1 b: 1 KY_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2020-03-26 period_end_date: 2020-05-10 value: @@ -4507,9 +4505,9 @@ interventions: a: -1 b: 1 KY_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2020-05-11 period_end_date: 2020-05-21 value: @@ -4525,9 +4523,9 @@ interventions: a: -1 b: 1 KY_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2020-05-22 period_end_date: 2020-06-28 value: @@ -4543,9 +4541,9 @@ interventions: a: -1 b: 1 KY_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2020-06-29 period_end_date: 2020-07-27 value: @@ -4561,9 +4559,9 @@ interventions: a: -1 b: 1 KY_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2020-07-28 period_end_date: 2020-08-10 value: @@ -4579,9 +4577,9 @@ interventions: a: -1 b: 1 KY_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2020-08-11 period_end_date: 2020-11-19 value: @@ -4597,9 +4595,9 @@ interventions: a: -1 b: 1 KY_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2020-11-20 period_end_date: 2020-12-13 value: @@ -4615,9 +4613,9 @@ interventions: a: -1 b: 1 KY_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2020-12-14 period_end_date: 2021-03-04 value: @@ -4633,9 +4631,9 @@ interventions: a: -1 b: 1 KY_open_p3D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-03-05 period_end_date: 2021-05-15 value: @@ -4651,9 +4649,9 @@ interventions: a: -1 b: 1 KY_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-05-16 period_end_date: 2021-05-27 value: @@ -4669,9 +4667,9 @@ interventions: a: -1 b: 1 KY_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-05-28 period_end_date: 2021-06-10 value: @@ -4687,9 +4685,9 @@ interventions: a: -1 b: 1 KY_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-06-11 period_end_date: 2021-07-28 value: @@ -4705,9 +4703,9 @@ interventions: a: -1 b: 1 KY_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-07-29 period_end_date: 2021-08-09 value: @@ -4723,9 +4721,9 @@ interventions: a: -1 b: 1 KY_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-08-10 period_end_date: 2021-08-18 value: @@ -4741,9 +4739,9 @@ interventions: a: -1 b: 1 LA_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2020-03-23 period_end_date: 2020-05-14 value: @@ -4759,9 +4757,9 @@ interventions: a: -1 b: 1 LA_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2020-05-15 period_end_date: 2020-06-04 value: @@ -4777,9 +4775,9 @@ interventions: a: -1 b: 1 LA_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2020-06-05 period_end_date: 2020-07-12 value: @@ -4795,9 +4793,9 @@ interventions: a: -1 b: 1 LA_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2020-07-13 period_end_date: 2020-09-10 value: @@ -4813,9 +4811,9 @@ interventions: a: -1 b: 1 LA_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2020-09-11 period_end_date: 2020-11-24 value: @@ -4831,9 +4829,9 @@ interventions: a: -1 b: 1 LA_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2020-11-25 period_end_date: 2021-03-02 value: @@ -4849,9 +4847,9 @@ interventions: a: -1 b: 1 LA_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-03-03 period_end_date: 2021-03-10 value: @@ -4867,9 +4865,9 @@ interventions: a: -1 b: 1 LA_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-03-11 period_end_date: 2021-03-30 value: @@ -4885,9 +4883,9 @@ interventions: a: -1 b: 1 LA_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-03-31 period_end_date: 2021-04-27 value: @@ -4903,9 +4901,9 @@ interventions: a: -1 b: 1 LA_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-04-28 period_end_date: 2021-05-25 value: @@ -4921,9 +4919,9 @@ interventions: a: -1 b: 1 LA_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-05-26 period_end_date: 2021-08-03 value: @@ -4939,9 +4937,9 @@ interventions: a: -1 b: 1 LA_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-08-04 period_end_date: 2021-10-26 value: @@ -4957,9 +4955,9 @@ interventions: a: -1 b: 1 ME_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2020-04-02 period_end_date: 2020-04-30 value: @@ -4975,9 +4973,9 @@ interventions: a: -1 b: 1 ME_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2020-05-01 period_end_date: 2020-05-31 value: @@ -4993,9 +4991,9 @@ interventions: a: -1 b: 1 ME_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2020-06-01 period_end_date: 2020-06-30 value: @@ -5011,9 +5009,9 @@ interventions: a: -1 b: 1 ME_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2020-07-01 period_end_date: 2020-10-12 value: @@ -5029,9 +5027,9 @@ interventions: a: -1 b: 1 ME_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2020-10-13 period_end_date: 2020-11-19 value: @@ -5047,9 +5045,9 @@ interventions: a: -1 b: 1 ME_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2020-11-20 period_end_date: 2021-01-31 value: @@ -5065,9 +5063,9 @@ interventions: a: -1 b: 1 ME_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-02-01 period_end_date: 2021-02-11 value: @@ -5083,9 +5081,9 @@ interventions: a: -1 b: 1 ME_open_p4C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-02-12 period_end_date: 2021-03-25 value: @@ -5101,9 +5099,9 @@ interventions: a: -1 b: 1 ME_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-03-26 period_end_date: 2021-05-23 value: @@ -5119,9 +5117,9 @@ interventions: a: -1 b: 1 ME_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-05-24 period_end_date: 2021-10-28 value: @@ -5137,9 +5135,9 @@ interventions: a: -1 b: 1 MD_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2020-03-30 period_end_date: 2020-05-14 value: @@ -5155,9 +5153,9 @@ interventions: a: -1 b: 1 MD_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2020-05-15 period_end_date: 2020-06-04 value: @@ -5173,9 +5171,9 @@ interventions: a: -1 b: 1 MD_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2020-06-05 period_end_date: 2020-09-03 value: @@ -5191,9 +5189,9 @@ interventions: a: -1 b: 1 MD_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2020-09-04 period_end_date: 2020-11-10 value: @@ -5209,9 +5207,9 @@ interventions: a: -1 b: 1 MD_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2020-11-11 period_end_date: 2020-12-16 value: @@ -5227,9 +5225,9 @@ interventions: a: -1 b: 1 MD_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2020-12-17 period_end_date: 2021-01-31 value: @@ -5245,9 +5243,9 @@ interventions: a: -1 b: 1 MD_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-02-01 period_end_date: 2021-03-11 value: @@ -5263,9 +5261,9 @@ interventions: a: -1 b: 1 MD_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-03-12 period_end_date: 2021-05-14 value: @@ -5281,9 +5279,9 @@ interventions: a: -1 b: 1 MD_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-05-15 period_end_date: 2021-06-30 value: @@ -5299,9 +5297,9 @@ interventions: a: -1 b: 1 MD_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-07-01 period_end_date: 2021-07-26 value: @@ -5317,9 +5315,9 @@ interventions: a: -1 b: 1 MD_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-07-27 period_end_date: 2021-08-31 value: @@ -5335,9 +5333,9 @@ interventions: a: -1 b: 1 MD_open_p8A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-09-01 period_end_date: 2021-09-13 value: @@ -5353,9 +5351,9 @@ interventions: a: -1 b: 1 MA_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2020-03-24 period_end_date: 2020-05-18 value: @@ -5371,9 +5369,9 @@ interventions: a: -1 b: 1 MA_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2020-05-19 period_end_date: 2020-06-07 value: @@ -5389,9 +5387,9 @@ interventions: a: -1 b: 1 MA_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2020-06-08 period_end_date: 2020-07-05 value: @@ -5407,9 +5405,9 @@ interventions: a: -1 b: 1 MA_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2020-07-06 period_end_date: 2020-10-04 value: @@ -5425,9 +5423,9 @@ interventions: a: -1 b: 1 MA_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2020-10-05 period_end_date: 2020-10-22 value: @@ -5443,9 +5441,9 @@ interventions: a: -1 b: 1 MA_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2020-10-23 period_end_date: 2020-12-12 value: @@ -5461,9 +5459,9 @@ interventions: a: -1 b: 1 MA_open_p3D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2020-12-13 period_end_date: 2020-12-25 value: @@ -5479,9 +5477,9 @@ interventions: a: -1 b: 1 MA_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2020-12-26 period_end_date: 2021-01-24 value: @@ -5497,9 +5495,9 @@ interventions: a: -1 b: 1 MA_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-01-25 period_end_date: 2021-02-07 value: @@ -5515,9 +5513,9 @@ interventions: a: -1 b: 1 MA_open_p3E: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-02-08 period_end_date: 2021-02-28 value: @@ -5533,9 +5531,9 @@ interventions: a: -1 b: 1 MA_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-03-01 period_end_date: 2021-03-21 value: @@ -5551,9 +5549,9 @@ interventions: a: -1 b: 1 MA_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-03-22 period_end_date: 2021-04-29 value: @@ -5569,9 +5567,9 @@ interventions: a: -1 b: 1 MA_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-04-30 period_end_date: 2021-05-28 value: @@ -5587,9 +5585,9 @@ interventions: a: -1 b: 1 MA_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-05-29 period_end_date: 2021-08-23 value: @@ -5605,9 +5603,9 @@ interventions: a: -1 b: 1 MI_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2020-03-24 period_end_date: 2020-05-31 value: @@ -5623,9 +5621,9 @@ interventions: a: -1 b: 1 MI_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2020-06-01 period_end_date: 2020-06-30 value: @@ -5641,9 +5639,9 @@ interventions: a: -1 b: 1 MI_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2020-07-01 period_end_date: 2020-09-08 value: @@ -5659,9 +5657,9 @@ interventions: a: -1 b: 1 MI_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2020-09-09 period_end_date: 2020-10-08 value: @@ -5677,9 +5675,9 @@ interventions: a: -1 b: 1 MI_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2020-10-09 period_end_date: 2020-11-17 value: @@ -5695,9 +5693,9 @@ interventions: a: -1 b: 1 MI_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2020-11-18 period_end_date: 2020-12-20 value: @@ -5713,9 +5711,9 @@ interventions: a: -1 b: 1 MI_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2020-12-21 period_end_date: 2021-01-15 value: @@ -5731,9 +5729,9 @@ interventions: a: -1 b: 1 MI_open_p2E: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-01-16 period_end_date: 2021-01-31 value: @@ -5749,9 +5747,9 @@ interventions: a: -1 b: 1 MI_open_p2F: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-02-01 period_end_date: 2021-03-04 value: @@ -5767,9 +5765,9 @@ interventions: a: -1 b: 1 MI_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-03-05 period_end_date: 2021-03-21 value: @@ -5785,9 +5783,9 @@ interventions: a: -1 b: 1 MI_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-03-22 period_end_date: 2021-05-14 value: @@ -5803,9 +5801,9 @@ interventions: a: -1 b: 1 MI_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-05-15 period_end_date: 2021-05-31 value: @@ -5821,9 +5819,9 @@ interventions: a: -1 b: 1 MI_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-06-01 period_end_date: 2021-06-21 value: @@ -5839,9 +5837,9 @@ interventions: a: -1 b: 1 MI_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-06-22 period_end_date: 2021-08-15 value: @@ -5857,9 +5855,9 @@ interventions: a: -1 b: 1 MN_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2020-03-27 period_end_date: 2020-05-17 value: @@ -5875,9 +5873,9 @@ interventions: a: -1 b: 1 MN_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2020-05-18 period_end_date: 2020-05-31 value: @@ -5893,9 +5891,9 @@ interventions: a: -1 b: 1 MN_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2020-06-01 period_end_date: 2020-06-09 value: @@ -5911,9 +5909,9 @@ interventions: a: -1 b: 1 MN_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2020-06-10 period_end_date: 2020-07-24 value: @@ -5929,9 +5927,9 @@ interventions: a: -1 b: 1 MN_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2020-07-25 period_end_date: 2020-11-12 value: @@ -5947,9 +5945,9 @@ interventions: a: -1 b: 1 MN_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2020-11-13 period_end_date: 2020-12-17 value: @@ -5965,9 +5963,9 @@ interventions: a: -1 b: 1 MN_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2020-12-18 period_end_date: 2021-01-10 value: @@ -5983,9 +5981,9 @@ interventions: a: -1 b: 1 MN_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-01-11 period_end_date: 2021-02-12 value: @@ -6001,9 +5999,9 @@ interventions: a: -1 b: 1 MN_open_p3D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-02-13 period_end_date: 2021-03-14 value: @@ -6019,9 +6017,9 @@ interventions: a: -1 b: 1 MN_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-03-15 period_end_date: 2021-03-31 value: @@ -6037,9 +6035,9 @@ interventions: a: -1 b: 1 MN_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-04-01 period_end_date: 2021-05-06 value: @@ -6055,9 +6053,9 @@ interventions: a: -1 b: 1 MN_open_p4C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-05-07 period_end_date: 2021-05-13 value: @@ -6073,9 +6071,9 @@ interventions: a: -1 b: 1 MN_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-05-14 period_end_date: 2021-05-27 value: @@ -6091,9 +6089,9 @@ interventions: a: -1 b: 1 MN_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-05-28 period_end_date: 2021-08-15 value: @@ -6109,9 +6107,9 @@ interventions: a: -1 b: 1 MS_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2020-04-03 period_end_date: 2020-04-27 value: @@ -6127,9 +6125,9 @@ interventions: a: -1 b: 1 MS_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2020-04-28 period_end_date: 2020-05-06 value: @@ -6145,9 +6143,9 @@ interventions: a: -1 b: 1 MS_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2020-05-07 period_end_date: 2020-05-31 value: @@ -6163,9 +6161,9 @@ interventions: a: -1 b: 1 MS_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2020-06-01 period_end_date: 2020-09-13 value: @@ -6181,9 +6179,9 @@ interventions: a: -1 b: 1 MS_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2020-09-14 period_end_date: 2020-11-24 value: @@ -6199,9 +6197,9 @@ interventions: a: -1 b: 1 MS_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2020-11-25 period_end_date: 2020-12-10 value: @@ -6217,9 +6215,9 @@ interventions: a: -1 b: 1 MS_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2020-12-11 period_end_date: 2021-03-02 value: @@ -6235,9 +6233,9 @@ interventions: a: -1 b: 1 MS_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-03-03 period_end_date: 2021-03-30 value: @@ -6253,9 +6251,9 @@ interventions: a: -1 b: 1 MS_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-03-31 period_end_date: 2021-04-29 value: @@ -6271,9 +6269,9 @@ interventions: a: -1 b: 1 MS_open_p5C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-04-30 period_end_date: 2021-08-15 value: @@ -6289,9 +6287,9 @@ interventions: a: -1 b: 1 MO_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2020-04-06 period_end_date: 2020-05-03 value: @@ -6307,9 +6305,9 @@ interventions: a: -1 b: 1 MO_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2020-05-04 period_end_date: 2020-06-15 value: @@ -6325,9 +6323,9 @@ interventions: a: -1 b: 1 MO_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2020-06-16 period_end_date: 2021-05-16 value: @@ -6343,9 +6341,9 @@ interventions: a: -1 b: 1 MO_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2021-05-17 period_end_date: 2021-08-15 value: @@ -6361,9 +6359,9 @@ interventions: a: -1 b: 1 MT_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2020-03-28 period_end_date: 2020-04-26 value: @@ -6379,9 +6377,9 @@ interventions: a: -1 b: 1 MT_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2020-04-27 period_end_date: 2020-05-31 value: @@ -6397,9 +6395,9 @@ interventions: a: -1 b: 1 MT_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2020-06-01 period_end_date: 2020-11-19 value: @@ -6415,9 +6413,9 @@ interventions: a: -1 b: 1 MT_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2020-11-20 period_end_date: 2021-01-14 value: @@ -6433,9 +6431,9 @@ interventions: a: -1 b: 1 MT_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2021-01-15 period_end_date: 2021-02-11 value: @@ -6451,9 +6449,9 @@ interventions: a: -1 b: 1 MT_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2021-02-12 period_end_date: 2021-08-15 value: @@ -6469,9 +6467,9 @@ interventions: a: -1 b: 1 NE_sdA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2020-03-16 period_end_date: 2020-05-03 value: @@ -6487,9 +6485,9 @@ interventions: a: -1 b: 1 NE_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2020-05-04 period_end_date: 2020-05-31 value: @@ -6505,9 +6503,9 @@ interventions: a: -1 b: 1 NE_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2020-06-01 period_end_date: 2020-06-21 value: @@ -6523,9 +6521,9 @@ interventions: a: -1 b: 1 NE_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2020-06-22 period_end_date: 2020-09-13 value: @@ -6541,9 +6539,9 @@ interventions: a: -1 b: 1 NE_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2020-09-14 period_end_date: 2020-10-20 value: @@ -6559,9 +6557,9 @@ interventions: a: -1 b: 1 NE_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2020-10-21 period_end_date: 2020-11-10 value: @@ -6577,9 +6575,9 @@ interventions: a: -1 b: 1 NE_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2020-11-11 period_end_date: 2020-12-11 value: @@ -6595,9 +6593,9 @@ interventions: a: -1 b: 1 NE_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2020-12-12 period_end_date: 2020-12-23 value: @@ -6613,9 +6611,9 @@ interventions: a: -1 b: 1 NE_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2020-12-24 period_end_date: 2021-01-29 value: @@ -6631,9 +6629,9 @@ interventions: a: -1 b: 1 NE_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2021-01-30 period_end_date: 2021-05-23 value: @@ -6649,9 +6647,9 @@ interventions: a: -1 b: 1 NE_open_p4C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2021-05-24 period_end_date: 2021-08-15 value: @@ -6667,9 +6665,9 @@ interventions: a: -1 b: 1 NV_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2020-04-01 period_end_date: 2020-05-08 value: @@ -6685,9 +6683,9 @@ interventions: a: -1 b: 1 NV_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2020-05-09 period_end_date: 2020-05-28 value: @@ -6703,9 +6701,9 @@ interventions: a: -1 b: 1 NV_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2020-05-29 period_end_date: 2020-07-09 value: @@ -6721,9 +6719,9 @@ interventions: a: -1 b: 1 NV_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2020-07-10 period_end_date: 2020-09-19 value: @@ -6739,9 +6737,9 @@ interventions: a: -1 b: 1 NV_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2020-09-20 period_end_date: 2020-11-23 value: @@ -6757,9 +6755,9 @@ interventions: a: -1 b: 1 NV_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2020-11-24 period_end_date: 2021-02-14 value: @@ -6775,9 +6773,9 @@ interventions: a: -1 b: 1 NV_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-02-15 period_end_date: 2021-03-14 value: @@ -6793,9 +6791,9 @@ interventions: a: -1 b: 1 NV_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-03-15 period_end_date: 2021-03-29 value: @@ -6811,9 +6809,9 @@ interventions: a: -1 b: 1 NV_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-03-30 period_end_date: 2021-04-30 value: @@ -6829,9 +6827,9 @@ interventions: a: -1 b: 1 NV_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-05-01 period_end_date: 2021-05-02 value: @@ -6847,9 +6845,9 @@ interventions: a: -1 b: 1 NV_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-05-03 period_end_date: 2021-05-31 value: @@ -6865,9 +6863,9 @@ interventions: a: -1 b: 1 NV_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-06-01 period_end_date: 2021-07-29 value: @@ -6883,9 +6881,9 @@ interventions: a: -1 b: 1 NV_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-07-30 period_end_date: 2021-09-09 value: @@ -6901,9 +6899,9 @@ interventions: a: -1 b: 1 NV_open_p7B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-09-10 period_end_date: 2021-10-31 value: @@ -6919,9 +6917,9 @@ interventions: a: -1 b: 1 NH_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2020-03-27 period_end_date: 2020-05-10 value: @@ -6937,9 +6935,9 @@ interventions: a: -1 b: 1 NH_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2020-05-11 period_end_date: 2020-06-14 value: @@ -6955,9 +6953,9 @@ interventions: a: -1 b: 1 NH_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2020-06-15 period_end_date: 2020-06-28 value: @@ -6973,9 +6971,9 @@ interventions: a: -1 b: 1 NH_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2020-06-29 period_end_date: 2020-10-14 value: @@ -6991,9 +6989,9 @@ interventions: a: -1 b: 1 NH_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2020-10-15 period_end_date: 2020-10-29 value: @@ -7009,9 +7007,9 @@ interventions: a: -1 b: 1 NH_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2020-10-30 period_end_date: 2020-11-19 value: @@ -7027,9 +7025,9 @@ interventions: a: -1 b: 1 NH_open_p3D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2020-11-20 period_end_date: 2021-03-10 value: @@ -7045,9 +7043,9 @@ interventions: a: -1 b: 1 NH_open_p3E: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-03-11 period_end_date: 2021-04-16 value: @@ -7063,9 +7061,9 @@ interventions: a: -1 b: 1 NH_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-04-17 period_end_date: 2021-05-07 value: @@ -7081,9 +7079,9 @@ interventions: a: -1 b: 1 NH_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-05-08 period_end_date: 2021-08-15 value: @@ -7099,9 +7097,9 @@ interventions: a: -1 b: 1 NJ_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2020-03-21 period_end_date: 2020-05-18 value: @@ -7117,9 +7115,9 @@ interventions: a: -1 b: 1 NJ_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2020-05-19 period_end_date: 2020-06-14 value: @@ -7135,9 +7133,9 @@ interventions: a: -1 b: 1 NJ_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2020-06-15 period_end_date: 2020-09-03 value: @@ -7153,9 +7151,9 @@ interventions: a: -1 b: 1 NJ_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2020-09-04 period_end_date: 2020-11-11 value: @@ -7171,9 +7169,9 @@ interventions: a: -1 b: 1 NJ_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2020-11-12 period_end_date: 2020-12-06 value: @@ -7189,9 +7187,9 @@ interventions: a: -1 b: 1 NJ_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2020-12-07 period_end_date: 2021-01-01 value: @@ -7207,9 +7205,9 @@ interventions: a: -1 b: 1 NJ_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-01-02 period_end_date: 2021-02-04 value: @@ -7225,9 +7223,9 @@ interventions: a: -1 b: 1 NJ_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-02-05 period_end_date: 2021-02-21 value: @@ -7243,9 +7241,9 @@ interventions: a: -1 b: 1 NJ_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-02-22 period_end_date: 2021-03-18 value: @@ -7261,9 +7259,9 @@ interventions: a: -1 b: 1 NJ_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-03-19 period_end_date: 2021-04-01 value: @@ -7279,9 +7277,9 @@ interventions: a: -1 b: 1 NJ_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-04-02 period_end_date: 2021-05-27 value: @@ -7297,9 +7295,9 @@ interventions: a: -1 b: 1 NJ_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-05-28 period_end_date: 2021-06-03 value: @@ -7315,9 +7313,9 @@ interventions: a: -1 b: 1 NJ_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-06-04 period_end_date: 2021-08-08 value: @@ -7333,9 +7331,9 @@ interventions: a: -1 b: 1 NJ_open_p8A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-08-09 period_end_date: 2021-10-17 value: @@ -7351,9 +7349,9 @@ interventions: a: -1 b: 1 NJ_open_p9A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-10-18 period_end_date: 2021-10-31 value: @@ -7369,9 +7367,9 @@ interventions: a: -1 b: 1 NM_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2020-03-24 period_end_date: 2020-05-31 value: @@ -7387,9 +7385,9 @@ interventions: a: -1 b: 1 NM_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2020-06-01 period_end_date: 2020-07-12 value: @@ -7405,9 +7403,9 @@ interventions: a: -1 b: 1 NM_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2020-07-13 period_end_date: 2020-08-28 value: @@ -7423,9 +7421,9 @@ interventions: a: -1 b: 1 NM_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2020-08-29 period_end_date: 2020-10-15 value: @@ -7441,9 +7439,9 @@ interventions: a: -1 b: 1 NM_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2020-10-16 period_end_date: 2020-11-15 value: @@ -7459,9 +7457,9 @@ interventions: a: -1 b: 1 NM_lockdownB: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2020-11-16 period_end_date: 2020-12-01 value: @@ -7477,9 +7475,9 @@ interventions: a: -1 b: 1 NM_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2020-12-02 period_end_date: 2021-02-09 value: @@ -7495,9 +7493,9 @@ interventions: a: -1 b: 1 NM_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-02-10 period_end_date: 2021-02-23 value: @@ -7513,9 +7511,9 @@ interventions: a: -1 b: 1 NM_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-02-24 period_end_date: 2021-03-09 value: @@ -7531,9 +7529,9 @@ interventions: a: -1 b: 1 NM_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-03-10 period_end_date: 2021-03-23 value: @@ -7549,9 +7547,9 @@ interventions: a: -1 b: 1 NM_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-03-24 period_end_date: 2021-04-06 value: @@ -7567,9 +7565,9 @@ interventions: a: -1 b: 1 NM_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-04-07 period_end_date: 2021-04-20 value: @@ -7585,9 +7583,9 @@ interventions: a: -1 b: 1 NM_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-04-21 period_end_date: 2021-05-04 value: @@ -7603,9 +7601,9 @@ interventions: a: -1 b: 1 NM_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-05-05 period_end_date: 2021-05-13 value: @@ -7621,9 +7619,9 @@ interventions: a: -1 b: 1 NM_open_p6B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-05-14 period_end_date: 2021-06-01 value: @@ -7639,9 +7637,9 @@ interventions: a: -1 b: 1 NM_open_p6C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-06-02 period_end_date: 2021-06-30 value: @@ -7657,9 +7655,9 @@ interventions: a: -1 b: 1 NM_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-07-01 period_end_date: 2021-08-19 value: @@ -7675,9 +7673,9 @@ interventions: a: -1 b: 1 NY_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2020-03-22 period_end_date: 2020-06-07 value: @@ -7693,9 +7691,9 @@ interventions: a: -1 b: 1 NY_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2020-06-08 period_end_date: 2020-06-21 value: @@ -7711,9 +7709,9 @@ interventions: a: -1 b: 1 NY_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2020-06-22 period_end_date: 2020-07-05 value: @@ -7729,9 +7727,9 @@ interventions: a: -1 b: 1 NY_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2020-07-06 period_end_date: 2020-07-19 value: @@ -7747,9 +7745,9 @@ interventions: a: -1 b: 1 NY_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2020-07-20 period_end_date: 2020-09-29 value: @@ -7765,9 +7763,9 @@ interventions: a: -1 b: 1 NY_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2020-09-30 period_end_date: 2020-10-13 value: @@ -7783,9 +7781,9 @@ interventions: a: -1 b: 1 NY_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2020-10-14 period_end_date: 2020-11-12 value: @@ -7801,9 +7799,9 @@ interventions: a: -1 b: 1 NY_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2020-11-13 period_end_date: 2020-12-13 value: @@ -7819,9 +7817,9 @@ interventions: a: -1 b: 1 NY_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2020-12-14 period_end_date: 2021-01-26 value: @@ -7837,9 +7835,9 @@ interventions: a: -1 b: 1 NY_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-01-27 period_end_date: 2021-02-11 value: @@ -7855,9 +7853,9 @@ interventions: a: -1 b: 1 NY_open_p3D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-02-12 period_end_date: 2021-03-18 value: @@ -7873,9 +7871,9 @@ interventions: a: -1 b: 1 NY_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-03-19 period_end_date: 2021-03-31 value: @@ -7891,9 +7889,9 @@ interventions: a: -1 b: 1 NY_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-04-01 period_end_date: 2021-05-18 value: @@ -7909,9 +7907,9 @@ interventions: a: -1 b: 1 NY_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-05-19 period_end_date: 2021-09-12 value: @@ -7927,9 +7925,9 @@ interventions: a: -1 b: 1 NY_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-09-13 period_end_date: 2021-09-26 value: @@ -7945,9 +7943,9 @@ interventions: a: -1 b: 1 NY_open_p7B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-09-27 period_end_date: 2021-10-31 value: @@ -7963,9 +7961,9 @@ interventions: a: -1 b: 1 NC_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2020-03-30 period_end_date: 2020-05-07 value: @@ -7981,9 +7979,9 @@ interventions: a: -1 b: 1 NC_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2020-05-08 period_end_date: 2020-05-21 value: @@ -7999,9 +7997,9 @@ interventions: a: -1 b: 1 NC_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2020-05-22 period_end_date: 2020-09-03 value: @@ -8017,9 +8015,9 @@ interventions: a: -1 b: 1 NC_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2020-09-04 period_end_date: 2020-10-01 value: @@ -8035,9 +8033,9 @@ interventions: a: -1 b: 1 NC_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2020-10-02 period_end_date: 2020-12-10 value: @@ -8053,9 +8051,9 @@ interventions: a: -1 b: 1 NC_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2020-12-11 period_end_date: 2021-02-25 value: @@ -8071,9 +8069,9 @@ interventions: a: -1 b: 1 NC_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-02-26 period_end_date: 2021-03-25 value: @@ -8089,9 +8087,9 @@ interventions: a: -1 b: 1 NC_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-03-26 period_end_date: 2021-04-29 value: @@ -8107,9 +8105,9 @@ interventions: a: -1 b: 1 NC_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-04-30 period_end_date: 2021-05-13 value: @@ -8125,9 +8123,9 @@ interventions: a: -1 b: 1 NC_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-05-14 period_end_date: 2021-08-15 value: @@ -8143,9 +8141,9 @@ interventions: a: -1 b: 1 ND_sdA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2020-03-19 period_end_date: 2020-04-30 value: @@ -8161,9 +8159,9 @@ interventions: a: -1 b: 1 ND_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2020-05-01 period_end_date: 2020-05-28 value: @@ -8179,9 +8177,9 @@ interventions: a: -1 b: 1 ND_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2020-05-29 period_end_date: 2020-10-15 value: @@ -8197,9 +8195,9 @@ interventions: a: -1 b: 1 ND_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2020-10-16 period_end_date: 2020-11-15 value: @@ -8215,9 +8213,9 @@ interventions: a: -1 b: 1 ND_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2020-11-16 period_end_date: 2020-12-21 value: @@ -8233,9 +8231,9 @@ interventions: a: -1 b: 1 ND_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2020-12-22 period_end_date: 2021-01-07 value: @@ -8251,9 +8249,9 @@ interventions: a: -1 b: 1 ND_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2021-01-08 period_end_date: 2021-01-17 value: @@ -8269,9 +8267,9 @@ interventions: a: -1 b: 1 ND_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2021-01-18 period_end_date: 2021-08-15 value: @@ -8287,9 +8285,9 @@ interventions: a: -1 b: 1 OH_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2020-03-23 period_end_date: 2020-05-03 value: @@ -8305,9 +8303,9 @@ interventions: a: -1 b: 1 OH_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2020-05-04 period_end_date: 2020-05-20 value: @@ -8323,9 +8321,9 @@ interventions: a: -1 b: 1 OH_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2020-05-21 period_end_date: 2020-06-18 value: @@ -8341,9 +8339,9 @@ interventions: a: -1 b: 1 OH_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2020-06-19 period_end_date: 2020-09-20 value: @@ -8359,9 +8357,9 @@ interventions: a: -1 b: 1 OH_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2020-09-21 period_end_date: 2020-11-18 value: @@ -8377,9 +8375,9 @@ interventions: a: -1 b: 1 OH_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2020-11-19 period_end_date: 2021-02-10 value: @@ -8395,9 +8393,9 @@ interventions: a: -1 b: 1 OH_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-02-11 period_end_date: 2021-03-01 value: @@ -8413,9 +8411,9 @@ interventions: a: -1 b: 1 OH_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-03-02 period_end_date: 2021-04-04 value: @@ -8431,9 +8429,9 @@ interventions: a: -1 b: 1 OH_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-04-05 period_end_date: 2021-04-26 value: @@ -8449,9 +8447,9 @@ interventions: a: -1 b: 1 OH_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-04-27 period_end_date: 2021-05-16 value: @@ -8467,9 +8465,9 @@ interventions: a: -1 b: 1 OH_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-05-17 period_end_date: 2021-06-01 value: @@ -8485,9 +8483,9 @@ interventions: a: -1 b: 1 OH_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-06-02 period_end_date: 2021-06-18 value: @@ -8503,9 +8501,9 @@ interventions: a: -1 b: 1 OH_open_p6B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-06-19 period_end_date: 2021-08-15 value: @@ -8521,9 +8519,9 @@ interventions: a: -1 b: 1 OK_sdA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2020-03-24 period_end_date: 2020-04-23 value: @@ -8539,9 +8537,9 @@ interventions: a: -1 b: 1 OK_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2020-04-24 period_end_date: 2020-05-14 value: @@ -8557,9 +8555,9 @@ interventions: a: -1 b: 1 OK_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2020-05-15 period_end_date: 2020-05-31 value: @@ -8575,9 +8573,9 @@ interventions: a: -1 b: 1 OK_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2020-06-01 period_end_date: 2020-11-15 value: @@ -8593,9 +8591,9 @@ interventions: a: -1 b: 1 OK_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2020-11-16 period_end_date: 2020-12-13 value: @@ -8611,9 +8609,9 @@ interventions: a: -1 b: 1 OK_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2020-12-14 period_end_date: 2021-01-13 value: @@ -8629,9 +8627,9 @@ interventions: a: -1 b: 1 OK_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-01-14 period_end_date: 2021-03-11 value: @@ -8647,9 +8645,9 @@ interventions: a: -1 b: 1 OK_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-03-12 period_end_date: 2021-08-15 value: @@ -8665,9 +8663,9 @@ interventions: a: -1 b: 1 OR_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2020-03-23 period_end_date: 2020-05-14 value: @@ -8683,9 +8681,9 @@ interventions: a: -1 b: 1 OR_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2020-05-15 period_end_date: 2020-06-04 value: @@ -8701,9 +8699,9 @@ interventions: a: -1 b: 1 OR_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2020-06-05 period_end_date: 2020-06-30 value: @@ -8719,9 +8717,9 @@ interventions: a: -1 b: 1 OR_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2020-07-01 period_end_date: 2020-11-10 value: @@ -8737,9 +8735,9 @@ interventions: a: -1 b: 1 OR_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2020-11-11 period_end_date: 2020-11-17 value: @@ -8755,9 +8753,9 @@ interventions: a: -1 b: 1 OR_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2020-11-18 period_end_date: 2020-12-02 value: @@ -8773,9 +8771,9 @@ interventions: a: -1 b: 1 OR_open_p1C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2020-12-03 period_end_date: 2021-02-11 value: @@ -8791,9 +8789,9 @@ interventions: a: -1 b: 1 OR_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-02-12 period_end_date: 2021-02-25 value: @@ -8809,9 +8807,9 @@ interventions: a: -1 b: 1 OR_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-02-26 period_end_date: 2021-03-28 value: @@ -8827,9 +8825,9 @@ interventions: a: -1 b: 1 OR_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-03-29 period_end_date: 2021-04-18 value: @@ -8845,9 +8843,9 @@ interventions: a: -1 b: 1 OR_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-04-19 period_end_date: 2021-04-29 value: @@ -8863,9 +8861,9 @@ interventions: a: -1 b: 1 OR_open_p2E: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-04-30 period_end_date: 2021-06-08 value: @@ -8881,9 +8879,9 @@ interventions: a: -1 b: 1 OR_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-06-09 period_end_date: 2021-06-29 value: @@ -8899,9 +8897,9 @@ interventions: a: -1 b: 1 OR_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-06-30 period_end_date: 2021-08-12 value: @@ -8917,9 +8915,9 @@ interventions: a: -1 b: 1 OR_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-08-13 period_end_date: 2021-08-26 value: @@ -8935,9 +8933,9 @@ interventions: a: -1 b: 1 OR_open_p7B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-08-27 period_end_date: 2021-10-17 value: @@ -8953,9 +8951,9 @@ interventions: a: -1 b: 1 PA_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2020-03-28 period_end_date: 2020-05-07 value: @@ -8971,9 +8969,9 @@ interventions: a: -1 b: 1 PA_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2020-05-08 period_end_date: 2020-05-28 value: @@ -8989,9 +8987,9 @@ interventions: a: -1 b: 1 PA_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2020-05-29 period_end_date: 2020-07-15 value: @@ -9007,9 +9005,9 @@ interventions: a: -1 b: 1 PA_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2020-07-16 period_end_date: 2020-09-13 value: @@ -9025,9 +9023,9 @@ interventions: a: -1 b: 1 PA_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2020-09-14 period_end_date: 2020-10-05 value: @@ -9043,9 +9041,9 @@ interventions: a: -1 b: 1 PA_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2020-10-06 period_end_date: 2020-12-11 value: @@ -9061,9 +9059,9 @@ interventions: a: -1 b: 1 PA_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2020-12-12 period_end_date: 2021-01-03 value: @@ -9079,9 +9077,9 @@ interventions: a: -1 b: 1 PA_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-01-04 period_end_date: 2021-02-28 value: @@ -9097,9 +9095,9 @@ interventions: a: -1 b: 1 PA_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-03-01 period_end_date: 2021-04-03 value: @@ -9115,9 +9113,9 @@ interventions: a: -1 b: 1 PA_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-04-04 period_end_date: 2021-05-12 value: @@ -9133,9 +9131,9 @@ interventions: a: -1 b: 1 PA_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-05-13 period_end_date: 2021-05-16 value: @@ -9151,9 +9149,9 @@ interventions: a: -1 b: 1 PA_open_p6B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-05-17 period_end_date: 2021-05-30 value: @@ -9169,9 +9167,9 @@ interventions: a: -1 b: 1 PA_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-05-31 period_end_date: 2021-06-27 value: @@ -9187,9 +9185,9 @@ interventions: a: -1 b: 1 PA_open_p7B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-06-28 period_end_date: 2021-09-06 value: @@ -9205,9 +9203,9 @@ interventions: a: -1 b: 1 RI_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2020-03-28 period_end_date: 2020-05-08 value: @@ -9223,9 +9221,9 @@ interventions: a: -1 b: 1 RI_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2020-05-09 period_end_date: 2020-05-31 value: @@ -9241,9 +9239,9 @@ interventions: a: -1 b: 1 RI_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2020-06-01 period_end_date: 2020-06-29 value: @@ -9259,9 +9257,9 @@ interventions: a: -1 b: 1 RI_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2020-06-30 period_end_date: 2020-11-07 value: @@ -9277,9 +9275,9 @@ interventions: a: -1 b: 1 RI_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2020-11-08 period_end_date: 2020-11-29 value: @@ -9295,9 +9293,9 @@ interventions: a: -1 b: 1 RI_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2020-11-30 period_end_date: 2020-12-20 value: @@ -9313,9 +9311,9 @@ interventions: a: -1 b: 1 RI_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2020-12-21 period_end_date: 2021-01-19 value: @@ -9331,9 +9329,9 @@ interventions: a: -1 b: 1 RI_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-01-20 period_end_date: 2021-02-11 value: @@ -9349,9 +9347,9 @@ interventions: a: -1 b: 1 RI_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-02-12 period_end_date: 2021-03-18 value: @@ -9367,9 +9365,9 @@ interventions: a: -1 b: 1 RI_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-03-19 period_end_date: 2021-05-17 value: @@ -9385,9 +9383,9 @@ interventions: a: -1 b: 1 RI_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-05-18 period_end_date: 2021-05-20 value: @@ -9403,9 +9401,9 @@ interventions: a: -1 b: 1 RI_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-05-21 period_end_date: 2021-08-12 value: @@ -9421,9 +9419,9 @@ interventions: a: -1 b: 1 RI_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-08-13 period_end_date: 2021-08-18 value: @@ -9439,9 +9437,9 @@ interventions: a: -1 b: 1 RI_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-08-19 period_end_date: 2021-09-30 value: @@ -9457,9 +9455,9 @@ interventions: a: -1 b: 1 SC_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2020-04-07 period_end_date: 2020-04-20 value: @@ -9475,9 +9473,9 @@ interventions: a: -1 b: 1 SC_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2020-04-21 period_end_date: 2020-05-10 value: @@ -9493,9 +9491,9 @@ interventions: a: -1 b: 1 SC_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2020-05-11 period_end_date: 2020-08-02 value: @@ -9511,9 +9509,9 @@ interventions: a: -1 b: 1 SC_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2020-08-03 period_end_date: 2020-10-01 value: @@ -9529,9 +9527,9 @@ interventions: a: -1 b: 1 SC_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2020-10-02 period_end_date: 2021-02-28 value: @@ -9547,9 +9545,9 @@ interventions: a: -1 b: 1 SC_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-03-01 period_end_date: 2021-03-18 value: @@ -9565,9 +9563,9 @@ interventions: a: -1 b: 1 SC_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-03-19 period_end_date: 2021-05-10 value: @@ -9583,9 +9581,9 @@ interventions: a: -1 b: 1 SC_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-05-11 period_end_date: 2021-06-05 value: @@ -9601,9 +9599,9 @@ interventions: a: -1 b: 1 SC_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-06-06 period_end_date: 2021-08-15 value: @@ -9619,9 +9617,9 @@ interventions: a: -1 b: 1 SD_sdA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2020-03-16 period_end_date: 2020-04-27 value: @@ -9637,9 +9635,9 @@ interventions: a: -1 b: 1 SD_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2020-04-28 period_end_date: 2021-08-15 value: @@ -9655,9 +9653,9 @@ interventions: a: -1 b: 1 TN_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2020-04-02 period_end_date: 2020-04-30 value: @@ -9673,9 +9671,9 @@ interventions: a: -1 b: 1 TN_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2020-05-01 period_end_date: 2020-05-24 value: @@ -9691,9 +9689,9 @@ interventions: a: -1 b: 1 TN_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2020-05-25 period_end_date: 2020-09-28 value: @@ -9709,9 +9707,9 @@ interventions: a: -1 b: 1 TN_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2020-09-29 period_end_date: 2020-12-19 value: @@ -9727,9 +9725,9 @@ interventions: a: -1 b: 1 TN_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2020-12-20 period_end_date: 2021-01-19 value: @@ -9745,9 +9743,9 @@ interventions: a: -1 b: 1 TN_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-01-20 period_end_date: 2021-02-27 value: @@ -9763,9 +9761,9 @@ interventions: a: -1 b: 1 TN_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-02-28 period_end_date: 2021-04-27 value: @@ -9781,9 +9779,9 @@ interventions: a: -1 b: 1 TN_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-04-28 period_end_date: 2021-08-15 value: @@ -9799,9 +9797,9 @@ interventions: a: -1 b: 1 TX_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2020-03-31 period_end_date: 2020-04-30 value: @@ -9817,9 +9815,9 @@ interventions: a: -1 b: 1 TX_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2020-05-01 period_end_date: 2020-05-17 value: @@ -9835,9 +9833,9 @@ interventions: a: -1 b: 1 TX_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2020-05-18 period_end_date: 2020-06-02 value: @@ -9853,9 +9851,9 @@ interventions: a: -1 b: 1 TX_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2020-06-03 period_end_date: 2020-06-25 value: @@ -9871,9 +9869,9 @@ interventions: a: -1 b: 1 TX_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2020-06-26 period_end_date: 2020-09-20 value: @@ -9889,9 +9887,9 @@ interventions: a: -1 b: 1 TX_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2020-09-21 period_end_date: 2020-10-13 value: @@ -9907,9 +9905,9 @@ interventions: a: -1 b: 1 TX_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2020-10-14 period_end_date: 2021-03-09 value: @@ -9925,9 +9923,9 @@ interventions: a: -1 b: 1 TX_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2021-03-10 period_end_date: 2021-08-15 value: @@ -9943,9 +9941,9 @@ interventions: a: -1 b: 1 UT_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2020-03-27 period_end_date: 2020-05-01 value: @@ -9961,9 +9959,9 @@ interventions: a: -1 b: 1 UT_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2020-05-02 period_end_date: 2020-05-15 value: @@ -9979,9 +9977,9 @@ interventions: a: -1 b: 1 UT_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2020-05-16 period_end_date: 2020-06-18 value: @@ -9997,9 +9995,9 @@ interventions: a: -1 b: 1 UT_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2020-06-19 period_end_date: 2020-10-14 value: @@ -10015,9 +10013,9 @@ interventions: a: -1 b: 1 UT_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2020-10-15 period_end_date: 2020-11-08 value: @@ -10033,9 +10031,9 @@ interventions: a: -1 b: 1 UT_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2020-11-09 period_end_date: 2020-11-23 value: @@ -10051,9 +10049,9 @@ interventions: a: -1 b: 1 UT_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2020-11-24 period_end_date: 2021-03-04 value: @@ -10069,9 +10067,9 @@ interventions: a: -1 b: 1 UT_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-03-05 period_end_date: 2021-04-01 value: @@ -10087,9 +10085,9 @@ interventions: a: -1 b: 1 UT_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-04-02 period_end_date: 2021-04-09 value: @@ -10105,9 +10103,9 @@ interventions: a: -1 b: 1 UT_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-04-10 period_end_date: 2021-05-04 value: @@ -10123,9 +10121,9 @@ interventions: a: -1 b: 1 UT_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-05-05 period_end_date: 2021-08-15 value: @@ -10141,9 +10139,9 @@ interventions: a: -1 b: 1 VT_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2020-03-25 period_end_date: 2020-05-15 value: @@ -10159,9 +10157,9 @@ interventions: a: -1 b: 1 VT_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2020-05-16 period_end_date: 2020-05-31 value: @@ -10177,9 +10175,9 @@ interventions: a: -1 b: 1 VT_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2020-06-01 period_end_date: 2020-06-25 value: @@ -10195,9 +10193,9 @@ interventions: a: -1 b: 1 VT_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2020-06-26 period_end_date: 2020-07-31 value: @@ -10213,9 +10211,9 @@ interventions: a: -1 b: 1 VT_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2020-08-01 period_end_date: 2020-11-13 value: @@ -10231,9 +10229,9 @@ interventions: a: -1 b: 1 VT_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2020-11-14 period_end_date: 2021-02-11 value: @@ -10249,9 +10247,9 @@ interventions: a: -1 b: 1 VT_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-02-12 period_end_date: 2021-03-23 value: @@ -10267,9 +10265,9 @@ interventions: a: -1 b: 1 VT_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-03-24 period_end_date: 2021-05-14 value: @@ -10285,9 +10283,9 @@ interventions: a: -1 b: 1 VT_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-05-15 period_end_date: 2021-06-13 value: @@ -10303,9 +10301,9 @@ interventions: a: -1 b: 1 VT_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-06-14 period_end_date: 2021-08-15 value: @@ -10321,9 +10319,9 @@ interventions: a: -1 b: 1 VA_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2020-03-30 period_end_date: 2020-05-14 value: @@ -10339,9 +10337,9 @@ interventions: a: -1 b: 1 VA_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2020-05-15 period_end_date: 2020-06-04 value: @@ -10357,9 +10355,9 @@ interventions: a: -1 b: 1 VA_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2020-06-05 period_end_date: 2020-06-30 value: @@ -10375,9 +10373,9 @@ interventions: a: -1 b: 1 VA_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2020-07-01 period_end_date: 2020-07-30 value: @@ -10393,9 +10391,9 @@ interventions: a: -1 b: 1 VA_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2020-07-31 period_end_date: 2020-09-09 value: @@ -10411,9 +10409,9 @@ interventions: a: -1 b: 1 VA_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2020-09-10 period_end_date: 2020-11-14 value: @@ -10429,9 +10427,9 @@ interventions: a: -1 b: 1 VA_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2020-11-15 period_end_date: 2020-12-13 value: @@ -10447,9 +10445,9 @@ interventions: a: -1 b: 1 VA_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2020-12-14 period_end_date: 2021-02-28 value: @@ -10465,9 +10463,9 @@ interventions: a: -1 b: 1 VA_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: @@ -10483,9 +10481,9 @@ interventions: a: -1 b: 1 VA_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-04-01 period_end_date: 2021-05-13 value: @@ -10501,9 +10499,9 @@ interventions: a: -1 b: 1 VA_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-05-14 period_end_date: 2021-05-27 value: @@ -10519,9 +10517,9 @@ interventions: a: -1 b: 1 VA_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-05-28 period_end_date: 2021-08-15 value: @@ -10537,9 +10535,9 @@ interventions: a: -1 b: 1 WA_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2020-03-23 period_end_date: 2020-05-04 value: @@ -10555,9 +10553,9 @@ interventions: a: -1 b: 1 WA_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2020-05-05 period_end_date: 2020-05-28 value: @@ -10573,9 +10571,9 @@ interventions: a: -1 b: 1 WA_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2020-05-29 period_end_date: 2020-07-01 value: @@ -10591,9 +10589,9 @@ interventions: a: -1 b: 1 WA_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2020-07-02 period_end_date: 2020-10-12 value: @@ -10609,9 +10607,9 @@ interventions: a: -1 b: 1 WA_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2020-10-13 period_end_date: 2020-11-15 value: @@ -10627,9 +10625,9 @@ interventions: a: -1 b: 1 WA_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2020-11-16 period_end_date: 2021-01-10 value: @@ -10645,9 +10643,9 @@ interventions: a: -1 b: 1 WA_open_p2D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-01-11 period_end_date: 2021-01-31 value: @@ -10663,9 +10661,9 @@ interventions: a: -1 b: 1 WA_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-02-01 period_end_date: 2021-02-13 value: @@ -10681,9 +10679,9 @@ interventions: a: -1 b: 1 WA_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-02-14 period_end_date: 2021-03-21 value: @@ -10699,9 +10697,9 @@ interventions: a: -1 b: 1 WA_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-03-22 period_end_date: 2021-05-12 value: @@ -10717,9 +10715,9 @@ interventions: a: -1 b: 1 WA_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-05-13 period_end_date: 2021-05-17 value: @@ -10735,9 +10733,9 @@ interventions: a: -1 b: 1 WA_open_p6B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-05-18 period_end_date: 2021-06-29 value: @@ -10753,9 +10751,9 @@ interventions: a: -1 b: 1 WA_open_p7A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-06-30 period_end_date: 2021-07-05 value: @@ -10771,9 +10769,9 @@ interventions: a: -1 b: 1 WA_open_p8A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-07-06 period_end_date: 2021-08-22 value: @@ -10789,9 +10787,9 @@ interventions: a: -1 b: 1 WA_open_p9A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-08-23 period_end_date: 2021-09-12 value: @@ -10807,9 +10805,9 @@ interventions: a: -1 b: 1 WA_open_p9B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-09-13 period_end_date: 2021-10-17 value: @@ -10825,9 +10823,9 @@ interventions: a: -1 b: 1 WV_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2020-03-24 period_end_date: 2020-05-03 value: @@ -10843,9 +10841,9 @@ interventions: a: -1 b: 1 WV_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2020-05-04 period_end_date: 2020-05-20 value: @@ -10861,9 +10859,9 @@ interventions: a: -1 b: 1 WV_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2020-05-21 period_end_date: 2020-06-04 value: @@ -10879,9 +10877,9 @@ interventions: a: -1 b: 1 WV_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2020-06-05 period_end_date: 2020-06-30 value: @@ -10897,9 +10895,9 @@ interventions: a: -1 b: 1 WV_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2020-07-01 period_end_date: 2020-07-13 value: @@ -10915,9 +10913,9 @@ interventions: a: -1 b: 1 WV_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2020-07-14 period_end_date: 2020-10-12 value: @@ -10933,9 +10931,9 @@ interventions: a: -1 b: 1 WV_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2020-10-13 period_end_date: 2020-11-25 value: @@ -10951,9 +10949,9 @@ interventions: a: -1 b: 1 WV_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2020-11-26 period_end_date: 2021-02-13 value: @@ -10969,9 +10967,9 @@ interventions: a: -1 b: 1 WV_open_p3D: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-02-14 period_end_date: 2021-03-04 value: @@ -10987,9 +10985,9 @@ interventions: a: -1 b: 1 WV_open_p4B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-03-05 period_end_date: 2021-04-19 value: @@ -11005,9 +11003,9 @@ interventions: a: -1 b: 1 WV_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-04-20 period_end_date: 2021-05-13 value: @@ -11023,9 +11021,9 @@ interventions: a: -1 b: 1 WV_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-05-14 period_end_date: 2021-06-07 value: @@ -11041,9 +11039,9 @@ interventions: a: -1 b: 1 WV_open_p6B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-06-08 period_end_date: 2021-06-19 value: @@ -11059,9 +11057,9 @@ interventions: a: -1 b: 1 WV_open_p6C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-06-20 period_end_date: 2021-08-15 value: @@ -11077,9 +11075,9 @@ interventions: a: -1 b: 1 WI_lockdownA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2020-03-25 period_end_date: 2020-05-13 value: @@ -11095,9 +11093,9 @@ interventions: a: -1 b: 1 WI_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2020-05-14 period_end_date: 2020-06-12 value: @@ -11113,9 +11111,9 @@ interventions: a: -1 b: 1 WI_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2020-06-13 period_end_date: 2020-07-31 value: @@ -11131,9 +11129,9 @@ interventions: a: -1 b: 1 WI_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2020-08-01 period_end_date: 2020-10-28 value: @@ -11149,9 +11147,9 @@ interventions: a: -1 b: 1 WI_open_p1B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2020-10-29 period_end_date: 2021-01-12 value: @@ -11167,9 +11165,9 @@ interventions: a: -1 b: 1 WI_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-01-13 period_end_date: 2021-02-08 value: @@ -11185,9 +11183,9 @@ interventions: a: -1 b: 1 WI_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-02-09 period_end_date: 2021-03-18 value: @@ -11203,9 +11201,9 @@ interventions: a: -1 b: 1 WI_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-03-19 period_end_date: 2021-03-30 value: @@ -11221,9 +11219,9 @@ interventions: a: -1 b: 1 WI_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-03-31 period_end_date: 2021-05-31 value: @@ -11239,9 +11237,9 @@ interventions: a: -1 b: 1 WI_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-06-01 period_end_date: 2021-08-04 value: @@ -11257,9 +11255,9 @@ interventions: a: -1 b: 1 WI_open_p5C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-08-05 period_end_date: 2021-08-18 value: @@ -11275,9 +11273,9 @@ interventions: a: -1 b: 1 WY_sdA: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2020-03-28 period_end_date: 2020-04-30 value: @@ -11293,9 +11291,9 @@ interventions: a: -1 b: 1 WY_open_p1A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2020-05-01 period_end_date: 2020-05-14 value: @@ -11311,9 +11309,9 @@ interventions: a: -1 b: 1 WY_open_p2A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2020-05-15 period_end_date: 2020-06-14 value: @@ -11329,9 +11327,9 @@ interventions: a: -1 b: 1 WY_open_p3A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2020-06-15 period_end_date: 2020-08-15 value: @@ -11347,9 +11345,9 @@ interventions: a: -1 b: 1 WY_open_p4A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2020-08-16 period_end_date: 2020-11-23 value: @@ -11365,9 +11363,9 @@ interventions: a: -1 b: 1 WY_open_p3B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2020-11-24 period_end_date: 2020-12-08 value: @@ -11383,9 +11381,9 @@ interventions: a: -1 b: 1 WY_open_p2B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2020-12-09 period_end_date: 2021-01-08 value: @@ -11401,9 +11399,9 @@ interventions: a: -1 b: 1 WY_open_p2C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-01-09 period_end_date: 2021-01-25 value: @@ -11419,9 +11417,9 @@ interventions: a: -1 b: 1 WY_open_p3C: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-01-26 period_end_date: 2021-02-14 value: @@ -11437,9 +11435,9 @@ interventions: a: -1 b: 1 WY_open_p5A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-02-15 period_end_date: 2021-02-28 value: @@ -11455,9 +11453,9 @@ interventions: a: -1 b: 1 WY_open_p5B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-03-01 period_end_date: 2021-03-15 value: @@ -11473,9 +11471,9 @@ interventions: a: -1 b: 1 WY_open_p6A: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-03-16 period_end_date: 2021-05-20 value: @@ -11491,9 +11489,9 @@ interventions: a: -1 b: 1 WY_open_p6B: - template: Reduce + template: SinglePeriodModifier parameter: r0 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-05-21 period_end_date: 2021-08-15 value: @@ -11509,10 +11507,10 @@ interventions: a: -1 b: 1 Seas_jan: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-01-01 end_date: 2020-01-31 @@ -11533,10 +11531,10 @@ interventions: a: -1 b: 1 Seas_feb: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-02-01 end_date: 2020-02-29 @@ -11557,10 +11555,10 @@ interventions: a: -1 b: 1 Seas_mar: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-03-01 end_date: 2020-03-31 @@ -11581,10 +11579,10 @@ interventions: a: -1 b: 1 Seas_may: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-05-01 end_date: 2020-05-31 @@ -11605,10 +11603,10 @@ interventions: a: -1 b: 1 Seas_jun: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-06-01 end_date: 2020-06-30 @@ -11629,10 +11627,10 @@ interventions: a: -1 b: 1 Seas_jul: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-07-01 end_date: 2020-07-31 @@ -11653,10 +11651,10 @@ interventions: a: -1 b: 1 Seas_aug: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-08-01 end_date: 2020-08-31 @@ -11677,10 +11675,10 @@ interventions: a: -1 b: 1 Seas_sep: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-09-01 end_date: 2020-09-30 @@ -11701,10 +11699,10 @@ interventions: a: -1 b: 1 Seas_oct: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-10-01 end_date: 2020-10-31 @@ -11723,10 +11721,10 @@ interventions: a: -1 b: 1 Seas_nov: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-11-01 end_date: 2020-11-30 @@ -11745,10 +11743,10 @@ interventions: a: -1 b: 1 Seas_dec: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: 2020-12-01 end_date: 2020-12-31 @@ -11767,44095 +11765,44095 @@ interventions: a: -1 b: 1 AL_Dose1_jan2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00065 AL_Dose1_jan2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00139 AL_Dose1_feb2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00002 AL_Dose1_feb2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00162 AL_Dose1_feb2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.01672 AL_Dose1_mar2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.00007 AL_Dose1_mar2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.00283 AL_Dose1_mar2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.00899 AL_Dose1_apr2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.00012 AL_Dose1_apr2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.00624 AL_Dose1_apr2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.01364 AL_Dose1_may2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00021 AL_Dose1_may2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00308 AL_Dose1_may2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00594 AL_Dose1_jun2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.0005 AL_Dose1_jun2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00164 AL_Dose1_jun2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.0025 AL_Dose1_jul2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.0009 AL_Dose1_jul2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00261 AL_Dose1_jul2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00443 AL_Dose1_aug2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00149 AL_Dose1_aug2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00429 AL_Dose1_aug2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00513 AL_Dose1_sep2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.0008 AL_Dose1_sep2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00439 AL_Dose1_sep2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00668 AL_Dose1_oct2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00045 AL_Dose1_oct2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00208 AL_Dose1_oct2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00431 AL_Dose3_oct2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000066 AL_Dose3_oct2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000274 AL_Dose3_oct2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000361 AL_Dose1_nov2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.0007 AL_Dose1_nov2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.0021 AL_Dose1_nov2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.00692 AL_Dose3_nov2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.000118 AL_Dose3_nov2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.001277 AL_Dose3_nov2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.009406 AL_Dose1_dec2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00126 AL_Dose1_dec2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00248 AL_Dose1_dec2021_age65to100: - template: Reduce 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["10000"] + subpop: ["10000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00045 DE_Dose1_oct2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00379 DE_Dose1_oct2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.04731 DE_Dose3_oct2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000543 DE_Dose3_oct2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - 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nu3age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.000427 DE_Dose3_nov2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.001417 DE_Dose3_nov2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.007054 DE_Dose1_dec2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00383 DE_Dose1_dec2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00155 DE_Dose1_dec2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.02678 DE_Dose3_dec2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.001217 DE_Dose3_dec2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.001663 DE_Dose3_dec2021_65to100: - template: Reduce + template: 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Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.00064 DE_Dose1_feb2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.02742 DE_Dose3_feb2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.001155 DE_Dose3_feb2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.009221 DE_Dose3_feb2022_65to100: - 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DE_Dose3_mar2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.001185 DE_Dose3_mar2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.004472 DE_Dose3_mar2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.002811 DE_Dose1_apr2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 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value: 0.002316 DE_Dose3_apr2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.001613 DE_Dose1_may2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.00045 DE_Dose1_may2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.00016 DE_Dose1_may2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: 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value: distribution: fixed value: 0.00026 DE_Dose1_jun2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.0001 DE_Dose3_jun2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.002176 DE_Dose3_jun2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.002457 DE_Dose3_jun2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-06-01 period_end_date: 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period_end_date: 2022-07-31 value: distribution: fixed value: 0.00372 DE_Dose3_jul2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.001834 DE_Dose3_jul2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00151 DE_Dose1_aug2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00009 DE_Dose1_aug2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00003 DE_Dose3_aug2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.001758 DE_Dose3_aug2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00251 DE_Dose1_sep2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00005 DE_Dose1_sep2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00002 DE_Dose3_sep2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.001929 DE_Dose3_sep2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.000862 DC_Dose1_jan2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00049 DC_Dose1_jan2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["11000"] + subpop: 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distribution: fixed value: 0.00041 MI_Dose1_apr2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.01022 MI_Dose1_apr2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.01358 MI_Dose1_may2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00105 MI_Dose1_may2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00683 MI_Dose1_may2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00845 MI_Dose1_jun2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00206 MI_Dose1_jun2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00317 MI_Dose1_jun2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00472 MI_Dose1_jul2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00086 MI_Dose1_jul2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00164 MI_Dose1_jul2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00275 MI_Dose1_aug2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00076 MI_Dose1_aug2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00195 MI_Dose1_aug2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00275 MI_Dose1_sep2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00043 MI_Dose1_sep2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00164 MI_Dose1_sep2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00311 MI_Dose1_oct2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00033 MI_Dose1_oct2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00153 MI_Dose1_oct2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00594 MI_Dose3_oct2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000065 MI_Dose3_oct2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000518 MI_Dose3_oct2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000533 MI_Dose1_nov2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.00285 MI_Dose1_nov2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.00142 MI_Dose1_nov2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.0078 MI_Dose3_nov2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.000407 MI_Dose3_nov2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.001225 MI_Dose3_nov2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.007047 MI_Dose1_dec2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00229 MI_Dose1_dec2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00131 MI_Dose1_dec2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00425 MI_Dose3_dec2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.001041 MI_Dose3_dec2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.002375 MI_Dose3_dec2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.015951 MI_Dose1_jan2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00135 MI_Dose1_jan2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00121 MI_Dose1_jan2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00364 MI_Dose3_jan2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00199 MI_Dose3_jan2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.007229 MI_Dose3_jan2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.009437 MI_Dose1_feb2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.0013 MI_Dose1_feb2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.00112 MI_Dose1_feb2022_age65to100: - template: Reduce + 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Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00076 MI_Dose1_mar2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00103 MI_Dose1_mar2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00254 MI_Dose3_mar2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.000801 MI_Dose3_mar2022_18to64: - 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MI_Dose1_apr2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.00205 MI_Dose3_apr2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.000419 MI_Dose3_apr2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.001514 MI_Dose3_apr2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.001218 MI_Dose1_may2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.00046 MI_Dose1_may2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.00086 MI_Dose1_may2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.00163 MI_Dose3_may2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.000321 MI_Dose3_may2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.001211 MI_Dose3_may2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.000963 MI_Dose1_jun2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.00035 MI_Dose1_jun2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: 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value: distribution: fixed value: 0.001102 MI_Dose1_jul2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00027 MI_Dose1_jul2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00071 MI_Dose1_jul2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00099 MI_Dose3_jul2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2022-07-01 period_end_date: 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template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00086 NV_Dose3_aug2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.002108 NV_Dose3_aug2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.001907 NV_Dose3_aug2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.003047 NV_Dose1_sep2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00018 NV_Dose1_sep2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00003 NV_Dose1_sep2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00065 NV_Dose3_sep2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.002503 NV_Dose3_sep2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.001038 NV_Dose3_sep2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.001259 NH_Dose1_jan2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00131 NH_Dose1_jan2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00254 NH_Dose1_feb2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00005 NH_Dose1_feb2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00161 NH_Dose1_feb2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00884 NH_Dose1_mar2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.00012 NH_Dose1_mar2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.00354 NH_Dose1_mar2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.03253 NH_Dose1_apr2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.0006 NH_Dose1_apr2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.02036 NH_Dose1_apr2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.02024 NH_Dose1_may2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.0009 NH_Dose1_may2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00359 NH_Dose1_may2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.01476 NH_Dose1_jun2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00432 NH_Dose1_jun2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00186 NH_Dose1_jun2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00477 NH_Dose1_jul2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00123 NH_Dose1_jul2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00254 NH_Dose1_jul2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.01422 NH_Dose1_aug2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00118 NH_Dose1_aug2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00284 NH_Dose1_aug2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00723 NH_Dose1_sep2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00078 NH_Dose1_sep2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00353 NH_Dose1_sep2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.01937 NH_Dose1_oct2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00045 NH_Dose1_oct2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00688 NH_Dose1_oct2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.09664 NH_Dose3_oct2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000118 NH_Dose3_oct2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000837 NH_Dose3_oct2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.001245 NH_Dose1_nov2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.00234 NH_Dose1_nov2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.01996 NH_Dose1_nov2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.00796 NH_Dose3_nov2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.000597 NH_Dose3_nov2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.001232 NH_Dose3_nov2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.004894 NH_Dose1_dec2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00628 NH_Dose1_dec2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00423 NH_Dose1_dec2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.02613 NH_Dose3_dec2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.000889 NH_Dose3_dec2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.002716 NH_Dose3_dec2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.019894 NH_Dose1_jan2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00238 NH_Dose1_jan2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00293 NH_Dose1_jan2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.02581 NH_Dose3_jan2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.004045 NH_Dose3_jan2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.009508 NH_Dose3_jan2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.015219 NH_Dose1_feb2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.0017 NH_Dose1_feb2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.00198 NH_Dose1_feb2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.02689 NH_Dose3_feb2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.00112 NH_Dose3_feb2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.014721 NH_Dose3_feb2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.005095 NH_Dose1_mar2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00128 NH_Dose1_mar2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.0013 NH_Dose1_mar2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.02475 NH_Dose3_mar2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.001169 NH_Dose3_mar2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.000797 NH_Dose3_mar2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.001975 NH_Dose1_apr2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.00075 NH_Dose1_apr2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.00082 NH_Dose1_apr2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.02809 NH_Dose3_apr2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.000745 NH_Dose3_apr2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.001669 NH_Dose3_apr2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.002742 NH_Dose1_may2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.00043 NH_Dose1_may2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.00052 NH_Dose1_may2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.02353 NH_Dose3_may2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.000429 NH_Dose3_may2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: 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nu1age65to100 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.00034 OH_Dose3_jun2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.001797 OH_Dose3_jun2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.001631 OH_Dose3_jun2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.000999 OH_Dose1_jul2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00012 OH_Dose1_jul2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00042 OH_Dose1_jul2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00025 OH_Dose3_jul2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.002141 OH_Dose3_jul2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.001289 OH_Dose3_jul2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.000983 OH_Dose1_aug2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00007 OH_Dose1_aug2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00035 OH_Dose1_aug2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00018 OH_Dose3_aug2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.001128 OH_Dose3_aug2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.001092 OH_Dose3_aug2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.001341 OH_Dose1_sep2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00004 OH_Dose1_sep2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00029 OH_Dose1_sep2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00013 OH_Dose3_sep2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.001224 OH_Dose3_sep2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.000924 OH_Dose3_sep2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.000726 OK_Dose1_jan2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00136 OK_Dose1_jan2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00272 OK_Dose1_feb2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00007 OK_Dose1_feb2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00217 OK_Dose1_feb2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.01279 OK_Dose1_mar2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.00012 OK_Dose1_mar2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.00482 OK_Dose1_mar2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.02572 OK_Dose1_apr2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.00036 OK_Dose1_apr2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.00814 OK_Dose1_apr2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.01253 OK_Dose1_may2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00028 OK_Dose1_may2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00291 OK_Dose1_may2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00465 OK_Dose1_jun2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00116 OK_Dose1_jun2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00237 OK_Dose1_jun2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00329 OK_Dose1_jul2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00073 OK_Dose1_jul2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00186 OK_Dose1_jul2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00405 OK_Dose1_aug2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.0017 OK_Dose1_aug2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.0041 OK_Dose1_aug2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00502 OK_Dose1_sep2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.0007 OK_Dose1_sep2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00458 OK_Dose1_sep2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00959 OK_Dose1_oct2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00057 OK_Dose1_oct2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00291 OK_Dose1_oct2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.01442 OK_Dose3_oct2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00012 OK_Dose3_oct2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000748 OK_Dose3_oct2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.001009 OK_Dose1_nov2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.00122 OK_Dose1_nov2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.00254 OK_Dose1_nov2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.04208 OK_Dose3_nov2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.000359 OK_Dose3_nov2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.002012 OK_Dose3_nov2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.007255 OK_Dose1_dec2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.002 OK_Dose1_dec2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00097 OK_Dose1_dec2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.01382 OK_Dose3_dec2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.000276 OK_Dose3_dec2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.002807 OK_Dose3_dec2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.020407 OK_Dose1_jan2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00166 OK_Dose1_jan2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00062 OK_Dose1_jan2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.0139 OK_Dose3_jan2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.001154 OK_Dose3_jan2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00769 OK_Dose3_jan2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.007578 OK_Dose1_feb2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.00298 OK_Dose1_feb2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.0004 OK_Dose1_feb2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.01395 OK_Dose3_feb2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.000684 OK_Dose3_feb2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.003616 OK_Dose3_feb2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.003343 OK_Dose1_mar2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.0014 OK_Dose1_mar2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00026 OK_Dose1_mar2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.01397 OK_Dose3_mar2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.001639 OK_Dose3_mar2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00164 OK_Dose3_mar2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.001195 OK_Dose1_apr2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.00117 OK_Dose1_apr2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.00016 OK_Dose1_apr2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.014 OK_Dose3_apr2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.000678 OK_Dose3_apr2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.001916 OK_Dose3_apr2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.001995 OK_Dose1_may2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.00097 OK_Dose1_may2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.0001 OK_Dose1_may2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.01402 OK_Dose3_may2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.000552 OK_Dose3_may2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.002135 OK_Dose3_may2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.001435 OK_Dose1_jun2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.0008 OK_Dose1_jun2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.00006 OK_Dose1_jun2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.01399 OK_Dose3_jun2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.000984 OK_Dose3_jun2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.003628 OK_Dose3_jun2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.002757 OK_Dose1_jul2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00066 OK_Dose1_jul2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00004 OK_Dose1_jul2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.01408 OK_Dose3_jul2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.001959 OK_Dose3_jul2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00209 OK_Dose3_jul2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.002734 OK_Dose1_aug2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00054 OK_Dose1_aug2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00002 OK_Dose1_aug2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.01399 OK_Dose3_aug2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.001412 OK_Dose3_aug2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.001974 OK_Dose3_aug2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.005074 OK_Dose1_sep2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00044 OK_Dose1_sep2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00001 OK_Dose1_sep2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.01406 OK_Dose3_sep2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.002674 OK_Dose3_sep2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00071 OK_Dose3_sep2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.000949 OR_Dose1_jan2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00104 OR_Dose1_jan2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00166 OR_Dose1_feb2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00004 OR_Dose1_feb2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00278 OR_Dose1_feb2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00454 OR_Dose1_mar2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.00006 OR_Dose1_mar2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.00287 OR_Dose1_mar2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.02005 OR_Dose1_apr2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.00024 OR_Dose1_apr2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.00903 OR_Dose1_apr2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.02318 OR_Dose1_may2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00169 OR_Dose1_may2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.01144 OR_Dose1_may2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-05-01 period_end_date: 2021-05-31 value: distribution: fixed value: 0.00908 OR_Dose1_jun2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00312 OR_Dose1_jun2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00572 OR_Dose1_jun2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00595 OR_Dose1_jul2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00095 OR_Dose1_jul2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00273 OR_Dose1_jul2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.0033 OR_Dose1_aug2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00093 OR_Dose1_aug2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00281 OR_Dose1_aug2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00393 OR_Dose1_sep2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00065 OR_Dose1_sep2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00522 OR_Dose1_sep2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-09-01 period_end_date: 2021-09-30 value: distribution: fixed value: 0.00606 OR_Dose1_oct2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00053 OR_Dose1_oct2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00307 OR_Dose1_oct2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.00616 OR_Dose3_oct2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000059 OR_Dose3_oct2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000459 OR_Dose3_oct2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-10-01 period_end_date: 2021-10-31 value: distribution: fixed value: 0.000868 OR_Dose1_nov2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.00486 OR_Dose1_nov2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.00208 OR_Dose1_nov2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.00986 OR_Dose3_nov2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.000235 OR_Dose3_nov2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.002193 OR_Dose3_nov2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-11-01 period_end_date: 2021-11-30 value: distribution: fixed value: 0.002872 OR_Dose1_dec2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.0039 OR_Dose1_dec2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00044 OR_Dose1_dec2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00679 OR_Dose3_dec2021_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00168 OR_Dose3_dec2021_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.00279 OR_Dose3_dec2021_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-12-01 period_end_date: 2021-12-31 value: distribution: fixed value: 0.012615 OR_Dose1_jan2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00182 OR_Dose1_jan2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00022 OR_Dose1_jan2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.00614 OR_Dose3_jan2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.003027 OR_Dose3_jan2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.005397 OR_Dose3_jan2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-01-01 period_end_date: 2022-01-31 value: distribution: fixed value: 0.017703 OR_Dose1_feb2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.00201 OR_Dose1_feb2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.00011 OR_Dose1_feb2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.00544 OR_Dose3_feb2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.000734 OR_Dose3_feb2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.010243 OR_Dose3_feb2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.004881 OR_Dose1_mar2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00143 OR_Dose1_mar2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00006 OR_Dose1_mar2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00471 OR_Dose3_mar2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00096 OR_Dose3_mar2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.005879 OR_Dose3_mar2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.003238 OR_Dose1_apr2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.00119 OR_Dose1_apr2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.00003 OR_Dose1_apr2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.00395 OR_Dose3_apr2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.000615 OR_Dose3_apr2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.002466 OR_Dose3_apr2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-04-01 period_end_date: 2022-04-30 value: distribution: fixed value: 0.001584 OR_Dose1_may2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.00099 OR_Dose1_may2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.00001 OR_Dose1_may2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.00323 OR_Dose3_may2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.000501 OR_Dose3_may2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.001379 OR_Dose3_may2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-05-01 period_end_date: 2022-05-31 value: distribution: fixed value: 0.000992 OR_Dose1_jun2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.00082 OR_Dose1_jun2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.00001 OR_Dose1_jun2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.00259 OR_Dose3_jun2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["41000"] + 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["41000"] + subpop: ["41000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00203 OR_Dose3_jul2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.003896 OR_Dose3_jul2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.002081 OR_Dose3_jul2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.001232 OR_Dose1_aug2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - 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nu3age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.002096 OR_Dose1_sep2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00045 OR_Dose1_sep2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00119 OR_Dose3_sep2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.001596 OR_Dose3_sep2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.000342 OR_Dose3_sep2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.001242 PA_Dose1_jan2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00102 PA_Dose1_jan2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-01-01 period_end_date: 2021-01-31 value: distribution: fixed value: 0.00147 PA_Dose1_feb2021_age0to17: - template: Reduce + template: 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PA_Dose1_jun2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00269 PA_Dose1_jun2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00601 PA_Dose1_jun2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.07974 PA_Dose1_jul2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed 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period_end_date: 2021-02-28 value: distribution: fixed value: 0.00001 TN_Dose1_feb2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.0009 TN_Dose1_feb2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-02-01 period_end_date: 2021-02-28 value: distribution: fixed value: 0.00789 TN_Dose1_mar2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.0001 TN_Dose1_mar2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.00298 TN_Dose1_mar2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-03-01 period_end_date: 2021-03-31 value: distribution: fixed value: 0.01971 TN_Dose1_apr2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.0002 TN_Dose1_apr2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-04-01 period_end_date: 2021-04-30 value: distribution: fixed value: 0.00723 TN_Dose1_apr2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["47000"] + subpop: ["47000"] 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["47000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00104 TN_Dose1_jun2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00209 TN_Dose1_jun2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-06-01 period_end_date: 2021-06-30 value: distribution: fixed value: 0.00288 TN_Dose1_jul2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00077 TN_Dose1_jul2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00196 TN_Dose1_jul2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-07-01 period_end_date: 2021-07-31 value: distribution: fixed value: 0.00351 TN_Dose1_aug2021_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00117 TN_Dose1_aug2021_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-08-01 period_end_date: 2021-08-31 value: distribution: fixed value: 0.00343 TN_Dose1_aug2021_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - 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period_end_date: 2022-02-28 value: distribution: fixed value: 0.00092 WY_Dose1_feb2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.00192 WY_Dose3_feb2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.000516 WY_Dose3_feb2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.003403 WY_Dose3_feb2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-02-01 period_end_date: 2022-02-28 value: distribution: fixed value: 0.002668 WY_Dose1_mar2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00116 WY_Dose1_mar2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00072 WY_Dose1_mar2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-03-01 period_end_date: 2022-03-31 value: distribution: fixed value: 0.00153 WY_Dose3_mar2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["56000"] + subpop: ["56000"] 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nu3age65to100 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-06-01 period_end_date: 2022-06-30 value: distribution: fixed value: 0.002057 WY_Dose1_jul2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00043 WY_Dose1_jul2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00023 WY_Dose1_jul2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.00052 WY_Dose3_jul2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.001345 WY_Dose3_jul2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.001902 WY_Dose3_jul2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-07-01 period_end_date: 2022-07-31 value: distribution: fixed value: 0.001688 WY_Dose1_aug2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00033 WY_Dose1_aug2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00017 WY_Dose1_aug2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.00039 WY_Dose3_aug2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.001075 WY_Dose3_aug2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.001447 WY_Dose3_aug2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-08-01 period_end_date: 2022-08-31 value: distribution: fixed value: 0.0029 WY_Dose1_sep2022_age0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu1age0to17 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00025 WY_Dose1_sep2022_age18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu1age18to64 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00012 WY_Dose1_sep2022_age65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu1age65to100 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.00029 WY_Dose3_sep2022_0to17: - template: Reduce + template: SinglePeriodModifier parameter: nu3age0to17 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.001513 WY_Dose3_sep2022_18to64: - template: Reduce + template: SinglePeriodModifier parameter: nu3age18to64 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.001191 WY_Dose3_sep2022_65to100: - template: Reduce + template: SinglePeriodModifier parameter: nu3age65to100 - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2022-09-01 period_end_date: 2022-09-03 value: distribution: fixed value: 0.001099 local_variance_chi3: - template: Stacked + template: StackedModifier scenarios: ["local_variance_chi3_NEW"] NPI: - template: Stacked + template: StackedModifier scenarios: ["school_year", "holiday_season2021", "AL_lockdownA", "AL_open_p1A", "AL_open_p2A", "AL_open_p2B", "AL_open_p3A", "AL_open_p4A", "AL_open_p5A", "AK_lockdownA", "AK_open_p1A", "AK_open_p2A", "AK_open_p4A", "AK_open_p3A", "AK_open_p4B", "AZ_lockdownA", "AZ_open_p2A", "AZ_open_p1A", "AZ_open_p2B", "AZ_open_p2C", "AZ_open_p3A", "AZ_open_p4A", "AR_sdA", "AR_open_p1A", "AR_open_p2A", "AR_open_p2B", "AR_open_p2C", "AR_open_p2D", "AR_open_p3A", "AR_open_p4A", "CA_lockdownA", "CA_open_p2A", "CA_open_p2B", "CA_open_p1A", "CA_open_p1B", "CA_lockdownB", "CA_lockdownC", "CA_open_p1C", "CA_open_p2C", "CA_open_p3A", "CA_open_p4A", "CA_open_p5A", "CA_open_p5B", "CO_lockdownA", "CO_open_p2A", "CO_open_p1A", "CO_open_p2B", "CO_open_p1B", "CO_lockdownB", "CO_open_p1C", "CO_open_p3A", "CO_open_p3B", "CO_open_p4A", "CO_open_p5A", "CO_open_p6A", "CO_open_p7A", "CT_lockdownA", "CT_open_p1A", "CT_open_p2A", "CT_open_p3A", "CT_open_p2B", "CT_open_p2C", "CT_open_p4A", "CT_open_p5A", "CT_open_p5B", "CT_open_p6A", "CT_open_p7A", "DE_lockdownA", "DE_open_p1A", "DE_open_p2A", "DE_open_p1B", "DE_open_p1C", "DE_open_p1D", "DE_open_p2B", "DE_open_p2C", "DE_open_p2D", "DE_open_p3A", "DE_open_p4A", "DC_lockdownA", "DC_open_p1A", "DC_open_p2A", "DC_open_p2B", "DC_open_p2C", "DC_open_p1B", "DC_open_p2D", "DC_open_p2E", "DC_open_p3A", "DC_open_p4A", "DC_open_p5A", "DC_open_p6A", "DC_open_p4B", "DC_open_p7A", "FL_lockdownA", "FL_open_p1A", "FL_open_p2A", "FL_open_p3A", "FL_open_p4A", "FL_open_p5A", "FL_open_p6A", "FL_open_p7A", "GA_lockdownA", "GA_open_p1A", "GA_open_p2A", "GA_open_p3A", "GA_open_p3B", "GA_open_p3C", "GA_open_p4A", "GA_open_p5A", "GA_open_p5B", "HI_lockdownA", "HI_open_p1A", "HI_open_p2A", "HI_open_p1B", "HI_open_p2B", "HI_open_p1C", "HI_open_p2C", "HI_open_p2D", "HI_open_p3A", "HI_open_p3B", "HI_open_p3C", "HI_open_p3D", "HI_open_p4A", "HI_open_p5A", "HI_open_p5B", "HI_open_p6A", "HI_open_p6B", "ID_lockdownA", "ID_open_p1A", "ID_open_p2A", "ID_open_p3A", "ID_open_p4A", "ID_open_p3B", "ID_open_p2B", "ID_open_p2C", "ID_open_p3C", "ID_open_p4B", "IL_lockdownA", "IL_open_p3A", "IL_open_p4A", "IL_open_p3B", "IL_open_p3C", "IL_open_p2A", "IL_open_p2B", "IL_open_p3D", "IL_open_p4B", "IL_open_p5A", "IL_open_p6A", "IL_open_p5B", "IL_open_p7A", "IN_lockdownA", "IN_open_p1A", "IN_open_p2A", "IN_open_p3A", "IN_open_p4A", "IN_open_p5A", "IN_open_p2B", "IN_open_p1B", "IN_open_p2C", "IN_open_p3B", "IN_open_p4B", "IN_open_p5B", "IN_open_p5C", "IA_sdA", "IA_open_p1A", "IA_open_p2A", "IA_open_p3A", "IA_open_p2B", "IA_open_p3B", "IA_open_p3C", "IA_open_p3D", "IA_open_p3E", "IA_open_p4A", "KS_lockdownA", "KS_open_p1A", "KS_open_p2A", "KS_open_p3A", "KS_open_p3B", "KS_open_p4A", "KS_open_p4B", "KS_open_p4C", "KY_lockdownA", "KY_open_p1A", "KY_open_p2A", "KY_open_p3A", "KY_open_p2B", "KY_open_p3B", "KY_open_p2C", "KY_open_p3C", "KY_open_p3D", "KY_open_p4A", "KY_open_p4B", "KY_open_p5A", "KY_open_p5B", "KY_open_p6A", "LA_lockdownA", "LA_open_p1A", "LA_open_p2A", "LA_open_p2B", "LA_open_p3A", "LA_open_p2C", "LA_open_p3B", "LA_open_p3C", "LA_open_p4A", "LA_open_p5A", "LA_open_p5B", "LA_open_p4B", "ME_lockdownA", "ME_open_p1A", "ME_open_p2A", "ME_open_p3A", "ME_open_p4A", "ME_open_p3B", "ME_open_p4B", "ME_open_p4C", "ME_open_p5A", "ME_open_p6A", "MD_lockdownA", "MD_open_p1A", "MD_open_p2A", "MD_open_p3A", "MD_open_p2B", "MD_open_p2C", "MD_open_p2D", "MD_open_p4A", "MD_open_p5A", "MD_open_p6A", "MD_open_p7A", "MD_open_p8A", "MA_lockdownA", "MA_open_p1A", "MA_open_p2A", "MA_open_p3A", "MA_open_p3B", "MA_open_p3C", "MA_open_p3D", "MA_open_p2B", "MA_open_p2C", "MA_open_p3E", "MA_open_p4A", "MA_open_p5A", "MA_open_p5B", "MA_open_p6A", "MI_lockdownA", "MI_open_p2A", "MI_open_p1A", "MI_open_p2B", "MI_open_p2C", "MI_open_p1B", "MI_open_p2D", "MI_open_p2E", "MI_open_p2F", "MI_open_p3A", "MI_open_p3B", "MI_open_p4A", "MI_open_p5A", "MI_open_p6A", "MN_lockdownA", "MN_open_p1A", "MN_open_p2A", "MN_open_p3A", "MN_open_p3B", "MN_open_p1B", "MN_open_p2B", "MN_open_p3C", "MN_open_p3D", "MN_open_p4A", "MN_open_p4B", "MN_open_p4C", "MN_open_p5A", "MN_open_p5B", "MS_lockdownA", "MS_open_p1A", "MS_open_p2A", "MS_open_p3A", "MS_open_p4A", "MS_open_p3B", "MS_open_p3C", "MS_open_p5A", "MS_open_p5B", "MS_open_p5C", "MO_lockdownA", "MO_open_p3A", "MO_open_p4A", "MO_open_p5A", "MT_lockdownA", "MT_open_p1A", "MT_open_p2A", "MT_open_p2B", "MT_open_p3A", "MT_open_p4A", "NE_sdA", "NE_open_p1A", "NE_open_p2A", "NE_open_p3A", "NE_open_p4A", "NE_open_p2B", "NE_open_p2C", "NE_open_p2D", "NE_open_p3B", "NE_open_p4B", "NE_open_p4C", "NV_lockdownA", "NV_open_p1A", "NV_open_p3A", "NV_open_p2A", "NV_open_p3B", "NV_open_p2B", "NV_open_p3C", "NV_open_p4A", "NV_open_p4B", "NV_open_p5A", "NV_open_p5B", "NV_open_p6A", "NV_open_p7A", "NV_open_p7B", "NH_lockdownA", "NH_open_p1A", "NH_open_p2A", "NH_open_p3A", "NH_open_p3B", "NH_open_p3C", "NH_open_p3D", "NH_open_p3E", "NH_open_p4A", "NH_open_p4B", "NJ_lockdownA", "NJ_open_p1A", "NJ_open_p2A", "NJ_open_p3A", "NJ_open_p2B", "NJ_open_p2C", "NJ_open_p2D", "NJ_open_p3B", "NJ_open_p3C", "NJ_open_p4A", "NJ_open_p5A", "NJ_open_p6A", "NJ_open_p7A", "NJ_open_p8A", "NJ_open_p9A", "NM_lockdownA", "NM_open_p2A", "NM_open_p1A", "NM_open_p2B", "NM_open_p2C", "NM_lockdownB", "NM_open_p1B", "NM_open_p2D", "NM_open_p3A", "NM_open_p3B", "NM_open_p4A", "NM_open_p5A", "NM_open_p4B", "NM_open_p6A", "NM_open_p6B", "NM_open_p6C", "NM_open_p7A", "NY_lockdownA", "NY_open_p1A", "NY_open_p1B", "NY_open_p2A", "NY_open_p3A", "NY_open_p3B", "NY_open_p2B", "NY_open_p2C", "NY_open_p2D", "NY_open_p3C", "NY_open_p3D", "NY_open_p4A", "NY_open_p5A", "NY_open_p6A", "NY_open_p7A", "NY_open_p7B", "NC_lockdownA", "NC_open_p1A", "NC_open_p2A", "NC_open_p2B", "NC_open_p3A", "NC_open_p2C", "NC_open_p4A", "NC_open_p5A", "NC_open_p5B", "NC_open_p6A", "ND_sdA", "ND_open_p1A", "ND_open_p3A", "ND_open_p2A", "ND_open_p2B", "ND_open_p2C", "ND_open_p2D", "ND_open_p4A", "OH_lockdownA", "OH_open_p1A", "OH_open_p2A", "OH_open_p3A", "OH_open_p3B", "OH_open_p2B", "OH_open_p3C", "OH_open_p4A", "OH_open_p4B", "OH_open_p5A", "OH_open_p5B", "OH_open_p6A", "OH_open_p6B", "OK_sdA", "OK_open_p1A", "OK_open_p2A", "OK_open_p3A", "OK_open_p3B", "OK_open_p2B", "OK_open_p2C", "OK_open_p4A", "OR_lockdownA", "OR_open_p1A", "OR_open_p2A", "OR_open_p2B", "OR_open_p2C", "OR_open_p1B", "OR_open_p1C", "OR_open_p2D", "OR_open_p3A", "OR_open_p4A", "OR_open_p4B", "OR_open_p2E", "OR_open_p5A", "OR_open_p6A", "OR_open_p7A", "OR_open_p7B", "PA_lockdownA", "PA_open_p1A", "PA_open_p2A", "PA_open_p2B", "PA_open_p3A", "PA_open_p3B", "PA_open_p1B", "PA_open_p3C", "PA_open_p4A", "PA_open_p5A", "PA_open_p6A", "PA_open_p6B", "PA_open_p7A", "PA_open_p7B", "RI_lockdownA", "RI_open_p1A", "RI_open_p2A", "RI_open_p3A", "RI_open_p2B", "RI_open_p1B", "RI_open_p2C", "RI_open_p2D", "RI_open_p3B", "RI_open_p4A", "RI_open_p5A", "RI_open_p6A", "RI_open_p5B", "RI_open_p7A", "SC_lockdownA", "SC_open_p1A", "SC_open_p2A", "SC_open_p3A", "SC_open_p3B", "SC_open_p4A", "SC_open_p4B", "SC_open_p5A", "SC_open_p5B", "SD_sdA", "SD_open_p4A", "TN_lockdownA", "TN_open_p1A", "TN_open_p2A", "TN_open_p2B", "TN_open_p2C", "TN_open_p3A", "TN_open_p3B", "TN_open_p4A", "TX_lockdownA", "TX_open_p1A", "TX_open_p2A", "TX_open_p2B", "TX_open_p1B", "TX_open_p2C", "TX_open_p3A", "TX_open_p4A", "UT_lockdownA", "UT_open_p1A", "UT_open_p2A", "UT_open_p3A", "UT_open_p3B", "UT_open_p2B", "UT_open_p3C", "UT_open_p4A", "UT_open_p4B", "UT_open_p5A", "UT_open_p5B", "VT_lockdownA", "VT_open_p1A", "VT_open_p2A", "VT_open_p3A", "VT_open_p3B", "VT_open_p2B", "VT_open_p2C", "VT_open_p4A", "VT_open_p5A", "VT_open_p6A", "VA_lockdownA", "VA_open_p1A", "VA_open_p2A", "VA_open_p3A", "VA_open_p2B", "VA_open_p3B", "VA_open_p3C", "VA_open_p2C", "VA_open_p4A", "VA_open_p4B", "VA_open_p5A", "VA_open_p5B", "WA_lockdownA", "WA_open_p1A", "WA_open_p2A", "WA_open_p2B", "WA_open_p2C", "WA_open_p1B", "WA_open_p2D", "WA_open_p3A", "WA_open_p4A", "WA_open_p5A", "WA_open_p6A", "WA_open_p6B", "WA_open_p7A", "WA_open_p8A", "WA_open_p9A", "WA_open_p9B", "WV_lockdownA", "WV_open_p1A", "WV_open_p2A", "WV_open_p3A", "WV_open_p4A", "WV_open_p2B", "WV_open_p3B", "WV_open_p3C", "WV_open_p3D", "WV_open_p4B", "WV_open_p5A", "WV_open_p6A", "WV_open_p6B", "WV_open_p6C", "WI_lockdownA", "WI_open_p1A", "WI_open_p2A", "WI_open_p2B", "WI_open_p1B", "WI_open_p3A", "WI_open_p3B", "WI_open_p4A", "WI_open_p5A", "WI_open_p5B", "WI_open_p5C", "WY_sdA", "WY_open_p1A", "WY_open_p2A", "WY_open_p3A", "WY_open_p4A", "WY_open_p3B", "WY_open_p2B", "WY_open_p2C", "WY_open_p3C", "WY_open_p5A", "WY_open_p5B", "WY_open_p6A", "WY_open_p6B"] seasonal: - template: Stacked + template: StackedModifier scenarios: ["Seas_jan", "Seas_feb", "Seas_mar", "Seas_may", "Seas_jun", "Seas_jul", "Seas_aug", "Seas_sep", "Seas_oct", "Seas_nov", "Seas_dec"] vaccination: - template: Stacked + template: StackedModifier scenarios: ["AL_Dose1_jan2021_age18to64", "AL_Dose1_jan2021_age65to100", "AL_Dose1_feb2021_age0to17", "AL_Dose1_feb2021_age18to64", "AL_Dose1_feb2021_age65to100", "AL_Dose1_mar2021_age0to17", "AL_Dose1_mar2021_age18to64", "AL_Dose1_mar2021_age65to100", "AL_Dose1_apr2021_age0to17", "AL_Dose1_apr2021_age18to64", "AL_Dose1_apr2021_age65to100", "AL_Dose1_may2021_age0to17", "AL_Dose1_may2021_age18to64", "AL_Dose1_may2021_age65to100", "AL_Dose1_jun2021_age0to17", "AL_Dose1_jun2021_age18to64", "AL_Dose1_jun2021_age65to100", "AL_Dose1_jul2021_age0to17", "AL_Dose1_jul2021_age18to64", "AL_Dose1_jul2021_age65to100", "AL_Dose1_aug2021_age0to17", "AL_Dose1_aug2021_age18to64", "AL_Dose1_aug2021_age65to100", "AL_Dose1_sep2021_age0to17", "AL_Dose1_sep2021_age18to64", "AL_Dose1_sep2021_age65to100", "AL_Dose1_oct2021_age0to17", "AL_Dose1_oct2021_age18to64", "AL_Dose1_oct2021_age65to100", "AL_Dose3_oct2021_0to17", "AL_Dose3_oct2021_18to64", "AL_Dose3_oct2021_65to100", "AL_Dose1_nov2021_age0to17", "AL_Dose1_nov2021_age18to64", "AL_Dose1_nov2021_age65to100", "AL_Dose3_nov2021_0to17", "AL_Dose3_nov2021_18to64", "AL_Dose3_nov2021_65to100", "AL_Dose1_dec2021_age0to17", "AL_Dose1_dec2021_age18to64", "AL_Dose1_dec2021_age65to100", "AL_Dose3_dec2021_0to17", "AL_Dose3_dec2021_18to64", "AL_Dose3_dec2021_65to100", "AL_Dose1_jan2022_age0to17", "AL_Dose1_jan2022_age18to64", "AL_Dose1_jan2022_age65to100", "AL_Dose3_jan2022_0to17", "AL_Dose3_jan2022_18to64", "AL_Dose3_jan2022_65to100", "AL_Dose1_feb2022_age0to17", "AL_Dose1_feb2022_age18to64", "AL_Dose1_feb2022_age65to100", "AL_Dose3_feb2022_0to17", "AL_Dose3_feb2022_18to64", "AL_Dose3_feb2022_65to100", "AL_Dose1_mar2022_age0to17", "AL_Dose1_mar2022_age18to64", "AL_Dose1_mar2022_age65to100", "AL_Dose3_mar2022_0to17", "AL_Dose3_mar2022_18to64", "AL_Dose3_mar2022_65to100", "AL_Dose1_apr2022_age0to17", "AL_Dose1_apr2022_age18to64", "AL_Dose1_apr2022_age65to100", "AL_Dose3_apr2022_0to17", "AL_Dose3_apr2022_18to64", "AL_Dose3_apr2022_65to100", "AL_Dose1_may2022_age0to17", "AL_Dose1_may2022_age18to64", "AL_Dose1_may2022_age65to100", "AL_Dose3_may2022_0to17", "AL_Dose3_may2022_18to64", "AL_Dose3_may2022_65to100", "AL_Dose1_jun2022_age0to17", "AL_Dose1_jun2022_age18to64", "AL_Dose1_jun2022_age65to100", "AL_Dose3_jun2022_0to17", "AL_Dose3_jun2022_18to64", "AL_Dose3_jun2022_65to100", "AL_Dose1_jul2022_age0to17", "AL_Dose1_jul2022_age18to64", "AL_Dose1_jul2022_age65to100", "AL_Dose3_jul2022_0to17", "AL_Dose3_jul2022_18to64", "AL_Dose3_jul2022_65to100", "AL_Dose1_aug2022_age0to17", "AL_Dose1_aug2022_age18to64", "AL_Dose1_aug2022_age65to100", "AL_Dose3_aug2022_0to17", "AL_Dose3_aug2022_18to64", "AL_Dose3_aug2022_65to100", "AL_Dose1_sep2022_age0to17", "AL_Dose1_sep2022_age18to64", "AL_Dose1_sep2022_age65to100", "AL_Dose3_sep2022_0to17", "AL_Dose3_sep2022_18to64", "AL_Dose3_sep2022_65to100", "AK_Dose1_jan2021_age18to64", "AK_Dose1_jan2021_age65to100", "AK_Dose1_feb2021_age0to17", "AK_Dose1_feb2021_age18to64", "AK_Dose1_feb2021_age65to100", "AK_Dose1_mar2021_age0to17", "AK_Dose1_mar2021_age18to64", "AK_Dose1_mar2021_age65to100", "AK_Dose1_apr2021_age0to17", "AK_Dose1_apr2021_age18to64", "AK_Dose1_apr2021_age65to100", "AK_Dose1_may2021_age0to17", "AK_Dose1_may2021_age18to64", "AK_Dose1_may2021_age65to100", "AK_Dose1_jun2021_age0to17", "AK_Dose1_jun2021_age18to64", "AK_Dose1_jun2021_age65to100", "AK_Dose1_jul2021_age0to17", "AK_Dose1_jul2021_age18to64", "AK_Dose1_jul2021_age65to100", "AK_Dose1_aug2021_age0to17", "AK_Dose1_aug2021_age18to64", "AK_Dose1_aug2021_age65to100", "AK_Dose1_sep2021_age0to17", "AK_Dose1_sep2021_age18to64", "AK_Dose1_sep2021_age65to100", "AK_Dose1_oct2021_age0to17", "AK_Dose1_oct2021_age18to64", "AK_Dose1_oct2021_age65to100", "AK_Dose3_oct2021_0to17", "AK_Dose3_oct2021_18to64", "AK_Dose3_oct2021_65to100", "AK_Dose1_nov2021_age0to17", "AK_Dose1_nov2021_age18to64", "AK_Dose1_nov2021_age65to100", "AK_Dose3_nov2021_0to17", "AK_Dose3_nov2021_18to64", "AK_Dose3_nov2021_65to100", "AK_Dose1_dec2021_age0to17", "AK_Dose1_dec2021_age18to64", "AK_Dose1_dec2021_age65to100", "AK_Dose3_dec2021_0to17", "AK_Dose3_dec2021_18to64", "AK_Dose3_dec2021_65to100", "AK_Dose1_jan2022_age0to17", "AK_Dose1_jan2022_age18to64", "AK_Dose1_jan2022_age65to100", "AK_Dose3_jan2022_0to17", "AK_Dose3_jan2022_18to64", "AK_Dose3_jan2022_65to100", "AK_Dose1_feb2022_age0to17", "AK_Dose1_feb2022_age18to64", "AK_Dose1_feb2022_age65to100", "AK_Dose3_feb2022_0to17", "AK_Dose3_feb2022_18to64", "AK_Dose3_feb2022_65to100", "AK_Dose1_mar2022_age0to17", "AK_Dose1_mar2022_age18to64", "AK_Dose1_mar2022_age65to100", "AK_Dose3_mar2022_0to17", "AK_Dose3_mar2022_18to64", "AK_Dose3_mar2022_65to100", "AK_Dose1_apr2022_age0to17", "AK_Dose1_apr2022_age18to64", "AK_Dose1_apr2022_age65to100", "AK_Dose3_apr2022_0to17", "AK_Dose3_apr2022_18to64", "AK_Dose3_apr2022_65to100", "AK_Dose1_may2022_age0to17", "AK_Dose1_may2022_age18to64", "AK_Dose1_may2022_age65to100", "AK_Dose3_may2022_0to17", "AK_Dose3_may2022_18to64", "AK_Dose3_may2022_65to100", "AK_Dose1_jun2022_age0to17", "AK_Dose1_jun2022_age18to64", "AK_Dose1_jun2022_age65to100", "AK_Dose3_jun2022_0to17", "AK_Dose3_jun2022_18to64", "AK_Dose3_jun2022_65to100", "AK_Dose1_jul2022_age0to17", "AK_Dose1_jul2022_age18to64", "AK_Dose1_jul2022_age65to100", "AK_Dose3_jul2022_0to17", "AK_Dose3_jul2022_18to64", "AK_Dose3_jul2022_65to100", "AK_Dose1_aug2022_age0to17", "AK_Dose1_aug2022_age18to64", "AK_Dose1_aug2022_age65to100", "AK_Dose3_aug2022_0to17", "AK_Dose3_aug2022_18to64", "AK_Dose3_aug2022_65to100", "AK_Dose1_sep2022_age0to17", "AK_Dose1_sep2022_age18to64", "AK_Dose1_sep2022_age65to100", "AK_Dose3_sep2022_0to17", "AK_Dose3_sep2022_18to64", "AK_Dose3_sep2022_65to100", "AZ_Dose1_jan2021_age18to64", "AZ_Dose1_jan2021_age65to100", "AZ_Dose1_feb2021_age0to17", "AZ_Dose1_feb2021_age18to64", "AZ_Dose1_feb2021_age65to100", "AZ_Dose1_mar2021_age0to17", "AZ_Dose1_mar2021_age18to64", "AZ_Dose1_mar2021_age65to100", "AZ_Dose1_apr2021_age0to17", "AZ_Dose1_apr2021_age18to64", "AZ_Dose1_apr2021_age65to100", "AZ_Dose1_may2021_age0to17", "AZ_Dose1_may2021_age18to64", "AZ_Dose1_may2021_age65to100", "AZ_Dose1_jun2021_age0to17", "AZ_Dose1_jun2021_age18to64", "AZ_Dose1_jun2021_age65to100", "AZ_Dose1_jul2021_age0to17", "AZ_Dose1_jul2021_age18to64", "AZ_Dose1_jul2021_age65to100", "AZ_Dose1_aug2021_age0to17", "AZ_Dose1_aug2021_age18to64", "AZ_Dose1_aug2021_age65to100", "AZ_Dose1_sep2021_age0to17", "AZ_Dose1_sep2021_age18to64", "AZ_Dose1_sep2021_age65to100", "AZ_Dose1_oct2021_age0to17", "AZ_Dose1_oct2021_age18to64", "AZ_Dose1_oct2021_age65to100", "AZ_Dose3_oct2021_0to17", "AZ_Dose3_oct2021_18to64", "AZ_Dose3_oct2021_65to100", "AZ_Dose1_nov2021_age0to17", "AZ_Dose1_nov2021_age18to64", "AZ_Dose1_nov2021_age65to100", "AZ_Dose3_nov2021_0to17", "AZ_Dose3_nov2021_18to64", "AZ_Dose3_nov2021_65to100", "AZ_Dose1_dec2021_age0to17", "AZ_Dose1_dec2021_age18to64", "AZ_Dose1_dec2021_age65to100", "AZ_Dose3_dec2021_0to17", "AZ_Dose3_dec2021_18to64", "AZ_Dose3_dec2021_65to100", "AZ_Dose1_jan2022_age0to17", "AZ_Dose1_jan2022_age18to64", "AZ_Dose1_jan2022_age65to100", "AZ_Dose3_jan2022_0to17", "AZ_Dose3_jan2022_18to64", "AZ_Dose3_jan2022_65to100", "AZ_Dose1_feb2022_age0to17", "AZ_Dose1_feb2022_age18to64", "AZ_Dose1_feb2022_age65to100", "AZ_Dose3_feb2022_0to17", "AZ_Dose3_feb2022_18to64", "AZ_Dose3_feb2022_65to100", "AZ_Dose1_mar2022_age0to17", "AZ_Dose1_mar2022_age18to64", "AZ_Dose1_mar2022_age65to100", "AZ_Dose3_mar2022_0to17", "AZ_Dose3_mar2022_18to64", "AZ_Dose3_mar2022_65to100", "AZ_Dose1_apr2022_age0to17", "AZ_Dose1_apr2022_age18to64", "AZ_Dose1_apr2022_age65to100", "AZ_Dose3_apr2022_0to17", "AZ_Dose3_apr2022_18to64", "AZ_Dose3_apr2022_65to100", "AZ_Dose1_may2022_age0to17", "AZ_Dose1_may2022_age18to64", "AZ_Dose1_may2022_age65to100", "AZ_Dose3_may2022_0to17", "AZ_Dose3_may2022_18to64", "AZ_Dose3_may2022_65to100", "AZ_Dose1_jun2022_age0to17", "AZ_Dose1_jun2022_age18to64", "AZ_Dose1_jun2022_age65to100", "AZ_Dose3_jun2022_0to17", "AZ_Dose3_jun2022_18to64", "AZ_Dose3_jun2022_65to100", "AZ_Dose1_jul2022_age0to17", "AZ_Dose1_jul2022_age18to64", "AZ_Dose1_jul2022_age65to100", "AZ_Dose3_jul2022_0to17", "AZ_Dose3_jul2022_18to64", "AZ_Dose3_jul2022_65to100", "AZ_Dose1_aug2022_age0to17", "AZ_Dose1_aug2022_age18to64", "AZ_Dose1_aug2022_age65to100", "AZ_Dose3_aug2022_0to17", "AZ_Dose3_aug2022_18to64", "AZ_Dose3_aug2022_65to100", "AZ_Dose1_sep2022_age0to17", "AZ_Dose1_sep2022_age18to64", "AZ_Dose1_sep2022_age65to100", "AZ_Dose3_sep2022_0to17", "AZ_Dose3_sep2022_18to64", "AZ_Dose3_sep2022_65to100", "AR_Dose1_jan2021_age18to64", "AR_Dose1_jan2021_age65to100", "AR_Dose1_feb2021_age0to17", "AR_Dose1_feb2021_age18to64", "AR_Dose1_feb2021_age65to100", "AR_Dose1_mar2021_age0to17", "AR_Dose1_mar2021_age18to64", "AR_Dose1_mar2021_age65to100", "AR_Dose1_apr2021_age0to17", "AR_Dose1_apr2021_age18to64", "AR_Dose1_apr2021_age65to100", "AR_Dose1_may2021_age0to17", "AR_Dose1_may2021_age18to64", "AR_Dose1_may2021_age65to100", "AR_Dose1_jun2021_age0to17", "AR_Dose1_jun2021_age18to64", "AR_Dose1_jun2021_age65to100", "AR_Dose1_jul2021_age0to17", "AR_Dose1_jul2021_age18to64", "AR_Dose1_jul2021_age65to100", "AR_Dose1_aug2021_age0to17", "AR_Dose1_aug2021_age18to64", "AR_Dose1_aug2021_age65to100", "AR_Dose1_sep2021_age0to17", "AR_Dose1_sep2021_age18to64", "AR_Dose1_sep2021_age65to100", "AR_Dose1_oct2021_age0to17", "AR_Dose1_oct2021_age18to64", "AR_Dose1_oct2021_age65to100", "AR_Dose3_oct2021_0to17", "AR_Dose3_oct2021_18to64", "AR_Dose3_oct2021_65to100", "AR_Dose1_nov2021_age0to17", "AR_Dose1_nov2021_age18to64", "AR_Dose1_nov2021_age65to100", "AR_Dose3_nov2021_0to17", "AR_Dose3_nov2021_18to64", "AR_Dose3_nov2021_65to100", "AR_Dose1_dec2021_age0to17", "AR_Dose1_dec2021_age18to64", "AR_Dose1_dec2021_age65to100", "AR_Dose3_dec2021_0to17", "AR_Dose3_dec2021_18to64", "AR_Dose3_dec2021_65to100", "AR_Dose1_jan2022_age0to17", "AR_Dose1_jan2022_age18to64", "AR_Dose1_jan2022_age65to100", "AR_Dose3_jan2022_0to17", "AR_Dose3_jan2022_18to64", "AR_Dose3_jan2022_65to100", "AR_Dose1_feb2022_age0to17", "AR_Dose1_feb2022_age18to64", "AR_Dose1_feb2022_age65to100", "AR_Dose3_feb2022_0to17", "AR_Dose3_feb2022_18to64", "AR_Dose3_feb2022_65to100", "AR_Dose1_mar2022_age0to17", "AR_Dose1_mar2022_age18to64", "AR_Dose1_mar2022_age65to100", "AR_Dose3_mar2022_0to17", "AR_Dose3_mar2022_18to64", "AR_Dose3_mar2022_65to100", "AR_Dose1_apr2022_age0to17", "AR_Dose1_apr2022_age18to64", "AR_Dose1_apr2022_age65to100", "AR_Dose3_apr2022_0to17", "AR_Dose3_apr2022_18to64", "AR_Dose3_apr2022_65to100", "AR_Dose1_may2022_age0to17", "AR_Dose1_may2022_age18to64", "AR_Dose1_may2022_age65to100", "AR_Dose3_may2022_0to17", "AR_Dose3_may2022_18to64", "AR_Dose3_may2022_65to100", "AR_Dose1_jun2022_age0to17", "AR_Dose1_jun2022_age18to64", "AR_Dose1_jun2022_age65to100", "AR_Dose3_jun2022_0to17", "AR_Dose3_jun2022_18to64", "AR_Dose3_jun2022_65to100", "AR_Dose1_jul2022_age0to17", "AR_Dose1_jul2022_age18to64", "AR_Dose1_jul2022_age65to100", "AR_Dose3_jul2022_0to17", "AR_Dose3_jul2022_18to64", "AR_Dose3_jul2022_65to100", "AR_Dose1_aug2022_age0to17", "AR_Dose1_aug2022_age18to64", "AR_Dose1_aug2022_age65to100", "AR_Dose3_aug2022_0to17", "AR_Dose3_aug2022_18to64", "AR_Dose3_aug2022_65to100", "AR_Dose1_sep2022_age0to17", "AR_Dose1_sep2022_age18to64", "AR_Dose1_sep2022_age65to100", "AR_Dose3_sep2022_0to17", "AR_Dose3_sep2022_18to64", "AR_Dose3_sep2022_65to100", "CA_Dose1_jan2021_age18to64", "CA_Dose1_jan2021_age65to100", "CA_Dose1_feb2021_age18to64", "CA_Dose1_feb2021_age65to100", "CA_Dose1_mar2021_age0to17", "CA_Dose1_mar2021_age18to64", "CA_Dose1_mar2021_age65to100", "CA_Dose1_apr2021_age0to17", "CA_Dose1_apr2021_age18to64", "CA_Dose1_apr2021_age65to100", "CA_Dose1_may2021_age0to17", "CA_Dose1_may2021_age18to64", "CA_Dose1_may2021_age65to100", "CA_Dose1_jun2021_age0to17", "CA_Dose1_jun2021_age18to64", "CA_Dose1_jun2021_age65to100", "CA_Dose1_jul2021_age0to17", "CA_Dose1_jul2021_age18to64", "CA_Dose1_jul2021_age65to100", "CA_Dose1_aug2021_age0to17", "CA_Dose1_aug2021_age18to64", "CA_Dose1_aug2021_age65to100", "CA_Dose1_sep2021_age0to17", "CA_Dose1_sep2021_age18to64", "CA_Dose1_sep2021_age65to100", "CA_Dose1_oct2021_age0to17", "CA_Dose1_oct2021_age18to64", "CA_Dose1_oct2021_age65to100", "CA_Dose3_oct2021_0to17", "CA_Dose3_oct2021_18to64", "CA_Dose3_oct2021_65to100", "CA_Dose1_nov2021_age0to17", "CA_Dose1_nov2021_age18to64", "CA_Dose1_nov2021_age65to100", "CA_Dose3_nov2021_0to17", "CA_Dose3_nov2021_18to64", "CA_Dose3_nov2021_65to100", "CA_Dose1_dec2021_age0to17", "CA_Dose1_dec2021_age18to64", "CA_Dose1_dec2021_age65to100", "CA_Dose3_dec2021_0to17", "CA_Dose3_dec2021_18to64", "CA_Dose3_dec2021_65to100", "CA_Dose1_jan2022_age0to17", "CA_Dose1_jan2022_age18to64", "CA_Dose1_jan2022_age65to100", "CA_Dose3_jan2022_0to17", "CA_Dose3_jan2022_18to64", "CA_Dose3_jan2022_65to100", "CA_Dose1_feb2022_age0to17", "CA_Dose1_feb2022_age18to64", "CA_Dose1_feb2022_age65to100", "CA_Dose3_feb2022_0to17", "CA_Dose3_feb2022_18to64", "CA_Dose3_feb2022_65to100", "CA_Dose1_mar2022_age0to17", "CA_Dose1_mar2022_age18to64", "CA_Dose1_mar2022_age65to100", "CA_Dose3_mar2022_0to17", "CA_Dose3_mar2022_18to64", "CA_Dose3_mar2022_65to100", "CA_Dose1_apr2022_age0to17", "CA_Dose1_apr2022_age18to64", "CA_Dose1_apr2022_age65to100", "CA_Dose3_apr2022_0to17", "CA_Dose3_apr2022_18to64", "CA_Dose3_apr2022_65to100", "CA_Dose1_may2022_age0to17", "CA_Dose1_may2022_age18to64", "CA_Dose1_may2022_age65to100", "CA_Dose3_may2022_0to17", "CA_Dose3_may2022_18to64", "CA_Dose3_may2022_65to100", "CA_Dose1_jun2022_age0to17", "CA_Dose1_jun2022_age18to64", "CA_Dose1_jun2022_age65to100", "CA_Dose3_jun2022_0to17", "CA_Dose3_jun2022_18to64", "CA_Dose3_jun2022_65to100", "CA_Dose1_jul2022_age0to17", "CA_Dose1_jul2022_age18to64", "CA_Dose1_jul2022_age65to100", "CA_Dose3_jul2022_0to17", "CA_Dose3_jul2022_18to64", "CA_Dose3_jul2022_65to100", "CA_Dose1_aug2022_age0to17", "CA_Dose1_aug2022_age18to64", "CA_Dose1_aug2022_age65to100", "CA_Dose3_aug2022_0to17", "CA_Dose3_aug2022_18to64", "CA_Dose1_sep2022_age0to17", "CA_Dose1_sep2022_age18to64", "CA_Dose1_sep2022_age65to100", "CA_Dose3_sep2022_0to17", "CA_Dose3_sep2022_18to64", "CA_Dose3_sep2022_65to100", "CO_Dose1_jan2021_age18to64", "CO_Dose1_jan2021_age65to100", "CO_Dose1_feb2021_age0to17", "CO_Dose1_feb2021_age18to64", "CO_Dose1_feb2021_age65to100", "CO_Dose1_mar2021_age0to17", "CO_Dose1_mar2021_age18to64", "CO_Dose1_mar2021_age65to100", "CO_Dose1_apr2021_age0to17", "CO_Dose1_apr2021_age18to64", "CO_Dose1_apr2021_age65to100", "CO_Dose1_may2021_age0to17", "CO_Dose1_may2021_age18to64", "CO_Dose1_may2021_age65to100", "CO_Dose1_jun2021_age0to17", "CO_Dose1_jun2021_age18to64", "CO_Dose1_jun2021_age65to100", "CO_Dose1_jul2021_age0to17", "CO_Dose1_jul2021_age18to64", "CO_Dose1_jul2021_age65to100", "CO_Dose1_aug2021_age0to17", "CO_Dose1_aug2021_age18to64", "CO_Dose1_aug2021_age65to100", "CO_Dose1_sep2021_age0to17", "CO_Dose1_sep2021_age18to64", "CO_Dose1_sep2021_age65to100", "CO_Dose1_oct2021_age0to17", "CO_Dose1_oct2021_age18to64", "CO_Dose1_oct2021_age65to100", "CO_Dose3_oct2021_0to17", "CO_Dose3_oct2021_18to64", "CO_Dose3_oct2021_65to100", "CO_Dose1_nov2021_age0to17", "CO_Dose1_nov2021_age18to64", "CO_Dose1_nov2021_age65to100", "CO_Dose3_nov2021_0to17", "CO_Dose3_nov2021_18to64", "CO_Dose3_nov2021_65to100", "CO_Dose1_dec2021_age0to17", "CO_Dose1_dec2021_age18to64", "CO_Dose1_dec2021_age65to100", "CO_Dose3_dec2021_0to17", "CO_Dose3_dec2021_18to64", "CO_Dose3_dec2021_65to100", "CO_Dose1_jan2022_age0to17", "CO_Dose1_jan2022_age18to64", "CO_Dose1_jan2022_age65to100", "CO_Dose3_jan2022_0to17", "CO_Dose3_jan2022_18to64", "CO_Dose3_jan2022_65to100", "CO_Dose1_feb2022_age0to17", "CO_Dose1_feb2022_age18to64", "CO_Dose1_feb2022_age65to100", "CO_Dose3_feb2022_0to17", "CO_Dose3_feb2022_18to64", "CO_Dose3_feb2022_65to100", "CO_Dose1_mar2022_age0to17", "CO_Dose1_mar2022_age18to64", "CO_Dose1_mar2022_age65to100", "CO_Dose3_mar2022_0to17", "CO_Dose3_mar2022_18to64", "CO_Dose3_mar2022_65to100", "CO_Dose1_apr2022_age0to17", "CO_Dose1_apr2022_age18to64", "CO_Dose1_apr2022_age65to100", "CO_Dose3_apr2022_0to17", "CO_Dose3_apr2022_18to64", "CO_Dose3_apr2022_65to100", "CO_Dose1_may2022_age0to17", "CO_Dose1_may2022_age18to64", "CO_Dose1_may2022_age65to100", "CO_Dose3_may2022_0to17", "CO_Dose3_may2022_18to64", "CO_Dose3_may2022_65to100", "CO_Dose1_jun2022_age0to17", "CO_Dose1_jun2022_age18to64", "CO_Dose1_jun2022_age65to100", "CO_Dose3_jun2022_0to17", "CO_Dose3_jun2022_18to64", "CO_Dose3_jun2022_65to100", "CO_Dose1_jul2022_age0to17", "CO_Dose1_jul2022_age18to64", "CO_Dose1_jul2022_age65to100", "CO_Dose3_jul2022_0to17", "CO_Dose3_jul2022_18to64", "CO_Dose3_jul2022_65to100", "CO_Dose1_aug2022_age0to17", "CO_Dose1_aug2022_age18to64", "CO_Dose1_aug2022_age65to100", "CO_Dose3_aug2022_0to17", "CO_Dose3_aug2022_18to64", "CO_Dose3_aug2022_65to100", "CO_Dose1_sep2022_age0to17", "CO_Dose1_sep2022_age18to64", "CO_Dose1_sep2022_age65to100", "CO_Dose3_sep2022_0to17", "CO_Dose3_sep2022_18to64", "CO_Dose3_sep2022_65to100", "CT_Dose1_jan2021_age18to64", "CT_Dose1_jan2021_age65to100", "CT_Dose1_feb2021_age0to17", "CT_Dose1_feb2021_age18to64", "CT_Dose1_feb2021_age65to100", "CT_Dose1_mar2021_age0to17", "CT_Dose1_mar2021_age18to64", "CT_Dose1_mar2021_age65to100", "CT_Dose1_apr2021_age0to17", "CT_Dose1_apr2021_age18to64", "CT_Dose1_apr2021_age65to100", "CT_Dose1_may2021_age0to17", "CT_Dose1_may2021_age18to64", "CT_Dose1_may2021_age65to100", "CT_Dose1_jun2021_age0to17", "CT_Dose1_jun2021_age18to64", "CT_Dose1_jun2021_age65to100", "CT_Dose1_jul2021_age0to17", "CT_Dose1_jul2021_age18to64", "CT_Dose1_jul2021_age65to100", "CT_Dose1_aug2021_age0to17", "CT_Dose1_aug2021_age18to64", "CT_Dose1_aug2021_age65to100", "CT_Dose1_sep2021_age0to17", "CT_Dose1_sep2021_age18to64", "CT_Dose1_sep2021_age65to100", "CT_Dose1_oct2021_age0to17", "CT_Dose1_oct2021_age18to64", "CT_Dose1_oct2021_age65to100", "CT_Dose3_oct2021_0to17", "CT_Dose3_oct2021_18to64", "CT_Dose3_oct2021_65to100", "CT_Dose1_nov2021_age0to17", "CT_Dose1_nov2021_age18to64", "CT_Dose1_nov2021_age65to100", "CT_Dose3_nov2021_0to17", "CT_Dose3_nov2021_18to64", "CT_Dose3_nov2021_65to100", "CT_Dose1_dec2021_age0to17", "CT_Dose1_dec2021_age18to64", "CT_Dose1_dec2021_age65to100", "CT_Dose3_dec2021_0to17", "CT_Dose3_dec2021_18to64", "CT_Dose3_dec2021_65to100", "CT_Dose1_jan2022_age0to17", "CT_Dose1_jan2022_age18to64", "CT_Dose1_jan2022_age65to100", "CT_Dose3_jan2022_0to17", "CT_Dose3_jan2022_18to64", "CT_Dose3_jan2022_65to100", "CT_Dose1_feb2022_age0to17", "CT_Dose1_feb2022_age18to64", "CT_Dose1_feb2022_age65to100", "CT_Dose3_feb2022_0to17", "CT_Dose3_feb2022_18to64", "CT_Dose3_feb2022_65to100", "CT_Dose1_mar2022_age0to17", "CT_Dose1_mar2022_age18to64", "CT_Dose1_mar2022_age65to100", "CT_Dose3_mar2022_0to17", "CT_Dose3_mar2022_18to64", "CT_Dose3_mar2022_65to100", "CT_Dose1_apr2022_age0to17", "CT_Dose1_apr2022_age18to64", "CT_Dose1_apr2022_age65to100", "CT_Dose3_apr2022_0to17", "CT_Dose3_apr2022_18to64", "CT_Dose3_apr2022_65to100", "CT_Dose1_may2022_age0to17", "CT_Dose1_may2022_age18to64", "CT_Dose1_may2022_age65to100", "CT_Dose3_may2022_0to17", "CT_Dose3_may2022_18to64", "CT_Dose3_may2022_65to100", "CT_Dose1_jun2022_age0to17", "CT_Dose1_jun2022_age18to64", "CT_Dose1_jun2022_age65to100", "CT_Dose3_jun2022_0to17", "CT_Dose3_jun2022_18to64", "CT_Dose3_jun2022_65to100", "CT_Dose1_jul2022_age0to17", "CT_Dose1_jul2022_age18to64", "CT_Dose1_jul2022_age65to100", "CT_Dose3_jul2022_0to17", "CT_Dose3_jul2022_18to64", "CT_Dose3_jul2022_65to100", "CT_Dose1_aug2022_age0to17", "CT_Dose1_aug2022_age18to64", "CT_Dose3_aug2022_0to17", "CT_Dose3_aug2022_18to64", "CT_Dose1_sep2022_age0to17", "CT_Dose1_sep2022_age18to64", "CT_Dose1_sep2022_age65to100", "CT_Dose3_sep2022_0to17", "CT_Dose3_sep2022_18to64", "CT_Dose3_sep2022_65to100", "DE_Dose1_jan2021_age18to64", "DE_Dose1_jan2021_age65to100", "DE_Dose1_feb2021_age18to64", "DE_Dose1_feb2021_age65to100", "DE_Dose1_mar2021_age18to64", "DE_Dose1_mar2021_age65to100", "DE_Dose1_apr2021_age0to17", "DE_Dose1_apr2021_age18to64", "DE_Dose1_apr2021_age65to100", "DE_Dose1_may2021_age0to17", "DE_Dose1_may2021_age18to64", "DE_Dose1_may2021_age65to100", "DE_Dose1_jun2021_age0to17", "DE_Dose1_jun2021_age18to64", "DE_Dose1_jun2021_age65to100", "DE_Dose1_jul2021_age0to17", "DE_Dose1_jul2021_age18to64", "DE_Dose1_jul2021_age65to100", "DE_Dose1_aug2021_age0to17", "DE_Dose1_aug2021_age18to64", "DE_Dose1_aug2021_age65to100", "DE_Dose1_sep2021_age0to17", "DE_Dose1_sep2021_age18to64", "DE_Dose1_sep2021_age65to100", "DE_Dose1_oct2021_age0to17", "DE_Dose1_oct2021_age18to64", "DE_Dose1_oct2021_age65to100", "DE_Dose3_oct2021_18to64", "DE_Dose3_oct2021_65to100", "DE_Dose1_nov2021_age0to17", "DE_Dose1_nov2021_age18to64", "DE_Dose1_nov2021_age65to100", "DE_Dose3_nov2021_0to17", "DE_Dose3_nov2021_18to64", "DE_Dose3_nov2021_65to100", "DE_Dose1_dec2021_age0to17", "DE_Dose1_dec2021_age18to64", "DE_Dose1_dec2021_age65to100", "DE_Dose3_dec2021_0to17", "DE_Dose3_dec2021_18to64", "DE_Dose3_dec2021_65to100", "DE_Dose1_jan2022_age0to17", "DE_Dose1_jan2022_age18to64", "DE_Dose1_jan2022_age65to100", "DE_Dose3_jan2022_0to17", "DE_Dose3_jan2022_18to64", "DE_Dose3_jan2022_65to100", "DE_Dose1_feb2022_age0to17", "DE_Dose1_feb2022_age18to64", "DE_Dose1_feb2022_age65to100", "DE_Dose3_feb2022_0to17", "DE_Dose3_feb2022_18to64", "DE_Dose3_feb2022_65to100", "DE_Dose1_mar2022_age0to17", "DE_Dose1_mar2022_age18to64", "DE_Dose1_mar2022_age65to100", "DE_Dose3_mar2022_0to17", "DE_Dose3_mar2022_18to64", "DE_Dose3_mar2022_65to100", "DE_Dose1_apr2022_age0to17", "DE_Dose1_apr2022_age18to64", "DE_Dose1_apr2022_age65to100", "DE_Dose3_apr2022_0to17", "DE_Dose3_apr2022_18to64", "DE_Dose3_apr2022_65to100", "DE_Dose1_may2022_age0to17", "DE_Dose1_may2022_age18to64", "DE_Dose1_may2022_age65to100", "DE_Dose3_may2022_0to17", "DE_Dose3_may2022_18to64", "DE_Dose3_may2022_65to100", "DE_Dose1_jun2022_age0to17", "DE_Dose1_jun2022_age18to64", "DE_Dose3_jun2022_0to17", "DE_Dose3_jun2022_18to64", "DE_Dose3_jun2022_65to100", "DE_Dose1_jul2022_age0to17", "DE_Dose1_jul2022_age18to64", "DE_Dose1_jul2022_age65to100", "DE_Dose3_jul2022_0to17", "DE_Dose3_jul2022_18to64", "DE_Dose3_jul2022_65to100", "DE_Dose1_aug2022_age0to17", "DE_Dose1_aug2022_age18to64", "DE_Dose3_aug2022_0to17", "DE_Dose3_aug2022_18to64", "DE_Dose1_sep2022_age0to17", "DE_Dose1_sep2022_age18to64", "DE_Dose3_sep2022_0to17", "DE_Dose3_sep2022_18to64", "DC_Dose1_jan2021_age18to64", "DC_Dose1_jan2021_age65to100", "DC_Dose1_feb2021_age0to17", "DC_Dose1_feb2021_age18to64", "DC_Dose1_feb2021_age65to100", "DC_Dose1_mar2021_age0to17", "DC_Dose1_mar2021_age18to64", "DC_Dose1_mar2021_age65to100", "DC_Dose1_apr2021_age0to17", "DC_Dose1_apr2021_age18to64", "DC_Dose1_apr2021_age65to100", "DC_Dose1_may2021_age0to17", "DC_Dose1_may2021_age18to64", "DC_Dose1_may2021_age65to100", "DC_Dose1_jun2021_age0to17", "DC_Dose1_jun2021_age18to64", "DC_Dose1_jun2021_age65to100", "DC_Dose1_jul2021_age0to17", "DC_Dose1_jul2021_age18to64", "DC_Dose1_jul2021_age65to100", "DC_Dose1_aug2021_age0to17", "DC_Dose1_aug2021_age18to64", "DC_Dose1_aug2021_age65to100", "DC_Dose1_sep2021_age0to17", "DC_Dose1_sep2021_age18to64", "DC_Dose1_sep2021_age65to100", "DC_Dose1_oct2021_age0to17", "DC_Dose1_oct2021_age18to64", "DC_Dose1_oct2021_age65to100", "DC_Dose3_oct2021_0to17", "DC_Dose3_oct2021_18to64", "DC_Dose3_oct2021_65to100", "DC_Dose1_nov2021_age0to17", "DC_Dose1_nov2021_age18to64", "DC_Dose1_nov2021_age65to100", "DC_Dose3_nov2021_0to17", "DC_Dose3_nov2021_18to64", "DC_Dose3_nov2021_65to100", "DC_Dose1_dec2021_age0to17", "DC_Dose1_dec2021_age18to64", "DC_Dose1_dec2021_age65to100", "DC_Dose3_dec2021_0to17", "DC_Dose3_dec2021_18to64", "DC_Dose3_dec2021_65to100", "DC_Dose1_jan2022_age0to17", "DC_Dose1_jan2022_age18to64", "DC_Dose1_jan2022_age65to100", "DC_Dose3_jan2022_0to17", "DC_Dose3_jan2022_18to64", "DC_Dose3_jan2022_65to100", "DC_Dose1_feb2022_age0to17", "DC_Dose1_feb2022_age18to64", "DC_Dose1_feb2022_age65to100", "DC_Dose3_feb2022_0to17", "DC_Dose3_feb2022_18to64", "DC_Dose3_feb2022_65to100", "DC_Dose1_mar2022_age0to17", "DC_Dose1_mar2022_age18to64", "DC_Dose1_mar2022_age65to100", "DC_Dose3_mar2022_0to17", "DC_Dose3_mar2022_18to64", "DC_Dose3_mar2022_65to100", "DC_Dose1_apr2022_age0to17", "DC_Dose1_apr2022_age18to64", "DC_Dose1_apr2022_age65to100", "DC_Dose3_apr2022_0to17", "DC_Dose3_apr2022_18to64", "DC_Dose3_apr2022_65to100", "DC_Dose1_may2022_age0to17", "DC_Dose1_may2022_age18to64", "DC_Dose1_may2022_age65to100", "DC_Dose3_may2022_0to17", "DC_Dose3_may2022_18to64", "DC_Dose3_may2022_65to100", "DC_Dose1_jun2022_age0to17", "DC_Dose1_jun2022_age18to64", "DC_Dose3_jun2022_0to17", "DC_Dose3_jun2022_18to64", "DC_Dose1_jul2022_age0to17", "DC_Dose1_jul2022_age18to64", "DC_Dose3_jul2022_0to17", "DC_Dose3_jul2022_18to64", "DC_Dose1_aug2022_age0to17", "DC_Dose1_aug2022_age18to64", "DC_Dose3_aug2022_0to17", "DC_Dose3_aug2022_18to64", "DC_Dose1_sep2022_age0to17", "DC_Dose3_sep2022_0to17", "DC_Dose3_sep2022_18to64", "FL_Dose1_jan2021_age18to64", "FL_Dose1_jan2021_age65to100", "FL_Dose1_feb2021_age0to17", "FL_Dose1_feb2021_age18to64", "FL_Dose1_feb2021_age65to100", "FL_Dose1_mar2021_age0to17", "FL_Dose1_mar2021_age18to64", "FL_Dose1_mar2021_age65to100", "FL_Dose1_apr2021_age0to17", "FL_Dose1_apr2021_age18to64", "FL_Dose1_apr2021_age65to100", "FL_Dose1_may2021_age0to17", "FL_Dose1_may2021_age18to64", "FL_Dose1_may2021_age65to100", "FL_Dose1_jun2021_age0to17", "FL_Dose1_jun2021_age18to64", "FL_Dose1_jun2021_age65to100", "FL_Dose1_jul2021_age0to17", "FL_Dose1_jul2021_age18to64", "FL_Dose1_jul2021_age65to100", "FL_Dose1_aug2021_age0to17", "FL_Dose1_aug2021_age18to64", "FL_Dose1_aug2021_age65to100", "FL_Dose1_sep2021_age0to17", "FL_Dose1_sep2021_age18to64", "FL_Dose1_sep2021_age65to100", "FL_Dose1_oct2021_age0to17", "FL_Dose1_oct2021_age18to64", "FL_Dose1_oct2021_age65to100", "FL_Dose3_oct2021_0to17", "FL_Dose3_oct2021_18to64", "FL_Dose3_oct2021_65to100", "FL_Dose1_nov2021_age0to17", "FL_Dose1_nov2021_age18to64", "FL_Dose1_nov2021_age65to100", "FL_Dose3_nov2021_0to17", "FL_Dose3_nov2021_18to64", "FL_Dose3_nov2021_65to100", "FL_Dose1_dec2021_age0to17", "FL_Dose1_dec2021_age18to64", "FL_Dose1_dec2021_age65to100", "FL_Dose3_dec2021_0to17", "FL_Dose3_dec2021_18to64", "FL_Dose3_dec2021_65to100", "FL_Dose1_jan2022_age0to17", "FL_Dose1_jan2022_age18to64", "FL_Dose1_jan2022_age65to100", "FL_Dose3_jan2022_0to17", "FL_Dose3_jan2022_18to64", "FL_Dose3_jan2022_65to100", "FL_Dose1_feb2022_age0to17", "FL_Dose1_feb2022_age18to64", "FL_Dose1_feb2022_age65to100", "FL_Dose3_feb2022_0to17", "FL_Dose3_feb2022_18to64", "FL_Dose3_feb2022_65to100", "FL_Dose1_mar2022_age0to17", "FL_Dose1_mar2022_age18to64", "FL_Dose1_mar2022_age65to100", "FL_Dose3_mar2022_0to17", "FL_Dose3_mar2022_18to64", "FL_Dose3_mar2022_65to100", "FL_Dose1_apr2022_age0to17", "FL_Dose1_apr2022_age18to64", "FL_Dose1_apr2022_age65to100", "FL_Dose3_apr2022_0to17", "FL_Dose3_apr2022_18to64", "FL_Dose3_apr2022_65to100", "FL_Dose1_may2022_age0to17", "FL_Dose1_may2022_age18to64", "FL_Dose1_may2022_age65to100", "FL_Dose3_may2022_0to17", "FL_Dose3_may2022_18to64", "FL_Dose3_may2022_65to100", "FL_Dose1_jun2022_age0to17", "FL_Dose1_jun2022_age18to64", "FL_Dose1_jun2022_age65to100", "FL_Dose3_jun2022_0to17", "FL_Dose3_jun2022_18to64", "FL_Dose3_jun2022_65to100", "FL_Dose1_jul2022_age0to17", "FL_Dose1_jul2022_age65to100", "FL_Dose3_jul2022_0to17", "FL_Dose3_jul2022_18to64", "FL_Dose3_jul2022_65to100", "FL_Dose1_aug2022_age0to17", "FL_Dose1_aug2022_age65to100", "FL_Dose3_aug2022_0to17", "FL_Dose3_aug2022_18to64", "FL_Dose3_aug2022_65to100", "FL_Dose1_sep2022_age0to17", "FL_Dose1_sep2022_age65to100", "FL_Dose3_sep2022_0to17", "FL_Dose3_sep2022_18to64", "FL_Dose3_sep2022_65to100", "GA_Dose1_jan2021_age18to64", "GA_Dose1_jan2021_age65to100", "GA_Dose1_feb2021_age18to64", "GA_Dose1_feb2021_age65to100", "GA_Dose1_mar2021_age0to17", "GA_Dose1_mar2021_age18to64", "GA_Dose1_mar2021_age65to100", "GA_Dose1_apr2021_age0to17", "GA_Dose1_apr2021_age18to64", "GA_Dose1_apr2021_age65to100", "GA_Dose1_may2021_age0to17", "GA_Dose1_may2021_age18to64", "GA_Dose1_may2021_age65to100", "GA_Dose1_jun2021_age0to17", "GA_Dose1_jun2021_age18to64", "GA_Dose1_jun2021_age65to100", "GA_Dose1_jul2021_age0to17", "GA_Dose1_jul2021_age18to64", "GA_Dose1_jul2021_age65to100", "GA_Dose1_aug2021_age0to17", "GA_Dose1_aug2021_age18to64", "GA_Dose1_aug2021_age65to100", "GA_Dose1_sep2021_age0to17", "GA_Dose1_sep2021_age18to64", "GA_Dose1_sep2021_age65to100", "GA_Dose1_oct2021_age0to17", "GA_Dose1_oct2021_age18to64", "GA_Dose1_oct2021_age65to100", "GA_Dose3_oct2021_0to17", "GA_Dose3_oct2021_18to64", "GA_Dose3_oct2021_65to100", "GA_Dose1_nov2021_age0to17", "GA_Dose1_nov2021_age18to64", "GA_Dose1_nov2021_age65to100", "GA_Dose3_nov2021_0to17", "GA_Dose3_nov2021_18to64", "GA_Dose3_nov2021_65to100", "GA_Dose1_dec2021_age0to17", "GA_Dose1_dec2021_age18to64", "GA_Dose1_dec2021_age65to100", "GA_Dose3_dec2021_0to17", "GA_Dose3_dec2021_18to64", "GA_Dose3_dec2021_65to100", "GA_Dose1_jan2022_age0to17", "GA_Dose1_jan2022_age18to64", "GA_Dose1_jan2022_age65to100", "GA_Dose3_jan2022_0to17", "GA_Dose3_jan2022_18to64", "GA_Dose3_jan2022_65to100", "GA_Dose1_feb2022_age0to17", "GA_Dose1_feb2022_age18to64", "GA_Dose1_feb2022_age65to100", "GA_Dose3_feb2022_0to17", "GA_Dose3_feb2022_18to64", "GA_Dose3_feb2022_65to100", "GA_Dose1_mar2022_age0to17", "GA_Dose1_mar2022_age18to64", "GA_Dose1_mar2022_age65to100", "GA_Dose3_mar2022_0to17", "GA_Dose3_mar2022_18to64", "GA_Dose3_mar2022_65to100", "GA_Dose1_apr2022_age0to17", "GA_Dose1_apr2022_age18to64", "GA_Dose1_apr2022_age65to100", "GA_Dose3_apr2022_0to17", "GA_Dose3_apr2022_18to64", "GA_Dose3_apr2022_65to100", "GA_Dose1_may2022_age0to17", "GA_Dose1_may2022_age18to64", "GA_Dose1_may2022_age65to100", "GA_Dose3_may2022_0to17", "GA_Dose3_may2022_18to64", "GA_Dose3_may2022_65to100", "GA_Dose1_jun2022_age0to17", "GA_Dose1_jun2022_age18to64", "GA_Dose1_jun2022_age65to100", "GA_Dose3_jun2022_0to17", "GA_Dose3_jun2022_18to64", "GA_Dose3_jun2022_65to100", "GA_Dose1_jul2022_age0to17", "GA_Dose1_jul2022_age18to64", "GA_Dose1_jul2022_age65to100", "GA_Dose3_jul2022_0to17", "GA_Dose3_jul2022_18to64", "GA_Dose3_jul2022_65to100", "GA_Dose1_aug2022_age0to17", "GA_Dose1_aug2022_age18to64", "GA_Dose1_aug2022_age65to100", "GA_Dose3_aug2022_0to17", "GA_Dose3_aug2022_18to64", "GA_Dose3_aug2022_65to100", "GA_Dose1_sep2022_age0to17", "GA_Dose1_sep2022_age18to64", "GA_Dose1_sep2022_age65to100", "GA_Dose3_sep2022_0to17", "GA_Dose3_sep2022_18to64", "GA_Dose3_sep2022_65to100", "HI_Dose1_jan2021_age18to64", "HI_Dose1_jan2021_age65to100", "HI_Dose1_feb2021_age18to64", "HI_Dose1_feb2021_age65to100", "HI_Dose1_mar2021_age18to64", "HI_Dose1_mar2021_age65to100", "HI_Dose1_apr2021_age18to64", "HI_Dose1_apr2021_age65to100", "HI_Dose1_may2021_age0to17", "HI_Dose1_may2021_age18to64", "HI_Dose1_may2021_age65to100", "HI_Dose1_jun2021_age0to17", "HI_Dose1_jun2021_age18to64", "HI_Dose1_jun2021_age65to100", "HI_Dose1_jul2021_age0to17", "HI_Dose1_jul2021_age18to64", "HI_Dose1_jul2021_age65to100", "HI_Dose1_aug2021_age0to17", "HI_Dose1_aug2021_age18to64", "HI_Dose1_sep2021_age0to17", "HI_Dose1_sep2021_age18to64", "HI_Dose1_oct2021_age0to17", "HI_Dose1_oct2021_age18to64", "HI_Dose3_oct2021_18to64", "HI_Dose3_oct2021_65to100", "HI_Dose1_nov2021_age0to17", "HI_Dose1_nov2021_age18to64", "HI_Dose1_nov2021_age65to100", "HI_Dose3_nov2021_18to64", "HI_Dose3_nov2021_65to100", "HI_Dose1_dec2021_age0to17", "HI_Dose1_dec2021_age18to64", "HI_Dose1_dec2021_age65to100", "HI_Dose3_dec2021_0to17", "HI_Dose3_dec2021_18to64", "HI_Dose3_dec2021_65to100", "HI_Dose1_jan2022_age0to17", "HI_Dose1_jan2022_age18to64", "HI_Dose1_jan2022_age65to100", "HI_Dose3_jan2022_0to17", "HI_Dose3_jan2022_18to64", "HI_Dose3_jan2022_65to100", "HI_Dose1_feb2022_age0to17", "HI_Dose1_feb2022_age18to64", "HI_Dose1_feb2022_age65to100", "HI_Dose3_feb2022_0to17", "HI_Dose3_feb2022_18to64", "HI_Dose3_feb2022_65to100", "HI_Dose1_mar2022_age0to17", "HI_Dose1_mar2022_age18to64", "HI_Dose1_mar2022_age65to100", "HI_Dose3_mar2022_0to17", "HI_Dose3_mar2022_18to64", "HI_Dose3_mar2022_65to100", "HI_Dose1_apr2022_age0to17", "HI_Dose1_apr2022_age18to64", "HI_Dose1_apr2022_age65to100", "HI_Dose3_apr2022_0to17", "HI_Dose3_apr2022_18to64", "HI_Dose3_apr2022_65to100", "HI_Dose1_may2022_age0to17", "HI_Dose1_may2022_age18to64", "HI_Dose1_may2022_age65to100", "HI_Dose3_may2022_0to17", "HI_Dose3_may2022_18to64", "HI_Dose1_jun2022_age0to17", "HI_Dose1_jun2022_age18to64", "HI_Dose1_jun2022_age65to100", "HI_Dose3_jun2022_0to17", "HI_Dose3_jun2022_18to64", "HI_Dose1_jul2022_age0to17", "HI_Dose1_jul2022_age18to64", "HI_Dose3_jul2022_0to17", "HI_Dose3_jul2022_18to64", "HI_Dose1_aug2022_age0to17", "HI_Dose1_aug2022_age18to64", "HI_Dose3_aug2022_0to17", "HI_Dose3_aug2022_18to64", "HI_Dose1_sep2022_age0to17", "HI_Dose1_sep2022_age18to64", "HI_Dose3_sep2022_0to17", "HI_Dose3_sep2022_18to64", "ID_Dose1_jan2021_age18to64", "ID_Dose1_jan2021_age65to100", "ID_Dose1_feb2021_age0to17", "ID_Dose1_feb2021_age18to64", "ID_Dose1_feb2021_age65to100", "ID_Dose1_mar2021_age0to17", "ID_Dose1_mar2021_age18to64", "ID_Dose1_mar2021_age65to100", "ID_Dose1_apr2021_age0to17", "ID_Dose1_apr2021_age18to64", "ID_Dose1_apr2021_age65to100", "ID_Dose1_may2021_age0to17", "ID_Dose1_may2021_age18to64", "ID_Dose1_may2021_age65to100", "ID_Dose1_jun2021_age0to17", "ID_Dose1_jun2021_age18to64", "ID_Dose1_jun2021_age65to100", "ID_Dose1_jul2021_age0to17", "ID_Dose1_jul2021_age18to64", "ID_Dose1_jul2021_age65to100", "ID_Dose1_aug2021_age0to17", "ID_Dose1_aug2021_age18to64", "ID_Dose1_aug2021_age65to100", "ID_Dose1_sep2021_age0to17", "ID_Dose1_sep2021_age18to64", "ID_Dose1_sep2021_age65to100", "ID_Dose1_oct2021_age0to17", "ID_Dose1_oct2021_age18to64", "ID_Dose1_oct2021_age65to100", "ID_Dose3_oct2021_0to17", "ID_Dose3_oct2021_18to64", "ID_Dose3_oct2021_65to100", "ID_Dose1_nov2021_age0to17", "ID_Dose1_nov2021_age18to64", "ID_Dose1_nov2021_age65to100", "ID_Dose3_nov2021_0to17", "ID_Dose3_nov2021_18to64", "ID_Dose3_nov2021_65to100", "ID_Dose1_dec2021_age0to17", "ID_Dose1_dec2021_age18to64", "ID_Dose1_dec2021_age65to100", "ID_Dose3_dec2021_0to17", "ID_Dose3_dec2021_18to64", "ID_Dose3_dec2021_65to100", "ID_Dose1_jan2022_age0to17", "ID_Dose1_jan2022_age18to64", "ID_Dose1_jan2022_age65to100", "ID_Dose3_jan2022_0to17", "ID_Dose3_jan2022_18to64", "ID_Dose3_jan2022_65to100", "ID_Dose1_feb2022_age0to17", "ID_Dose1_feb2022_age18to64", "ID_Dose1_feb2022_age65to100", "ID_Dose3_feb2022_0to17", "ID_Dose3_feb2022_18to64", "ID_Dose3_feb2022_65to100", "ID_Dose1_mar2022_age0to17", "ID_Dose1_mar2022_age18to64", "ID_Dose1_mar2022_age65to100", "ID_Dose3_mar2022_0to17", "ID_Dose3_mar2022_18to64", "ID_Dose3_mar2022_65to100", "ID_Dose1_apr2022_age0to17", "ID_Dose1_apr2022_age18to64", "ID_Dose1_apr2022_age65to100", "ID_Dose3_apr2022_0to17", "ID_Dose3_apr2022_18to64", "ID_Dose3_apr2022_65to100", "ID_Dose1_may2022_age0to17", "ID_Dose1_may2022_age18to64", "ID_Dose1_may2022_age65to100", "ID_Dose3_may2022_0to17", "ID_Dose3_may2022_18to64", "ID_Dose3_may2022_65to100", "ID_Dose1_jun2022_age0to17", "ID_Dose1_jun2022_age18to64", "ID_Dose1_jun2022_age65to100", "ID_Dose3_jun2022_0to17", "ID_Dose3_jun2022_18to64", "ID_Dose3_jun2022_65to100", "ID_Dose1_jul2022_age0to17", "ID_Dose1_jul2022_age18to64", "ID_Dose1_jul2022_age65to100", "ID_Dose3_jul2022_0to17", "ID_Dose3_jul2022_18to64", "ID_Dose3_jul2022_65to100", "ID_Dose1_aug2022_age0to17", "ID_Dose1_aug2022_age18to64", "ID_Dose1_aug2022_age65to100", "ID_Dose3_aug2022_0to17", "ID_Dose3_aug2022_18to64", "ID_Dose3_aug2022_65to100", "ID_Dose1_sep2022_age0to17", "ID_Dose1_sep2022_age18to64", "ID_Dose1_sep2022_age65to100", "ID_Dose3_sep2022_0to17", "ID_Dose3_sep2022_18to64", "ID_Dose3_sep2022_65to100", "IL_Dose1_jan2021_age0to17", "IL_Dose1_jan2021_age18to64", "IL_Dose1_jan2021_age65to100", "IL_Dose1_feb2021_age0to17", "IL_Dose1_feb2021_age18to64", "IL_Dose1_feb2021_age65to100", "IL_Dose1_mar2021_age0to17", "IL_Dose1_mar2021_age18to64", "IL_Dose1_mar2021_age65to100", "IL_Dose1_apr2021_age0to17", "IL_Dose1_apr2021_age18to64", "IL_Dose1_apr2021_age65to100", "IL_Dose1_may2021_age0to17", "IL_Dose1_may2021_age18to64", "IL_Dose1_may2021_age65to100", "IL_Dose1_jun2021_age0to17", "IL_Dose1_jun2021_age18to64", "IL_Dose1_jun2021_age65to100", "IL_Dose1_jul2021_age0to17", "IL_Dose1_jul2021_age18to64", "IL_Dose1_jul2021_age65to100", "IL_Dose1_aug2021_age0to17", "IL_Dose1_aug2021_age18to64", "IL_Dose1_aug2021_age65to100", "IL_Dose1_sep2021_age0to17", "IL_Dose1_sep2021_age18to64", "IL_Dose1_sep2021_age65to100", "IL_Dose1_oct2021_age0to17", "IL_Dose1_oct2021_age18to64", "IL_Dose1_oct2021_age65to100", "IL_Dose3_oct2021_0to17", "IL_Dose3_oct2021_18to64", "IL_Dose3_oct2021_65to100", "IL_Dose1_nov2021_age0to17", "IL_Dose1_nov2021_age18to64", "IL_Dose1_nov2021_age65to100", "IL_Dose3_nov2021_0to17", "IL_Dose3_nov2021_18to64", "IL_Dose3_nov2021_65to100", "IL_Dose1_dec2021_age0to17", "IL_Dose1_dec2021_age18to64", "IL_Dose1_dec2021_age65to100", "IL_Dose3_dec2021_0to17", "IL_Dose3_dec2021_18to64", "IL_Dose3_dec2021_65to100", "IL_Dose1_jan2022_age0to17", "IL_Dose1_jan2022_age18to64", "IL_Dose1_jan2022_age65to100", "IL_Dose3_jan2022_0to17", "IL_Dose3_jan2022_18to64", "IL_Dose3_jan2022_65to100", "IL_Dose1_feb2022_age0to17", "IL_Dose1_feb2022_age18to64", "IL_Dose1_feb2022_age65to100", "IL_Dose3_feb2022_0to17", "IL_Dose3_feb2022_18to64", "IL_Dose3_feb2022_65to100", "IL_Dose1_mar2022_age0to17", "IL_Dose1_mar2022_age18to64", "IL_Dose1_mar2022_age65to100", "IL_Dose3_mar2022_0to17", "IL_Dose3_mar2022_18to64", "IL_Dose3_mar2022_65to100", "IL_Dose1_apr2022_age0to17", "IL_Dose1_apr2022_age18to64", "IL_Dose1_apr2022_age65to100", "IL_Dose3_apr2022_0to17", "IL_Dose3_apr2022_18to64", "IL_Dose3_apr2022_65to100", "IL_Dose1_may2022_age0to17", "IL_Dose1_may2022_age18to64", "IL_Dose1_may2022_age65to100", "IL_Dose3_may2022_0to17", "IL_Dose3_may2022_18to64", "IL_Dose3_may2022_65to100", "IL_Dose1_jun2022_age0to17", "IL_Dose1_jun2022_age18to64", "IL_Dose1_jun2022_age65to100", "IL_Dose3_jun2022_0to17", "IL_Dose3_jun2022_18to64", "IL_Dose3_jun2022_65to100", "IL_Dose1_jul2022_age0to17", "IL_Dose1_jul2022_age18to64", "IL_Dose1_jul2022_age65to100", "IL_Dose3_jul2022_0to17", "IL_Dose3_jul2022_18to64", "IL_Dose3_jul2022_65to100", "IL_Dose1_aug2022_age0to17", "IL_Dose1_aug2022_age18to64", "IL_Dose1_aug2022_age65to100", "IL_Dose3_aug2022_0to17", "IL_Dose3_aug2022_18to64", "IL_Dose3_aug2022_65to100", "IL_Dose1_sep2022_age0to17", "IL_Dose1_sep2022_age18to64", "IL_Dose1_sep2022_age65to100", "IL_Dose3_sep2022_0to17", "IL_Dose3_sep2022_18to64", "IL_Dose3_sep2022_65to100", "IN_Dose1_jan2021_age18to64", "IN_Dose1_jan2021_age65to100", "IN_Dose1_feb2021_age18to64", "IN_Dose1_feb2021_age65to100", "IN_Dose1_mar2021_age0to17", "IN_Dose1_mar2021_age18to64", "IN_Dose1_mar2021_age65to100", "IN_Dose1_apr2021_age0to17", "IN_Dose1_apr2021_age18to64", "IN_Dose1_apr2021_age65to100", "IN_Dose1_may2021_age0to17", "IN_Dose1_may2021_age18to64", "IN_Dose1_may2021_age65to100", "IN_Dose1_jun2021_age0to17", "IN_Dose1_jun2021_age18to64", "IN_Dose1_jun2021_age65to100", "IN_Dose1_jul2021_age0to17", "IN_Dose1_jul2021_age18to64", "IN_Dose1_jul2021_age65to100", "IN_Dose1_aug2021_age0to17", "IN_Dose1_aug2021_age18to64", "IN_Dose1_aug2021_age65to100", "IN_Dose1_sep2021_age0to17", "IN_Dose1_sep2021_age18to64", "IN_Dose1_sep2021_age65to100", "IN_Dose1_oct2021_age0to17", "IN_Dose1_oct2021_age18to64", "IN_Dose1_oct2021_age65to100", "IN_Dose3_oct2021_0to17", "IN_Dose3_oct2021_18to64", "IN_Dose3_oct2021_65to100", "IN_Dose1_nov2021_age0to17", "IN_Dose1_nov2021_age18to64", "IN_Dose1_nov2021_age65to100", "IN_Dose3_nov2021_0to17", "IN_Dose3_nov2021_18to64", "IN_Dose3_nov2021_65to100", "IN_Dose1_dec2021_age0to17", "IN_Dose1_dec2021_age18to64", "IN_Dose1_dec2021_age65to100", "IN_Dose3_dec2021_0to17", "IN_Dose3_dec2021_18to64", "IN_Dose3_dec2021_65to100", "IN_Dose1_jan2022_age0to17", "IN_Dose1_jan2022_age18to64", "IN_Dose1_jan2022_age65to100", "IN_Dose3_jan2022_0to17", "IN_Dose3_jan2022_18to64", "IN_Dose3_jan2022_65to100", "IN_Dose1_feb2022_age0to17", "IN_Dose1_feb2022_age18to64", "IN_Dose1_feb2022_age65to100", "IN_Dose3_feb2022_0to17", "IN_Dose3_feb2022_18to64", "IN_Dose3_feb2022_65to100", "IN_Dose1_mar2022_age0to17", "IN_Dose1_mar2022_age18to64", "IN_Dose1_mar2022_age65to100", "IN_Dose3_mar2022_0to17", "IN_Dose3_mar2022_18to64", "IN_Dose3_mar2022_65to100", "IN_Dose1_apr2022_age0to17", "IN_Dose1_apr2022_age18to64", "IN_Dose1_apr2022_age65to100", "IN_Dose3_apr2022_0to17", "IN_Dose3_apr2022_18to64", "IN_Dose3_apr2022_65to100", "IN_Dose1_may2022_age0to17", "IN_Dose1_may2022_age18to64", "IN_Dose1_may2022_age65to100", "IN_Dose3_may2022_0to17", "IN_Dose3_may2022_18to64", "IN_Dose3_may2022_65to100", "IN_Dose1_jun2022_age0to17", "IN_Dose1_jun2022_age18to64", "IN_Dose1_jun2022_age65to100", "IN_Dose3_jun2022_0to17", "IN_Dose3_jun2022_18to64", "IN_Dose3_jun2022_65to100", "IN_Dose1_jul2022_age0to17", "IN_Dose1_jul2022_age18to64", "IN_Dose1_jul2022_age65to100", "IN_Dose3_jul2022_0to17", "IN_Dose3_jul2022_18to64", "IN_Dose3_jul2022_65to100", "IN_Dose1_aug2022_age0to17", "IN_Dose1_aug2022_age18to64", "IN_Dose1_aug2022_age65to100", "IN_Dose3_aug2022_0to17", "IN_Dose3_aug2022_18to64", "IN_Dose3_aug2022_65to100", "IN_Dose1_sep2022_age0to17", "IN_Dose1_sep2022_age18to64", "IN_Dose1_sep2022_age65to100", "IN_Dose3_sep2022_0to17", "IN_Dose3_sep2022_18to64", "IN_Dose3_sep2022_65to100", "IA_Dose1_jan2021_age18to64", "IA_Dose1_jan2021_age65to100", "IA_Dose1_feb2021_age0to17", "IA_Dose1_feb2021_age18to64", "IA_Dose1_feb2021_age65to100", "IA_Dose1_mar2021_age0to17", "IA_Dose1_mar2021_age18to64", "IA_Dose1_mar2021_age65to100", "IA_Dose1_apr2021_age0to17", "IA_Dose1_apr2021_age18to64", "IA_Dose1_apr2021_age65to100", "IA_Dose1_may2021_age0to17", "IA_Dose1_may2021_age18to64", "IA_Dose1_may2021_age65to100", "IA_Dose1_jun2021_age0to17", "IA_Dose1_jun2021_age18to64", "IA_Dose1_jun2021_age65to100", "IA_Dose1_jul2021_age0to17", "IA_Dose1_jul2021_age18to64", "IA_Dose1_jul2021_age65to100", "IA_Dose1_aug2021_age0to17", "IA_Dose1_aug2021_age18to64", "IA_Dose1_aug2021_age65to100", "IA_Dose1_sep2021_age0to17", "IA_Dose1_sep2021_age18to64", "IA_Dose1_sep2021_age65to100", "IA_Dose1_oct2021_age0to17", "IA_Dose1_oct2021_age18to64", "IA_Dose1_oct2021_age65to100", "IA_Dose3_oct2021_0to17", "IA_Dose3_oct2021_18to64", "IA_Dose3_oct2021_65to100", "IA_Dose1_nov2021_age0to17", "IA_Dose1_nov2021_age18to64", "IA_Dose1_nov2021_age65to100", "IA_Dose3_nov2021_0to17", "IA_Dose3_nov2021_18to64", "IA_Dose3_nov2021_65to100", "IA_Dose1_dec2021_age0to17", "IA_Dose1_dec2021_age18to64", "IA_Dose1_dec2021_age65to100", "IA_Dose3_dec2021_0to17", "IA_Dose3_dec2021_18to64", "IA_Dose3_dec2021_65to100", "IA_Dose1_jan2022_age0to17", "IA_Dose1_jan2022_age18to64", "IA_Dose1_jan2022_age65to100", "IA_Dose3_jan2022_0to17", "IA_Dose3_jan2022_18to64", "IA_Dose3_jan2022_65to100", "IA_Dose1_feb2022_age0to17", "IA_Dose1_feb2022_age18to64", "IA_Dose1_feb2022_age65to100", "IA_Dose3_feb2022_0to17", "IA_Dose3_feb2022_18to64", "IA_Dose3_feb2022_65to100", "IA_Dose1_mar2022_age0to17", "IA_Dose1_mar2022_age18to64", "IA_Dose1_mar2022_age65to100", "IA_Dose3_mar2022_0to17", "IA_Dose3_mar2022_18to64", "IA_Dose3_mar2022_65to100", "IA_Dose1_apr2022_age0to17", "IA_Dose1_apr2022_age18to64", "IA_Dose1_apr2022_age65to100", "IA_Dose3_apr2022_0to17", "IA_Dose3_apr2022_18to64", "IA_Dose3_apr2022_65to100", "IA_Dose1_may2022_age0to17", "IA_Dose1_may2022_age18to64", "IA_Dose1_may2022_age65to100", "IA_Dose3_may2022_0to17", "IA_Dose3_may2022_18to64", "IA_Dose3_may2022_65to100", "IA_Dose1_jun2022_age0to17", "IA_Dose1_jun2022_age18to64", "IA_Dose1_jun2022_age65to100", "IA_Dose3_jun2022_0to17", "IA_Dose3_jun2022_18to64", "IA_Dose3_jun2022_65to100", "IA_Dose1_jul2022_age0to17", "IA_Dose1_jul2022_age18to64", "IA_Dose1_jul2022_age65to100", "IA_Dose3_jul2022_0to17", "IA_Dose3_jul2022_18to64", "IA_Dose3_jul2022_65to100", "IA_Dose1_aug2022_age0to17", "IA_Dose1_aug2022_age18to64", "IA_Dose1_aug2022_age65to100", "IA_Dose3_aug2022_0to17", "IA_Dose3_aug2022_18to64", "IA_Dose3_aug2022_65to100", "IA_Dose1_sep2022_age0to17", "IA_Dose1_sep2022_age18to64", "IA_Dose1_sep2022_age65to100", "IA_Dose3_sep2022_0to17", "IA_Dose3_sep2022_18to64", "IA_Dose3_sep2022_65to100", "KS_Dose1_jan2021_age18to64", "KS_Dose1_jan2021_age65to100", "KS_Dose1_feb2021_age18to64", "KS_Dose1_feb2021_age65to100", "KS_Dose1_mar2021_age0to17", "KS_Dose1_mar2021_age18to64", "KS_Dose1_mar2021_age65to100", "KS_Dose1_apr2021_age0to17", "KS_Dose1_apr2021_age18to64", "KS_Dose1_apr2021_age65to100", "KS_Dose1_may2021_age0to17", "KS_Dose1_may2021_age18to64", "KS_Dose1_may2021_age65to100", "KS_Dose1_jun2021_age0to17", "KS_Dose1_jun2021_age18to64", "KS_Dose1_jun2021_age65to100", "KS_Dose1_jul2021_age0to17", "KS_Dose1_jul2021_age18to64", "KS_Dose1_jul2021_age65to100", "KS_Dose1_aug2021_age0to17", "KS_Dose1_aug2021_age18to64", "KS_Dose1_aug2021_age65to100", "KS_Dose1_sep2021_age0to17", "KS_Dose1_sep2021_age18to64", "KS_Dose1_sep2021_age65to100", "KS_Dose1_oct2021_age0to17", "KS_Dose1_oct2021_age18to64", "KS_Dose1_oct2021_age65to100", "KS_Dose3_oct2021_0to17", "KS_Dose3_oct2021_18to64", "KS_Dose3_oct2021_65to100", "KS_Dose1_nov2021_age0to17", "KS_Dose1_nov2021_age18to64", "KS_Dose1_nov2021_age65to100", "KS_Dose3_nov2021_0to17", "KS_Dose3_nov2021_18to64", "KS_Dose3_nov2021_65to100", "KS_Dose1_dec2021_age0to17", "KS_Dose1_dec2021_age18to64", "KS_Dose1_dec2021_age65to100", "KS_Dose3_dec2021_0to17", "KS_Dose3_dec2021_18to64", "KS_Dose3_dec2021_65to100", "KS_Dose1_jan2022_age0to17", "KS_Dose1_jan2022_age18to64", "KS_Dose1_jan2022_age65to100", "KS_Dose3_jan2022_0to17", "KS_Dose3_jan2022_18to64", "KS_Dose3_jan2022_65to100", "KS_Dose1_feb2022_age0to17", "KS_Dose1_feb2022_age18to64", "KS_Dose1_feb2022_age65to100", "KS_Dose3_feb2022_0to17", "KS_Dose3_feb2022_18to64", "KS_Dose3_feb2022_65to100", "KS_Dose1_mar2022_age0to17", "KS_Dose1_mar2022_age18to64", "KS_Dose1_mar2022_age65to100", "KS_Dose3_mar2022_0to17", "KS_Dose3_mar2022_18to64", "KS_Dose3_mar2022_65to100", "KS_Dose1_apr2022_age0to17", "KS_Dose1_apr2022_age18to64", "KS_Dose1_apr2022_age65to100", "KS_Dose3_apr2022_0to17", "KS_Dose3_apr2022_18to64", "KS_Dose3_apr2022_65to100", "KS_Dose1_may2022_age0to17", "KS_Dose1_may2022_age18to64", "KS_Dose1_may2022_age65to100", "KS_Dose3_may2022_0to17", "KS_Dose3_may2022_18to64", "KS_Dose3_may2022_65to100", "KS_Dose1_jun2022_age0to17", "KS_Dose1_jun2022_age18to64", "KS_Dose1_jun2022_age65to100", "KS_Dose3_jun2022_0to17", "KS_Dose3_jun2022_18to64", "KS_Dose3_jun2022_65to100", "KS_Dose1_jul2022_age0to17", "KS_Dose1_jul2022_age18to64", "KS_Dose3_jul2022_0to17", "KS_Dose3_jul2022_18to64", "KS_Dose3_jul2022_65to100", "KS_Dose1_aug2022_age0to17", "KS_Dose1_aug2022_age18to64", "KS_Dose1_aug2022_age65to100", "KS_Dose3_aug2022_0to17", "KS_Dose3_aug2022_18to64", "KS_Dose3_aug2022_65to100", "KS_Dose1_sep2022_age0to17", "KS_Dose1_sep2022_age18to64", "KS_Dose3_sep2022_0to17", "KS_Dose3_sep2022_18to64", "KY_Dose1_jan2021_age18to64", "KY_Dose1_jan2021_age65to100", "KY_Dose1_feb2021_age0to17", "KY_Dose1_feb2021_age18to64", "KY_Dose1_feb2021_age65to100", "KY_Dose1_mar2021_age0to17", "KY_Dose1_mar2021_age18to64", "KY_Dose1_mar2021_age65to100", "KY_Dose1_apr2021_age0to17", "KY_Dose1_apr2021_age18to64", "KY_Dose1_apr2021_age65to100", "KY_Dose1_may2021_age0to17", "KY_Dose1_may2021_age18to64", "KY_Dose1_may2021_age65to100", "KY_Dose1_jun2021_age0to17", "KY_Dose1_jun2021_age18to64", "KY_Dose1_jun2021_age65to100", "KY_Dose1_jul2021_age0to17", "KY_Dose1_jul2021_age18to64", "KY_Dose1_jul2021_age65to100", "KY_Dose1_aug2021_age0to17", "KY_Dose1_aug2021_age18to64", "KY_Dose1_aug2021_age65to100", "KY_Dose1_sep2021_age0to17", "KY_Dose1_sep2021_age18to64", "KY_Dose1_sep2021_age65to100", "KY_Dose1_oct2021_age0to17", "KY_Dose1_oct2021_age18to64", "KY_Dose1_oct2021_age65to100", "KY_Dose3_oct2021_0to17", "KY_Dose3_oct2021_18to64", "KY_Dose3_oct2021_65to100", "KY_Dose1_nov2021_age0to17", "KY_Dose1_nov2021_age18to64", "KY_Dose1_nov2021_age65to100", "KY_Dose3_nov2021_0to17", "KY_Dose3_nov2021_18to64", "KY_Dose3_nov2021_65to100", "KY_Dose1_dec2021_age0to17", "KY_Dose1_dec2021_age18to64", "KY_Dose1_dec2021_age65to100", "KY_Dose3_dec2021_0to17", "KY_Dose3_dec2021_18to64", "KY_Dose3_dec2021_65to100", "KY_Dose1_jan2022_age0to17", "KY_Dose1_jan2022_age18to64", "KY_Dose1_jan2022_age65to100", "KY_Dose3_jan2022_0to17", "KY_Dose3_jan2022_18to64", "KY_Dose3_jan2022_65to100", "KY_Dose1_feb2022_age0to17", "KY_Dose1_feb2022_age18to64", "KY_Dose1_feb2022_age65to100", "KY_Dose3_feb2022_0to17", "KY_Dose3_feb2022_18to64", "KY_Dose3_feb2022_65to100", "KY_Dose1_mar2022_age0to17", "KY_Dose1_mar2022_age18to64", "KY_Dose1_mar2022_age65to100", "KY_Dose3_mar2022_0to17", "KY_Dose3_mar2022_18to64", "KY_Dose3_mar2022_65to100", "KY_Dose1_apr2022_age0to17", "KY_Dose1_apr2022_age18to64", "KY_Dose1_apr2022_age65to100", "KY_Dose3_apr2022_0to17", "KY_Dose3_apr2022_18to64", "KY_Dose3_apr2022_65to100", "KY_Dose1_may2022_age0to17", "KY_Dose1_may2022_age18to64", "KY_Dose1_may2022_age65to100", "KY_Dose3_may2022_0to17", "KY_Dose3_may2022_18to64", "KY_Dose3_may2022_65to100", "KY_Dose1_jun2022_age0to17", "KY_Dose1_jun2022_age18to64", "KY_Dose1_jun2022_age65to100", "KY_Dose3_jun2022_0to17", "KY_Dose3_jun2022_18to64", "KY_Dose3_jun2022_65to100", "KY_Dose1_jul2022_age0to17", "KY_Dose1_jul2022_age18to64", "KY_Dose1_jul2022_age65to100", "KY_Dose3_jul2022_0to17", "KY_Dose3_jul2022_18to64", "KY_Dose3_jul2022_65to100", "KY_Dose1_aug2022_age0to17", "KY_Dose1_aug2022_age18to64", "KY_Dose1_aug2022_age65to100", "KY_Dose3_aug2022_0to17", "KY_Dose3_aug2022_18to64", "KY_Dose3_aug2022_65to100", "KY_Dose1_sep2022_age0to17", "KY_Dose1_sep2022_age18to64", "KY_Dose1_sep2022_age65to100", "KY_Dose3_sep2022_0to17", "KY_Dose3_sep2022_18to64", "KY_Dose3_sep2022_65to100", "LA_Dose1_jan2021_age18to64", "LA_Dose1_jan2021_age65to100", "LA_Dose1_feb2021_age18to64", "LA_Dose1_feb2021_age65to100", "LA_Dose1_mar2021_age0to17", "LA_Dose1_mar2021_age18to64", "LA_Dose1_mar2021_age65to100", "LA_Dose1_apr2021_age0to17", "LA_Dose1_apr2021_age18to64", "LA_Dose1_apr2021_age65to100", "LA_Dose1_may2021_age0to17", "LA_Dose1_may2021_age18to64", "LA_Dose1_may2021_age65to100", "LA_Dose1_jun2021_age0to17", "LA_Dose1_jun2021_age18to64", "LA_Dose1_jun2021_age65to100", "LA_Dose1_jul2021_age0to17", "LA_Dose1_jul2021_age18to64", "LA_Dose1_jul2021_age65to100", "LA_Dose1_aug2021_age0to17", "LA_Dose1_aug2021_age18to64", "LA_Dose1_aug2021_age65to100", "LA_Dose1_sep2021_age0to17", "LA_Dose1_sep2021_age18to64", "LA_Dose1_sep2021_age65to100", "LA_Dose1_oct2021_age0to17", "LA_Dose1_oct2021_age18to64", "LA_Dose1_oct2021_age65to100", "LA_Dose3_oct2021_0to17", "LA_Dose3_oct2021_18to64", "LA_Dose3_oct2021_65to100", "LA_Dose1_nov2021_age0to17", "LA_Dose1_nov2021_age18to64", "LA_Dose1_nov2021_age65to100", "LA_Dose3_nov2021_0to17", "LA_Dose3_nov2021_18to64", "LA_Dose3_nov2021_65to100", "LA_Dose1_dec2021_age0to17", "LA_Dose1_dec2021_age18to64", "LA_Dose1_dec2021_age65to100", "LA_Dose3_dec2021_0to17", "LA_Dose3_dec2021_18to64", "LA_Dose3_dec2021_65to100", "LA_Dose1_jan2022_age0to17", "LA_Dose1_jan2022_age18to64", "LA_Dose1_jan2022_age65to100", "LA_Dose3_jan2022_0to17", "LA_Dose3_jan2022_18to64", "LA_Dose3_jan2022_65to100", "LA_Dose1_feb2022_age0to17", "LA_Dose1_feb2022_age18to64", "LA_Dose1_feb2022_age65to100", "LA_Dose3_feb2022_0to17", "LA_Dose3_feb2022_18to64", "LA_Dose3_feb2022_65to100", "LA_Dose1_mar2022_age0to17", "LA_Dose1_mar2022_age18to64", "LA_Dose1_mar2022_age65to100", "LA_Dose3_mar2022_0to17", "LA_Dose3_mar2022_18to64", "LA_Dose3_mar2022_65to100", "LA_Dose1_apr2022_age0to17", "LA_Dose1_apr2022_age18to64", "LA_Dose1_apr2022_age65to100", "LA_Dose3_apr2022_0to17", "LA_Dose3_apr2022_18to64", "LA_Dose3_apr2022_65to100", "LA_Dose1_may2022_age0to17", "LA_Dose1_may2022_age18to64", "LA_Dose1_may2022_age65to100", "LA_Dose3_may2022_0to17", "LA_Dose3_may2022_18to64", "LA_Dose3_may2022_65to100", "LA_Dose1_jun2022_age0to17", "LA_Dose1_jun2022_age18to64", "LA_Dose1_jun2022_age65to100", "LA_Dose3_jun2022_0to17", "LA_Dose3_jun2022_18to64", "LA_Dose3_jun2022_65to100", "LA_Dose1_jul2022_age0to17", "LA_Dose1_jul2022_age18to64", "LA_Dose1_jul2022_age65to100", "LA_Dose3_jul2022_0to17", "LA_Dose3_jul2022_18to64", "LA_Dose3_jul2022_65to100", "LA_Dose1_aug2022_age0to17", "LA_Dose1_aug2022_age18to64", "LA_Dose1_aug2022_age65to100", "LA_Dose3_aug2022_0to17", "LA_Dose3_aug2022_18to64", "LA_Dose3_aug2022_65to100", "LA_Dose1_sep2022_age0to17", "LA_Dose1_sep2022_age18to64", "LA_Dose1_sep2022_age65to100", "LA_Dose3_sep2022_0to17", "LA_Dose3_sep2022_18to64", "LA_Dose3_sep2022_65to100", "ME_Dose1_jan2021_age18to64", "ME_Dose1_jan2021_age65to100", "ME_Dose1_feb2021_age0to17", "ME_Dose1_feb2021_age18to64", "ME_Dose1_feb2021_age65to100", "ME_Dose1_mar2021_age0to17", "ME_Dose1_mar2021_age18to64", "ME_Dose1_mar2021_age65to100", "ME_Dose1_apr2021_age0to17", "ME_Dose1_apr2021_age18to64", "ME_Dose1_apr2021_age65to100", "ME_Dose1_may2021_age0to17", "ME_Dose1_may2021_age18to64", "ME_Dose1_may2021_age65to100", "ME_Dose1_jun2021_age0to17", "ME_Dose1_jun2021_age18to64", "ME_Dose1_jun2021_age65to100", "ME_Dose1_jul2021_age0to17", "ME_Dose1_jul2021_age18to64", "ME_Dose1_jul2021_age65to100", "ME_Dose1_aug2021_age0to17", "ME_Dose1_aug2021_age18to64", "ME_Dose1_aug2021_age65to100", "ME_Dose1_sep2021_age0to17", "ME_Dose1_sep2021_age18to64", "ME_Dose1_sep2021_age65to100", "ME_Dose1_oct2021_age0to17", "ME_Dose1_oct2021_age18to64", "ME_Dose1_oct2021_age65to100", "ME_Dose3_oct2021_0to17", "ME_Dose3_oct2021_18to64", "ME_Dose3_oct2021_65to100", "ME_Dose1_nov2021_age0to17", "ME_Dose1_nov2021_age18to64", "ME_Dose1_nov2021_age65to100", "ME_Dose3_nov2021_0to17", "ME_Dose3_nov2021_18to64", "ME_Dose3_nov2021_65to100", "ME_Dose1_dec2021_age0to17", "ME_Dose1_dec2021_age18to64", "ME_Dose1_dec2021_age65to100", "ME_Dose3_dec2021_0to17", "ME_Dose3_dec2021_18to64", "ME_Dose3_dec2021_65to100", "ME_Dose1_jan2022_age0to17", "ME_Dose1_jan2022_age18to64", "ME_Dose1_jan2022_age65to100", "ME_Dose3_jan2022_0to17", "ME_Dose3_jan2022_18to64", "ME_Dose3_jan2022_65to100", "ME_Dose1_feb2022_age0to17", "ME_Dose1_feb2022_age18to64", "ME_Dose1_feb2022_age65to100", "ME_Dose3_feb2022_0to17", "ME_Dose3_feb2022_18to64", "ME_Dose3_feb2022_65to100", "ME_Dose1_mar2022_age0to17", "ME_Dose1_mar2022_age18to64", "ME_Dose1_mar2022_age65to100", "ME_Dose3_mar2022_0to17", "ME_Dose3_mar2022_18to64", "ME_Dose3_mar2022_65to100", "ME_Dose1_apr2022_age0to17", "ME_Dose1_apr2022_age18to64", "ME_Dose1_apr2022_age65to100", "ME_Dose3_apr2022_0to17", "ME_Dose3_apr2022_18to64", "ME_Dose3_apr2022_65to100", "ME_Dose1_may2022_age0to17", "ME_Dose1_may2022_age18to64", "ME_Dose1_may2022_age65to100", "ME_Dose3_may2022_0to17", "ME_Dose3_may2022_18to64", "ME_Dose3_may2022_65to100", "ME_Dose1_jun2022_age0to17", "ME_Dose1_jun2022_age18to64", "ME_Dose1_jun2022_age65to100", "ME_Dose3_jun2022_0to17", "ME_Dose3_jun2022_18to64", "ME_Dose3_jun2022_65to100", "ME_Dose1_jul2022_age0to17", "ME_Dose1_jul2022_age18to64", "ME_Dose1_jul2022_age65to100", "ME_Dose3_jul2022_0to17", "ME_Dose3_jul2022_18to64", "ME_Dose3_jul2022_65to100", "ME_Dose1_aug2022_age0to17", "ME_Dose1_aug2022_age18to64", "ME_Dose3_aug2022_0to17", "ME_Dose3_aug2022_18to64", "ME_Dose1_sep2022_age0to17", "ME_Dose1_sep2022_age18to64", "ME_Dose3_sep2022_0to17", "ME_Dose3_sep2022_18to64", "MD_Dose1_jan2021_age18to64", "MD_Dose1_jan2021_age65to100", "MD_Dose1_feb2021_age0to17", "MD_Dose1_feb2021_age18to64", "MD_Dose1_feb2021_age65to100", "MD_Dose1_mar2021_age0to17", "MD_Dose1_mar2021_age18to64", "MD_Dose1_mar2021_age65to100", "MD_Dose1_apr2021_age0to17", "MD_Dose1_apr2021_age18to64", "MD_Dose1_apr2021_age65to100", "MD_Dose1_may2021_age0to17", "MD_Dose1_may2021_age18to64", "MD_Dose1_may2021_age65to100", "MD_Dose1_jun2021_age0to17", "MD_Dose1_jun2021_age18to64", "MD_Dose1_jun2021_age65to100", "MD_Dose1_jul2021_age0to17", "MD_Dose1_jul2021_age18to64", "MD_Dose1_jul2021_age65to100", "MD_Dose1_aug2021_age0to17", "MD_Dose1_aug2021_age18to64", "MD_Dose1_aug2021_age65to100", "MD_Dose1_sep2021_age0to17", "MD_Dose1_sep2021_age18to64", "MD_Dose1_sep2021_age65to100", "MD_Dose1_oct2021_age0to17", "MD_Dose1_oct2021_age18to64", "MD_Dose1_oct2021_age65to100", "MD_Dose3_oct2021_0to17", "MD_Dose3_oct2021_18to64", "MD_Dose3_oct2021_65to100", "MD_Dose1_nov2021_age0to17", "MD_Dose1_nov2021_age18to64", "MD_Dose1_nov2021_age65to100", "MD_Dose3_nov2021_0to17", "MD_Dose3_nov2021_18to64", "MD_Dose3_nov2021_65to100", "MD_Dose1_dec2021_age0to17", "MD_Dose1_dec2021_age18to64", "MD_Dose1_dec2021_age65to100", "MD_Dose3_dec2021_0to17", "MD_Dose3_dec2021_18to64", "MD_Dose3_dec2021_65to100", "MD_Dose1_jan2022_age0to17", "MD_Dose1_jan2022_age18to64", "MD_Dose1_jan2022_age65to100", "MD_Dose3_jan2022_0to17", "MD_Dose3_jan2022_18to64", "MD_Dose3_jan2022_65to100", "MD_Dose1_feb2022_age0to17", "MD_Dose1_feb2022_age18to64", "MD_Dose1_feb2022_age65to100", "MD_Dose3_feb2022_0to17", "MD_Dose3_feb2022_18to64", "MD_Dose3_feb2022_65to100", "MD_Dose1_mar2022_age0to17", "MD_Dose1_mar2022_age18to64", "MD_Dose1_mar2022_age65to100", "MD_Dose3_mar2022_0to17", "MD_Dose3_mar2022_18to64", "MD_Dose3_mar2022_65to100", "MD_Dose1_apr2022_age0to17", "MD_Dose1_apr2022_age18to64", "MD_Dose1_apr2022_age65to100", "MD_Dose3_apr2022_0to17", "MD_Dose3_apr2022_18to64", "MD_Dose3_apr2022_65to100", "MD_Dose1_may2022_age0to17", "MD_Dose1_may2022_age18to64", "MD_Dose1_may2022_age65to100", "MD_Dose3_may2022_0to17", "MD_Dose3_may2022_18to64", "MD_Dose3_may2022_65to100", "MD_Dose1_jun2022_age0to17", "MD_Dose1_jun2022_age18to64", "MD_Dose1_jun2022_age65to100", "MD_Dose3_jun2022_0to17", "MD_Dose3_jun2022_18to64", "MD_Dose3_jun2022_65to100", "MD_Dose1_jul2022_age0to17", "MD_Dose1_jul2022_age18to64", "MD_Dose1_jul2022_age65to100", "MD_Dose3_jul2022_0to17", "MD_Dose3_jul2022_18to64", "MD_Dose3_jul2022_65to100", "MD_Dose1_aug2022_age0to17", "MD_Dose1_aug2022_age18to64", "MD_Dose1_aug2022_age65to100", "MD_Dose3_aug2022_0to17", "MD_Dose3_aug2022_18to64", "MD_Dose3_aug2022_65to100", "MD_Dose1_sep2022_age0to17", "MD_Dose1_sep2022_age18to64", "MD_Dose1_sep2022_age65to100", "MD_Dose3_sep2022_0to17", "MD_Dose3_sep2022_18to64", "MD_Dose3_sep2022_65to100", "MA_Dose1_jan2021_age18to64", "MA_Dose1_jan2021_age65to100", "MA_Dose1_feb2021_age0to17", "MA_Dose1_feb2021_age18to64", "MA_Dose1_feb2021_age65to100", "MA_Dose1_mar2021_age0to17", "MA_Dose1_mar2021_age18to64", "MA_Dose1_mar2021_age65to100", "MA_Dose1_apr2021_age0to17", "MA_Dose1_apr2021_age18to64", "MA_Dose1_apr2021_age65to100", "MA_Dose1_may2021_age0to17", "MA_Dose1_may2021_age18to64", "MA_Dose1_may2021_age65to100", "MA_Dose1_jun2021_age0to17", "MA_Dose1_jun2021_age18to64", "MA_Dose1_jun2021_age65to100", "MA_Dose1_jul2021_age0to17", "MA_Dose1_jul2021_age18to64", "MA_Dose1_jul2021_age65to100", "MA_Dose1_aug2021_age0to17", "MA_Dose1_aug2021_age18to64", "MA_Dose1_aug2021_age65to100", "MA_Dose1_sep2021_age0to17", "MA_Dose1_sep2021_age18to64", "MA_Dose1_sep2021_age65to100", "MA_Dose1_oct2021_age0to17", "MA_Dose1_oct2021_age18to64", "MA_Dose1_oct2021_age65to100", "MA_Dose3_oct2021_0to17", "MA_Dose3_oct2021_18to64", "MA_Dose3_oct2021_65to100", "MA_Dose1_nov2021_age0to17", "MA_Dose1_nov2021_age18to64", "MA_Dose1_nov2021_age65to100", "MA_Dose3_nov2021_0to17", "MA_Dose3_nov2021_18to64", "MA_Dose3_nov2021_65to100", "MA_Dose1_dec2021_age0to17", "MA_Dose1_dec2021_age18to64", "MA_Dose1_dec2021_age65to100", "MA_Dose3_dec2021_0to17", "MA_Dose3_dec2021_18to64", "MA_Dose3_dec2021_65to100", "MA_Dose1_jan2022_age0to17", "MA_Dose1_jan2022_age18to64", "MA_Dose1_jan2022_age65to100", "MA_Dose3_jan2022_0to17", "MA_Dose3_jan2022_18to64", "MA_Dose3_jan2022_65to100", "MA_Dose1_feb2022_age0to17", "MA_Dose1_feb2022_age18to64", "MA_Dose1_feb2022_age65to100", "MA_Dose3_feb2022_0to17", "MA_Dose3_feb2022_18to64", "MA_Dose3_feb2022_65to100", "MA_Dose1_mar2022_age0to17", "MA_Dose1_mar2022_age18to64", "MA_Dose1_mar2022_age65to100", "MA_Dose3_mar2022_0to17", "MA_Dose3_mar2022_18to64", "MA_Dose3_mar2022_65to100", "MA_Dose1_apr2022_age0to17", "MA_Dose1_apr2022_age18to64", "MA_Dose1_apr2022_age65to100", "MA_Dose3_apr2022_0to17", "MA_Dose3_apr2022_18to64", "MA_Dose3_apr2022_65to100", "MA_Dose1_may2022_age0to17", "MA_Dose1_may2022_age18to64", "MA_Dose1_may2022_age65to100", "MA_Dose3_may2022_0to17", "MA_Dose3_may2022_18to64", "MA_Dose3_may2022_65to100", "MA_Dose1_jun2022_age0to17", "MA_Dose1_jun2022_age18to64", "MA_Dose1_jun2022_age65to100", "MA_Dose3_jun2022_0to17", "MA_Dose3_jun2022_18to64", "MA_Dose3_jun2022_65to100", "MA_Dose1_jul2022_age0to17", "MA_Dose1_jul2022_age18to64", "MA_Dose1_jul2022_age65to100", "MA_Dose3_jul2022_0to17", "MA_Dose3_jul2022_18to64", "MA_Dose3_jul2022_65to100", "MA_Dose1_aug2022_age0to17", "MA_Dose1_aug2022_age18to64", "MA_Dose1_aug2022_age65to100", "MA_Dose3_aug2022_0to17", "MA_Dose3_aug2022_18to64", "MA_Dose1_sep2022_age0to17", "MA_Dose1_sep2022_age18to64", "MA_Dose1_sep2022_age65to100", "MA_Dose3_sep2022_0to17", "MA_Dose3_sep2022_18to64", "MA_Dose3_sep2022_65to100", "MI_Dose1_jan2021_age18to64", "MI_Dose1_jan2021_age65to100", "MI_Dose1_feb2021_age0to17", "MI_Dose1_feb2021_age18to64", "MI_Dose1_feb2021_age65to100", "MI_Dose1_mar2021_age0to17", "MI_Dose1_mar2021_age18to64", "MI_Dose1_mar2021_age65to100", "MI_Dose1_apr2021_age0to17", "MI_Dose1_apr2021_age18to64", "MI_Dose1_apr2021_age65to100", "MI_Dose1_may2021_age0to17", "MI_Dose1_may2021_age18to64", "MI_Dose1_may2021_age65to100", "MI_Dose1_jun2021_age0to17", "MI_Dose1_jun2021_age18to64", "MI_Dose1_jun2021_age65to100", "MI_Dose1_jul2021_age0to17", "MI_Dose1_jul2021_age18to64", "MI_Dose1_jul2021_age65to100", "MI_Dose1_aug2021_age0to17", "MI_Dose1_aug2021_age18to64", "MI_Dose1_aug2021_age65to100", "MI_Dose1_sep2021_age0to17", "MI_Dose1_sep2021_age18to64", "MI_Dose1_sep2021_age65to100", "MI_Dose1_oct2021_age0to17", "MI_Dose1_oct2021_age18to64", "MI_Dose1_oct2021_age65to100", "MI_Dose3_oct2021_0to17", "MI_Dose3_oct2021_18to64", "MI_Dose3_oct2021_65to100", "MI_Dose1_nov2021_age0to17", "MI_Dose1_nov2021_age18to64", "MI_Dose1_nov2021_age65to100", "MI_Dose3_nov2021_0to17", "MI_Dose3_nov2021_18to64", "MI_Dose3_nov2021_65to100", "MI_Dose1_dec2021_age0to17", "MI_Dose1_dec2021_age18to64", "MI_Dose1_dec2021_age65to100", "MI_Dose3_dec2021_0to17", "MI_Dose3_dec2021_18to64", "MI_Dose3_dec2021_65to100", "MI_Dose1_jan2022_age0to17", "MI_Dose1_jan2022_age18to64", "MI_Dose1_jan2022_age65to100", "MI_Dose3_jan2022_0to17", "MI_Dose3_jan2022_18to64", "MI_Dose3_jan2022_65to100", "MI_Dose1_feb2022_age0to17", "MI_Dose1_feb2022_age18to64", "MI_Dose1_feb2022_age65to100", "MI_Dose3_feb2022_0to17", "MI_Dose3_feb2022_18to64", "MI_Dose3_feb2022_65to100", "MI_Dose1_mar2022_age0to17", "MI_Dose1_mar2022_age18to64", "MI_Dose1_mar2022_age65to100", "MI_Dose3_mar2022_0to17", "MI_Dose3_mar2022_18to64", "MI_Dose3_mar2022_65to100", "MI_Dose1_apr2022_age0to17", "MI_Dose1_apr2022_age18to64", "MI_Dose1_apr2022_age65to100", "MI_Dose3_apr2022_0to17", "MI_Dose3_apr2022_18to64", "MI_Dose3_apr2022_65to100", "MI_Dose1_may2022_age0to17", "MI_Dose1_may2022_age18to64", "MI_Dose1_may2022_age65to100", "MI_Dose3_may2022_0to17", "MI_Dose3_may2022_18to64", "MI_Dose3_may2022_65to100", "MI_Dose1_jun2022_age0to17", "MI_Dose1_jun2022_age18to64", "MI_Dose1_jun2022_age65to100", "MI_Dose3_jun2022_0to17", "MI_Dose3_jun2022_18to64", "MI_Dose3_jun2022_65to100", "MI_Dose1_jul2022_age0to17", "MI_Dose1_jul2022_age18to64", "MI_Dose1_jul2022_age65to100", "MI_Dose3_jul2022_0to17", "MI_Dose3_jul2022_18to64", "MI_Dose3_jul2022_65to100", "MI_Dose1_aug2022_age0to17", "MI_Dose1_aug2022_age18to64", "MI_Dose1_aug2022_age65to100", "MI_Dose3_aug2022_0to17", "MI_Dose3_aug2022_18to64", "MI_Dose3_aug2022_65to100", "MI_Dose1_sep2022_age0to17", "MI_Dose1_sep2022_age18to64", "MI_Dose1_sep2022_age65to100", "MI_Dose3_sep2022_0to17", "MI_Dose3_sep2022_18to64", "MI_Dose3_sep2022_65to100", "MN_Dose1_jan2021_age18to64", "MN_Dose1_jan2021_age65to100", "MN_Dose1_feb2021_age0to17", "MN_Dose1_feb2021_age18to64", "MN_Dose1_feb2021_age65to100", "MN_Dose1_mar2021_age0to17", "MN_Dose1_mar2021_age18to64", "MN_Dose1_mar2021_age65to100", "MN_Dose1_apr2021_age0to17", "MN_Dose1_apr2021_age18to64", "MN_Dose1_apr2021_age65to100", "MN_Dose1_may2021_age0to17", "MN_Dose1_may2021_age18to64", "MN_Dose1_may2021_age65to100", "MN_Dose1_jun2021_age0to17", "MN_Dose1_jun2021_age18to64", "MN_Dose1_jun2021_age65to100", "MN_Dose1_jul2021_age0to17", "MN_Dose1_jul2021_age18to64", "MN_Dose1_jul2021_age65to100", "MN_Dose1_aug2021_age0to17", "MN_Dose1_aug2021_age18to64", "MN_Dose1_aug2021_age65to100", "MN_Dose1_sep2021_age0to17", "MN_Dose1_sep2021_age18to64", "MN_Dose1_sep2021_age65to100", "MN_Dose1_oct2021_age0to17", "MN_Dose1_oct2021_age18to64", "MN_Dose1_oct2021_age65to100", "MN_Dose3_oct2021_0to17", "MN_Dose3_oct2021_18to64", "MN_Dose3_oct2021_65to100", "MN_Dose1_nov2021_age0to17", "MN_Dose1_nov2021_age18to64", "MN_Dose1_nov2021_age65to100", "MN_Dose3_nov2021_0to17", "MN_Dose3_nov2021_18to64", "MN_Dose3_nov2021_65to100", "MN_Dose1_dec2021_age0to17", "MN_Dose1_dec2021_age18to64", "MN_Dose1_dec2021_age65to100", "MN_Dose3_dec2021_0to17", "MN_Dose3_dec2021_18to64", "MN_Dose3_dec2021_65to100", "MN_Dose1_jan2022_age0to17", "MN_Dose1_jan2022_age18to64", "MN_Dose1_jan2022_age65to100", "MN_Dose3_jan2022_0to17", "MN_Dose3_jan2022_18to64", "MN_Dose3_jan2022_65to100", "MN_Dose1_feb2022_age0to17", "MN_Dose1_feb2022_age18to64", "MN_Dose1_feb2022_age65to100", "MN_Dose3_feb2022_0to17", "MN_Dose3_feb2022_18to64", "MN_Dose3_feb2022_65to100", "MN_Dose1_mar2022_age0to17", "MN_Dose1_mar2022_age18to64", "MN_Dose1_mar2022_age65to100", "MN_Dose3_mar2022_0to17", "MN_Dose3_mar2022_18to64", "MN_Dose3_mar2022_65to100", "MN_Dose1_apr2022_age0to17", "MN_Dose1_apr2022_age18to64", "MN_Dose1_apr2022_age65to100", "MN_Dose3_apr2022_0to17", "MN_Dose3_apr2022_18to64", "MN_Dose3_apr2022_65to100", "MN_Dose1_may2022_age0to17", "MN_Dose1_may2022_age18to64", "MN_Dose1_may2022_age65to100", "MN_Dose3_may2022_0to17", "MN_Dose3_may2022_18to64", "MN_Dose3_may2022_65to100", "MN_Dose1_jun2022_age0to17", "MN_Dose1_jun2022_age18to64", "MN_Dose1_jun2022_age65to100", "MN_Dose3_jun2022_0to17", "MN_Dose3_jun2022_18to64", "MN_Dose3_jun2022_65to100", "MN_Dose1_jul2022_age0to17", "MN_Dose1_jul2022_age18to64", "MN_Dose1_jul2022_age65to100", "MN_Dose3_jul2022_0to17", "MN_Dose3_jul2022_18to64", "MN_Dose3_jul2022_65to100", "MN_Dose1_aug2022_age0to17", "MN_Dose1_aug2022_age18to64", "MN_Dose1_aug2022_age65to100", "MN_Dose3_aug2022_0to17", "MN_Dose3_aug2022_18to64", "MN_Dose3_aug2022_65to100", "MN_Dose1_sep2022_age0to17", "MN_Dose1_sep2022_age18to64", "MN_Dose1_sep2022_age65to100", "MN_Dose3_sep2022_0to17", "MN_Dose3_sep2022_18to64", "MN_Dose3_sep2022_65to100", "MS_Dose1_jan2021_age18to64", "MS_Dose1_jan2021_age65to100", "MS_Dose1_feb2021_age18to64", "MS_Dose1_feb2021_age65to100", "MS_Dose1_mar2021_age18to64", "MS_Dose1_mar2021_age65to100", "MS_Dose1_apr2021_age18to64", "MS_Dose1_apr2021_age65to100", "MS_Dose1_may2021_age0to17", "MS_Dose1_may2021_age18to64", "MS_Dose1_may2021_age65to100", "MS_Dose1_jun2021_age0to17", "MS_Dose1_jun2021_age18to64", "MS_Dose1_jun2021_age65to100", "MS_Dose1_jul2021_age0to17", "MS_Dose1_jul2021_age18to64", "MS_Dose1_jul2021_age65to100", "MS_Dose1_aug2021_age0to17", "MS_Dose1_aug2021_age18to64", "MS_Dose1_aug2021_age65to100", "MS_Dose1_sep2021_age0to17", "MS_Dose1_sep2021_age18to64", "MS_Dose1_sep2021_age65to100", "MS_Dose1_oct2021_age0to17", "MS_Dose1_oct2021_age18to64", "MS_Dose1_oct2021_age65to100", "MS_Dose3_oct2021_18to64", "MS_Dose3_oct2021_65to100", "MS_Dose1_nov2021_age0to17", "MS_Dose1_nov2021_age18to64", "MS_Dose1_nov2021_age65to100", "MS_Dose3_nov2021_18to64", "MS_Dose3_nov2021_65to100", "MS_Dose1_dec2021_age0to17", "MS_Dose1_dec2021_age18to64", "MS_Dose1_dec2021_age65to100", "MS_Dose3_dec2021_0to17", "MS_Dose3_dec2021_18to64", "MS_Dose3_dec2021_65to100", "MS_Dose1_jan2022_age0to17", "MS_Dose1_jan2022_age18to64", "MS_Dose1_jan2022_age65to100", "MS_Dose3_jan2022_0to17", "MS_Dose3_jan2022_18to64", "MS_Dose3_jan2022_65to100", "MS_Dose1_feb2022_age0to17", "MS_Dose1_feb2022_age18to64", "MS_Dose1_feb2022_age65to100", "MS_Dose3_feb2022_0to17", "MS_Dose3_feb2022_18to64", "MS_Dose3_feb2022_65to100", "MS_Dose1_mar2022_age0to17", "MS_Dose1_mar2022_age18to64", "MS_Dose1_mar2022_age65to100", "MS_Dose3_mar2022_0to17", "MS_Dose3_mar2022_18to64", "MS_Dose3_mar2022_65to100", "MS_Dose1_apr2022_age0to17", "MS_Dose1_apr2022_age18to64", "MS_Dose1_apr2022_age65to100", "MS_Dose3_apr2022_0to17", "MS_Dose3_apr2022_18to64", "MS_Dose3_apr2022_65to100", "MS_Dose1_may2022_age0to17", "MS_Dose1_may2022_age18to64", "MS_Dose1_may2022_age65to100", "MS_Dose3_may2022_0to17", "MS_Dose3_may2022_18to64", "MS_Dose3_may2022_65to100", "MS_Dose1_jun2022_age0to17", "MS_Dose1_jun2022_age18to64", "MS_Dose1_jun2022_age65to100", "MS_Dose3_jun2022_0to17", "MS_Dose3_jun2022_18to64", "MS_Dose3_jun2022_65to100", "MS_Dose1_jul2022_age0to17", "MS_Dose1_jul2022_age18to64", "MS_Dose1_jul2022_age65to100", "MS_Dose3_jul2022_0to17", "MS_Dose3_jul2022_18to64", "MS_Dose3_jul2022_65to100", "MS_Dose1_aug2022_age0to17", "MS_Dose1_aug2022_age18to64", "MS_Dose1_aug2022_age65to100", "MS_Dose3_aug2022_0to17", "MS_Dose3_aug2022_18to64", "MS_Dose3_aug2022_65to100", "MS_Dose1_sep2022_age0to17", "MS_Dose1_sep2022_age18to64", "MS_Dose1_sep2022_age65to100", "MS_Dose3_sep2022_0to17", "MS_Dose3_sep2022_18to64", "MS_Dose3_sep2022_65to100", "MO_Dose1_jan2021_age18to64", "MO_Dose1_jan2021_age65to100", "MO_Dose1_feb2021_age0to17", "MO_Dose1_feb2021_age18to64", "MO_Dose1_feb2021_age65to100", "MO_Dose1_mar2021_age0to17", "MO_Dose1_mar2021_age18to64", "MO_Dose1_mar2021_age65to100", "MO_Dose1_apr2021_age0to17", "MO_Dose1_apr2021_age18to64", "MO_Dose1_apr2021_age65to100", "MO_Dose1_may2021_age0to17", "MO_Dose1_may2021_age18to64", "MO_Dose1_may2021_age65to100", "MO_Dose1_jun2021_age0to17", "MO_Dose1_jun2021_age18to64", "MO_Dose1_jun2021_age65to100", "MO_Dose1_jul2021_age0to17", "MO_Dose1_jul2021_age18to64", "MO_Dose1_jul2021_age65to100", "MO_Dose1_aug2021_age0to17", "MO_Dose1_aug2021_age18to64", "MO_Dose1_aug2021_age65to100", "MO_Dose1_sep2021_age0to17", "MO_Dose1_sep2021_age18to64", "MO_Dose1_sep2021_age65to100", "MO_Dose1_oct2021_age0to17", "MO_Dose1_oct2021_age18to64", "MO_Dose1_oct2021_age65to100", "MO_Dose3_oct2021_0to17", "MO_Dose3_oct2021_18to64", "MO_Dose3_oct2021_65to100", "MO_Dose1_nov2021_age0to17", "MO_Dose1_nov2021_age18to64", "MO_Dose1_nov2021_age65to100", "MO_Dose3_nov2021_0to17", "MO_Dose3_nov2021_18to64", "MO_Dose3_nov2021_65to100", "MO_Dose1_dec2021_age0to17", "MO_Dose1_dec2021_age18to64", "MO_Dose1_dec2021_age65to100", "MO_Dose3_dec2021_0to17", "MO_Dose3_dec2021_18to64", "MO_Dose3_dec2021_65to100", "MO_Dose1_jan2022_age0to17", "MO_Dose1_jan2022_age18to64", "MO_Dose1_jan2022_age65to100", "MO_Dose3_jan2022_0to17", "MO_Dose3_jan2022_18to64", "MO_Dose3_jan2022_65to100", "MO_Dose1_feb2022_age0to17", "MO_Dose1_feb2022_age18to64", "MO_Dose1_feb2022_age65to100", "MO_Dose3_feb2022_0to17", "MO_Dose3_feb2022_18to64", "MO_Dose3_feb2022_65to100", "MO_Dose1_mar2022_age0to17", "MO_Dose1_mar2022_age18to64", "MO_Dose1_mar2022_age65to100", "MO_Dose3_mar2022_0to17", "MO_Dose3_mar2022_18to64", "MO_Dose3_mar2022_65to100", "MO_Dose1_apr2022_age0to17", "MO_Dose1_apr2022_age18to64", "MO_Dose1_apr2022_age65to100", "MO_Dose3_apr2022_0to17", "MO_Dose3_apr2022_18to64", "MO_Dose3_apr2022_65to100", "MO_Dose1_may2022_age0to17", "MO_Dose1_may2022_age18to64", "MO_Dose1_may2022_age65to100", "MO_Dose3_may2022_0to17", "MO_Dose3_may2022_18to64", "MO_Dose3_may2022_65to100", "MO_Dose1_jun2022_age0to17", "MO_Dose1_jun2022_age18to64", "MO_Dose1_jun2022_age65to100", "MO_Dose3_jun2022_0to17", "MO_Dose3_jun2022_18to64", "MO_Dose3_jun2022_65to100", "MO_Dose1_jul2022_age0to17", "MO_Dose1_jul2022_age18to64", "MO_Dose1_jul2022_age65to100", "MO_Dose3_jul2022_0to17", "MO_Dose3_jul2022_18to64", "MO_Dose3_jul2022_65to100", "MO_Dose1_aug2022_age0to17", "MO_Dose1_aug2022_age18to64", "MO_Dose1_aug2022_age65to100", "MO_Dose3_aug2022_0to17", "MO_Dose3_aug2022_18to64", "MO_Dose3_aug2022_65to100", "MO_Dose1_sep2022_age0to17", "MO_Dose1_sep2022_age18to64", "MO_Dose1_sep2022_age65to100", "MO_Dose3_sep2022_0to17", "MO_Dose3_sep2022_18to64", "MO_Dose3_sep2022_65to100", "MT_Dose1_jan2021_age18to64", "MT_Dose1_jan2021_age65to100", "MT_Dose1_feb2021_age0to17", "MT_Dose1_feb2021_age18to64", "MT_Dose1_feb2021_age65to100", "MT_Dose1_mar2021_age0to17", "MT_Dose1_mar2021_age18to64", "MT_Dose1_mar2021_age65to100", "MT_Dose1_apr2021_age0to17", "MT_Dose1_apr2021_age18to64", "MT_Dose1_apr2021_age65to100", "MT_Dose1_may2021_age0to17", "MT_Dose1_may2021_age18to64", "MT_Dose1_may2021_age65to100", "MT_Dose1_jun2021_age0to17", "MT_Dose1_jun2021_age18to64", "MT_Dose1_jun2021_age65to100", "MT_Dose1_jul2021_age0to17", "MT_Dose1_jul2021_age18to64", "MT_Dose1_jul2021_age65to100", "MT_Dose1_aug2021_age0to17", "MT_Dose1_aug2021_age18to64", "MT_Dose1_aug2021_age65to100", "MT_Dose1_sep2021_age0to17", "MT_Dose1_sep2021_age18to64", "MT_Dose1_sep2021_age65to100", "MT_Dose1_oct2021_age0to17", "MT_Dose1_oct2021_age18to64", "MT_Dose1_oct2021_age65to100", "MT_Dose3_oct2021_0to17", "MT_Dose3_oct2021_18to64", "MT_Dose3_oct2021_65to100", "MT_Dose1_nov2021_age0to17", "MT_Dose1_nov2021_age18to64", "MT_Dose1_nov2021_age65to100", "MT_Dose3_nov2021_0to17", "MT_Dose3_nov2021_18to64", "MT_Dose3_nov2021_65to100", "MT_Dose1_dec2021_age0to17", "MT_Dose1_dec2021_age18to64", "MT_Dose1_dec2021_age65to100", "MT_Dose3_dec2021_0to17", "MT_Dose3_dec2021_18to64", "MT_Dose3_dec2021_65to100", "MT_Dose1_jan2022_age0to17", "MT_Dose1_jan2022_age18to64", "MT_Dose1_jan2022_age65to100", "MT_Dose3_jan2022_0to17", "MT_Dose3_jan2022_18to64", "MT_Dose3_jan2022_65to100", "MT_Dose1_feb2022_age0to17", "MT_Dose1_feb2022_age18to64", "MT_Dose1_feb2022_age65to100", "MT_Dose3_feb2022_0to17", "MT_Dose3_feb2022_18to64", "MT_Dose3_feb2022_65to100", "MT_Dose1_mar2022_age0to17", "MT_Dose1_mar2022_age18to64", "MT_Dose1_mar2022_age65to100", "MT_Dose3_mar2022_0to17", "MT_Dose3_mar2022_18to64", "MT_Dose3_mar2022_65to100", "MT_Dose1_apr2022_age0to17", "MT_Dose1_apr2022_age18to64", "MT_Dose1_apr2022_age65to100", "MT_Dose3_apr2022_0to17", "MT_Dose3_apr2022_18to64", "MT_Dose3_apr2022_65to100", "MT_Dose1_may2022_age0to17", "MT_Dose1_may2022_age18to64", "MT_Dose1_may2022_age65to100", "MT_Dose3_may2022_0to17", "MT_Dose3_may2022_18to64", "MT_Dose3_may2022_65to100", "MT_Dose1_jun2022_age0to17", "MT_Dose1_jun2022_age18to64", "MT_Dose1_jun2022_age65to100", "MT_Dose3_jun2022_0to17", "MT_Dose3_jun2022_18to64", "MT_Dose3_jun2022_65to100", "MT_Dose1_jul2022_age0to17", "MT_Dose1_jul2022_age18to64", "MT_Dose1_jul2022_age65to100", "MT_Dose3_jul2022_0to17", "MT_Dose3_jul2022_18to64", "MT_Dose3_jul2022_65to100", "MT_Dose1_aug2022_age0to17", "MT_Dose1_aug2022_age18to64", "MT_Dose1_aug2022_age65to100", "MT_Dose3_aug2022_0to17", "MT_Dose3_aug2022_18to64", "MT_Dose3_aug2022_65to100", "MT_Dose1_sep2022_age0to17", "MT_Dose1_sep2022_age18to64", "MT_Dose1_sep2022_age65to100", "MT_Dose3_sep2022_0to17", "MT_Dose3_sep2022_18to64", "MT_Dose3_sep2022_65to100", "NE_Dose1_jan2021_age18to64", "NE_Dose1_jan2021_age65to100", "NE_Dose1_feb2021_age0to17", "NE_Dose1_feb2021_age18to64", "NE_Dose1_feb2021_age65to100", "NE_Dose1_mar2021_age0to17", "NE_Dose1_mar2021_age18to64", "NE_Dose1_mar2021_age65to100", "NE_Dose1_apr2021_age0to17", "NE_Dose1_apr2021_age18to64", "NE_Dose1_apr2021_age65to100", "NE_Dose1_may2021_age0to17", "NE_Dose1_may2021_age18to64", "NE_Dose1_may2021_age65to100", "NE_Dose1_jun2021_age0to17", "NE_Dose1_jun2021_age18to64", "NE_Dose1_jun2021_age65to100", "NE_Dose1_jul2021_age0to17", "NE_Dose1_jul2021_age18to64", "NE_Dose1_jul2021_age65to100", "NE_Dose1_aug2021_age0to17", "NE_Dose1_aug2021_age18to64", "NE_Dose1_aug2021_age65to100", "NE_Dose1_sep2021_age0to17", "NE_Dose1_sep2021_age18to64", "NE_Dose1_sep2021_age65to100", "NE_Dose1_oct2021_age0to17", "NE_Dose1_oct2021_age18to64", "NE_Dose1_oct2021_age65to100", "NE_Dose3_oct2021_0to17", "NE_Dose3_oct2021_18to64", "NE_Dose3_oct2021_65to100", "NE_Dose1_nov2021_age0to17", "NE_Dose1_nov2021_age18to64", "NE_Dose1_nov2021_age65to100", "NE_Dose3_nov2021_0to17", "NE_Dose3_nov2021_18to64", "NE_Dose3_nov2021_65to100", "NE_Dose1_dec2021_age0to17", "NE_Dose1_dec2021_age18to64", "NE_Dose1_dec2021_age65to100", "NE_Dose3_dec2021_0to17", "NE_Dose3_dec2021_18to64", "NE_Dose3_dec2021_65to100", "NE_Dose1_jan2022_age0to17", "NE_Dose1_jan2022_age18to64", "NE_Dose1_jan2022_age65to100", "NE_Dose3_jan2022_0to17", "NE_Dose3_jan2022_18to64", "NE_Dose3_jan2022_65to100", "NE_Dose1_feb2022_age0to17", "NE_Dose1_feb2022_age18to64", "NE_Dose1_feb2022_age65to100", "NE_Dose3_feb2022_0to17", "NE_Dose3_feb2022_18to64", "NE_Dose3_feb2022_65to100", "NE_Dose1_mar2022_age0to17", "NE_Dose1_mar2022_age18to64", "NE_Dose1_mar2022_age65to100", "NE_Dose3_mar2022_0to17", "NE_Dose3_mar2022_18to64", "NE_Dose3_mar2022_65to100", "NE_Dose1_apr2022_age0to17", "NE_Dose1_apr2022_age18to64", "NE_Dose1_apr2022_age65to100", "NE_Dose3_apr2022_0to17", "NE_Dose3_apr2022_18to64", "NE_Dose3_apr2022_65to100", "NE_Dose1_may2022_age0to17", "NE_Dose1_may2022_age18to64", "NE_Dose1_may2022_age65to100", "NE_Dose3_may2022_0to17", "NE_Dose3_may2022_18to64", "NE_Dose3_may2022_65to100", "NE_Dose1_jun2022_age0to17", "NE_Dose1_jun2022_age18to64", "NE_Dose1_jun2022_age65to100", "NE_Dose3_jun2022_0to17", "NE_Dose3_jun2022_18to64", "NE_Dose3_jun2022_65to100", "NE_Dose1_jul2022_age0to17", "NE_Dose1_jul2022_age18to64", "NE_Dose1_jul2022_age65to100", "NE_Dose3_jul2022_0to17", "NE_Dose3_jul2022_18to64", "NE_Dose3_jul2022_65to100", "NE_Dose1_aug2022_age0to17", "NE_Dose1_aug2022_age18to64", "NE_Dose1_aug2022_age65to100", "NE_Dose3_aug2022_0to17", "NE_Dose3_aug2022_18to64", "NE_Dose3_aug2022_65to100", "NE_Dose1_sep2022_age0to17", "NE_Dose1_sep2022_age18to64", "NE_Dose1_sep2022_age65to100", "NE_Dose3_sep2022_0to17", "NE_Dose3_sep2022_18to64", "NE_Dose3_sep2022_65to100", "NV_Dose1_jan2021_age18to64", "NV_Dose1_jan2021_age65to100", "NV_Dose1_feb2021_age0to17", "NV_Dose1_feb2021_age18to64", "NV_Dose1_feb2021_age65to100", "NV_Dose1_mar2021_age0to17", "NV_Dose1_mar2021_age18to64", "NV_Dose1_mar2021_age65to100", "NV_Dose1_apr2021_age0to17", "NV_Dose1_apr2021_age18to64", "NV_Dose1_apr2021_age65to100", "NV_Dose1_may2021_age0to17", "NV_Dose1_may2021_age18to64", "NV_Dose1_may2021_age65to100", "NV_Dose1_jun2021_age0to17", "NV_Dose1_jun2021_age18to64", "NV_Dose1_jun2021_age65to100", "NV_Dose1_jul2021_age0to17", "NV_Dose1_jul2021_age18to64", "NV_Dose1_jul2021_age65to100", "NV_Dose1_aug2021_age0to17", "NV_Dose1_aug2021_age18to64", "NV_Dose1_aug2021_age65to100", "NV_Dose1_sep2021_age0to17", "NV_Dose1_sep2021_age18to64", "NV_Dose1_sep2021_age65to100", "NV_Dose1_oct2021_age0to17", "NV_Dose1_oct2021_age18to64", "NV_Dose1_oct2021_age65to100", "NV_Dose3_oct2021_0to17", "NV_Dose3_oct2021_18to64", "NV_Dose3_oct2021_65to100", "NV_Dose1_nov2021_age0to17", "NV_Dose1_nov2021_age18to64", "NV_Dose1_nov2021_age65to100", "NV_Dose3_nov2021_0to17", "NV_Dose3_nov2021_18to64", "NV_Dose3_nov2021_65to100", "NV_Dose1_dec2021_age0to17", "NV_Dose1_dec2021_age18to64", "NV_Dose1_dec2021_age65to100", "NV_Dose3_dec2021_0to17", "NV_Dose3_dec2021_18to64", "NV_Dose3_dec2021_65to100", "NV_Dose1_jan2022_age0to17", "NV_Dose1_jan2022_age18to64", "NV_Dose1_jan2022_age65to100", "NV_Dose3_jan2022_0to17", "NV_Dose3_jan2022_18to64", "NV_Dose3_jan2022_65to100", "NV_Dose1_feb2022_age0to17", "NV_Dose1_feb2022_age18to64", "NV_Dose1_feb2022_age65to100", "NV_Dose3_feb2022_0to17", "NV_Dose3_feb2022_18to64", "NV_Dose3_feb2022_65to100", "NV_Dose1_mar2022_age0to17", "NV_Dose1_mar2022_age18to64", "NV_Dose1_mar2022_age65to100", "NV_Dose3_mar2022_0to17", "NV_Dose3_mar2022_18to64", "NV_Dose3_mar2022_65to100", "NV_Dose1_apr2022_age0to17", "NV_Dose1_apr2022_age18to64", "NV_Dose1_apr2022_age65to100", "NV_Dose3_apr2022_0to17", "NV_Dose3_apr2022_18to64", "NV_Dose3_apr2022_65to100", "NV_Dose1_may2022_age0to17", "NV_Dose1_may2022_age18to64", "NV_Dose1_may2022_age65to100", "NV_Dose3_may2022_0to17", "NV_Dose3_may2022_18to64", "NV_Dose3_may2022_65to100", "NV_Dose1_jun2022_age0to17", "NV_Dose1_jun2022_age18to64", "NV_Dose1_jun2022_age65to100", "NV_Dose3_jun2022_0to17", "NV_Dose3_jun2022_18to64", "NV_Dose3_jun2022_65to100", "NV_Dose1_jul2022_age0to17", "NV_Dose1_jul2022_age18to64", "NV_Dose1_jul2022_age65to100", "NV_Dose3_jul2022_0to17", "NV_Dose3_jul2022_18to64", "NV_Dose3_jul2022_65to100", "NV_Dose1_aug2022_age0to17", "NV_Dose1_aug2022_age18to64", "NV_Dose1_aug2022_age65to100", "NV_Dose3_aug2022_0to17", "NV_Dose3_aug2022_18to64", "NV_Dose3_aug2022_65to100", "NV_Dose1_sep2022_age0to17", "NV_Dose1_sep2022_age18to64", "NV_Dose1_sep2022_age65to100", "NV_Dose3_sep2022_0to17", "NV_Dose3_sep2022_18to64", "NV_Dose3_sep2022_65to100", "NH_Dose1_jan2021_age18to64", "NH_Dose1_jan2021_age65to100", "NH_Dose1_feb2021_age0to17", "NH_Dose1_feb2021_age18to64", "NH_Dose1_feb2021_age65to100", "NH_Dose1_mar2021_age0to17", "NH_Dose1_mar2021_age18to64", "NH_Dose1_mar2021_age65to100", "NH_Dose1_apr2021_age0to17", "NH_Dose1_apr2021_age18to64", "NH_Dose1_apr2021_age65to100", "NH_Dose1_may2021_age0to17", "NH_Dose1_may2021_age18to64", "NH_Dose1_may2021_age65to100", "NH_Dose1_jun2021_age0to17", "NH_Dose1_jun2021_age18to64", "NH_Dose1_jun2021_age65to100", "NH_Dose1_jul2021_age0to17", "NH_Dose1_jul2021_age18to64", "NH_Dose1_jul2021_age65to100", "NH_Dose1_aug2021_age0to17", "NH_Dose1_aug2021_age18to64", "NH_Dose1_aug2021_age65to100", "NH_Dose1_sep2021_age0to17", "NH_Dose1_sep2021_age18to64", "NH_Dose1_sep2021_age65to100", "NH_Dose1_oct2021_age0to17", "NH_Dose1_oct2021_age18to64", "NH_Dose1_oct2021_age65to100", "NH_Dose3_oct2021_0to17", "NH_Dose3_oct2021_18to64", "NH_Dose3_oct2021_65to100", "NH_Dose1_nov2021_age0to17", "NH_Dose1_nov2021_age18to64", "NH_Dose1_nov2021_age65to100", "NH_Dose3_nov2021_0to17", "NH_Dose3_nov2021_18to64", "NH_Dose3_nov2021_65to100", "NH_Dose1_dec2021_age0to17", "NH_Dose1_dec2021_age18to64", "NH_Dose1_dec2021_age65to100", "NH_Dose3_dec2021_0to17", "NH_Dose3_dec2021_18to64", "NH_Dose3_dec2021_65to100", "NH_Dose1_jan2022_age0to17", "NH_Dose1_jan2022_age18to64", "NH_Dose1_jan2022_age65to100", "NH_Dose3_jan2022_0to17", "NH_Dose3_jan2022_18to64", "NH_Dose3_jan2022_65to100", "NH_Dose1_feb2022_age0to17", "NH_Dose1_feb2022_age18to64", "NH_Dose1_feb2022_age65to100", "NH_Dose3_feb2022_0to17", "NH_Dose3_feb2022_18to64", "NH_Dose3_feb2022_65to100", "NH_Dose1_mar2022_age0to17", "NH_Dose1_mar2022_age18to64", "NH_Dose1_mar2022_age65to100", "NH_Dose3_mar2022_0to17", "NH_Dose3_mar2022_18to64", "NH_Dose3_mar2022_65to100", "NH_Dose1_apr2022_age0to17", "NH_Dose1_apr2022_age18to64", "NH_Dose1_apr2022_age65to100", "NH_Dose3_apr2022_0to17", "NH_Dose3_apr2022_18to64", "NH_Dose3_apr2022_65to100", "NH_Dose1_may2022_age0to17", "NH_Dose1_may2022_age18to64", "NH_Dose1_may2022_age65to100", "NH_Dose3_may2022_0to17", "NH_Dose3_may2022_18to64", "NH_Dose3_may2022_65to100", "NH_Dose1_jun2022_age0to17", "NH_Dose1_jun2022_age18to64", "NH_Dose1_jun2022_age65to100", "NH_Dose3_jun2022_0to17", "NH_Dose3_jun2022_18to64", "NH_Dose3_jun2022_65to100", "NH_Dose1_jul2022_age0to17", "NH_Dose1_jul2022_age18to64", "NH_Dose1_jul2022_age65to100", "NH_Dose3_jul2022_0to17", "NH_Dose3_jul2022_18to64", "NH_Dose3_jul2022_65to100", "NH_Dose1_aug2022_age0to17", "NH_Dose1_aug2022_age18to64", "NH_Dose3_aug2022_0to17", "NH_Dose3_aug2022_18to64", "NH_Dose1_sep2022_age0to17", "NH_Dose1_sep2022_age18to64", "NH_Dose3_sep2022_0to17", "NH_Dose3_sep2022_18to64", "NJ_Dose1_jan2021_age18to64", "NJ_Dose1_jan2021_age65to100", "NJ_Dose1_feb2021_age18to64", "NJ_Dose1_feb2021_age65to100", "NJ_Dose1_mar2021_age18to64", "NJ_Dose1_mar2021_age65to100", "NJ_Dose1_apr2021_age0to17", "NJ_Dose1_apr2021_age18to64", "NJ_Dose1_apr2021_age65to100", "NJ_Dose1_may2021_age0to17", "NJ_Dose1_may2021_age18to64", "NJ_Dose1_may2021_age65to100", "NJ_Dose1_jun2021_age0to17", "NJ_Dose1_jun2021_age18to64", "NJ_Dose1_jun2021_age65to100", "NJ_Dose1_jul2021_age0to17", "NJ_Dose1_jul2021_age18to64", "NJ_Dose1_jul2021_age65to100", "NJ_Dose1_aug2021_age0to17", "NJ_Dose1_aug2021_age18to64", "NJ_Dose1_aug2021_age65to100", "NJ_Dose1_sep2021_age0to17", "NJ_Dose1_sep2021_age18to64", "NJ_Dose1_sep2021_age65to100", "NJ_Dose1_oct2021_age0to17", "NJ_Dose1_oct2021_age18to64", "NJ_Dose1_oct2021_age65to100", "NJ_Dose3_oct2021_18to64", "NJ_Dose3_oct2021_65to100", "NJ_Dose1_nov2021_age0to17", "NJ_Dose1_nov2021_age18to64", "NJ_Dose1_nov2021_age65to100", "NJ_Dose3_nov2021_0to17", "NJ_Dose3_nov2021_18to64", "NJ_Dose3_nov2021_65to100", "NJ_Dose1_dec2021_age0to17", "NJ_Dose1_dec2021_age18to64", "NJ_Dose1_dec2021_age65to100", "NJ_Dose3_dec2021_0to17", "NJ_Dose3_dec2021_18to64", "NJ_Dose3_dec2021_65to100", "NJ_Dose1_jan2022_age0to17", "NJ_Dose1_jan2022_age18to64", "NJ_Dose1_jan2022_age65to100", "NJ_Dose3_jan2022_0to17", "NJ_Dose3_jan2022_18to64", "NJ_Dose3_jan2022_65to100", "NJ_Dose1_feb2022_age0to17", "NJ_Dose1_feb2022_age18to64", "NJ_Dose1_feb2022_age65to100", "NJ_Dose3_feb2022_0to17", "NJ_Dose3_feb2022_18to64", "NJ_Dose3_feb2022_65to100", "NJ_Dose1_mar2022_age0to17", "NJ_Dose1_mar2022_age18to64", "NJ_Dose1_mar2022_age65to100", "NJ_Dose3_mar2022_0to17", "NJ_Dose3_mar2022_18to64", "NJ_Dose3_mar2022_65to100", "NJ_Dose1_apr2022_age0to17", "NJ_Dose1_apr2022_age18to64", "NJ_Dose1_apr2022_age65to100", "NJ_Dose3_apr2022_0to17", "NJ_Dose3_apr2022_18to64", "NJ_Dose3_apr2022_65to100", "NJ_Dose1_may2022_age0to17", "NJ_Dose1_may2022_age18to64", "NJ_Dose1_may2022_age65to100", "NJ_Dose3_may2022_0to17", "NJ_Dose3_may2022_18to64", "NJ_Dose3_may2022_65to100", "NJ_Dose1_jun2022_age0to17", "NJ_Dose1_jun2022_age18to64", "NJ_Dose1_jun2022_age65to100", "NJ_Dose3_jun2022_0to17", "NJ_Dose3_jun2022_18to64", "NJ_Dose3_jun2022_65to100", "NJ_Dose1_jul2022_age0to17", "NJ_Dose1_jul2022_age18to64", "NJ_Dose1_jul2022_age65to100", "NJ_Dose3_jul2022_0to17", "NJ_Dose3_jul2022_18to64", "NJ_Dose3_jul2022_65to100", "NJ_Dose1_aug2022_age0to17", "NJ_Dose1_aug2022_age18to64", "NJ_Dose1_aug2022_age65to100", "NJ_Dose3_aug2022_0to17", "NJ_Dose3_aug2022_18to64", "NJ_Dose3_aug2022_65to100", "NJ_Dose1_sep2022_age0to17", "NJ_Dose1_sep2022_age18to64", "NJ_Dose1_sep2022_age65to100", "NJ_Dose3_sep2022_0to17", "NJ_Dose3_sep2022_18to64", "NJ_Dose3_sep2022_65to100", "NM_Dose1_jan2021_age0to17", "NM_Dose1_jan2021_age18to64", "NM_Dose1_jan2021_age65to100", "NM_Dose1_feb2021_age0to17", "NM_Dose1_feb2021_age18to64", "NM_Dose1_feb2021_age65to100", "NM_Dose1_mar2021_age0to17", "NM_Dose1_mar2021_age18to64", "NM_Dose1_mar2021_age65to100", "NM_Dose1_apr2021_age0to17", "NM_Dose1_apr2021_age18to64", "NM_Dose1_apr2021_age65to100", "NM_Dose1_may2021_age0to17", "NM_Dose1_may2021_age18to64", "NM_Dose1_may2021_age65to100", "NM_Dose1_jun2021_age0to17", "NM_Dose1_jun2021_age18to64", "NM_Dose1_jun2021_age65to100", "NM_Dose1_jul2021_age0to17", "NM_Dose1_jul2021_age18to64", "NM_Dose1_jul2021_age65to100", "NM_Dose1_aug2021_age0to17", "NM_Dose1_aug2021_age18to64", "NM_Dose1_aug2021_age65to100", "NM_Dose1_sep2021_age0to17", "NM_Dose1_sep2021_age18to64", "NM_Dose1_sep2021_age65to100", "NM_Dose1_oct2021_age0to17", "NM_Dose1_oct2021_age18to64", "NM_Dose1_oct2021_age65to100", "NM_Dose3_oct2021_0to17", "NM_Dose3_oct2021_18to64", "NM_Dose3_oct2021_65to100", "NM_Dose1_nov2021_age0to17", "NM_Dose1_nov2021_age18to64", "NM_Dose1_nov2021_age65to100", "NM_Dose3_nov2021_0to17", "NM_Dose3_nov2021_18to64", "NM_Dose3_nov2021_65to100", "NM_Dose1_dec2021_age0to17", "NM_Dose1_dec2021_age18to64", "NM_Dose1_dec2021_age65to100", "NM_Dose3_dec2021_0to17", "NM_Dose3_dec2021_18to64", "NM_Dose3_dec2021_65to100", "NM_Dose1_jan2022_age0to17", "NM_Dose1_jan2022_age18to64", "NM_Dose1_jan2022_age65to100", "NM_Dose3_jan2022_0to17", "NM_Dose3_jan2022_18to64", "NM_Dose3_jan2022_65to100", "NM_Dose1_feb2022_age0to17", "NM_Dose1_feb2022_age18to64", "NM_Dose1_feb2022_age65to100", "NM_Dose3_feb2022_0to17", "NM_Dose3_feb2022_18to64", "NM_Dose3_feb2022_65to100", "NM_Dose1_mar2022_age0to17", "NM_Dose1_mar2022_age18to64", "NM_Dose1_mar2022_age65to100", "NM_Dose3_mar2022_0to17", "NM_Dose3_mar2022_18to64", "NM_Dose3_mar2022_65to100", "NM_Dose1_apr2022_age0to17", "NM_Dose1_apr2022_age18to64", "NM_Dose1_apr2022_age65to100", "NM_Dose3_apr2022_0to17", "NM_Dose3_apr2022_18to64", "NM_Dose3_apr2022_65to100", "NM_Dose1_may2022_age0to17", "NM_Dose1_may2022_age18to64", "NM_Dose1_may2022_age65to100", "NM_Dose3_may2022_0to17", "NM_Dose3_may2022_18to64", "NM_Dose3_may2022_65to100", "NM_Dose1_jun2022_age0to17", "NM_Dose1_jun2022_age18to64", "NM_Dose1_jun2022_age65to100", "NM_Dose3_jun2022_0to17", "NM_Dose3_jun2022_18to64", "NM_Dose3_jun2022_65to100", "NM_Dose1_jul2022_age0to17", "NM_Dose1_jul2022_age18to64", "NM_Dose1_jul2022_age65to100", "NM_Dose3_jul2022_0to17", "NM_Dose3_jul2022_18to64", "NM_Dose3_jul2022_65to100", "NM_Dose1_aug2022_age0to17", "NM_Dose1_aug2022_age18to64", "NM_Dose3_aug2022_0to17", "NM_Dose3_aug2022_18to64", "NM_Dose1_sep2022_age0to17", "NM_Dose1_sep2022_age18to64", "NM_Dose3_sep2022_0to17", "NM_Dose3_sep2022_18to64", "NY_Dose1_jan2021_age18to64", "NY_Dose1_jan2021_age65to100", "NY_Dose1_feb2021_age0to17", "NY_Dose1_feb2021_age18to64", "NY_Dose1_feb2021_age65to100", "NY_Dose1_mar2021_age0to17", "NY_Dose1_mar2021_age18to64", "NY_Dose1_mar2021_age65to100", "NY_Dose1_apr2021_age0to17", "NY_Dose1_apr2021_age18to64", "NY_Dose1_apr2021_age65to100", "NY_Dose1_may2021_age0to17", "NY_Dose1_may2021_age18to64", "NY_Dose1_may2021_age65to100", "NY_Dose1_jun2021_age0to17", "NY_Dose1_jun2021_age18to64", "NY_Dose1_jun2021_age65to100", "NY_Dose1_jul2021_age0to17", "NY_Dose1_jul2021_age18to64", "NY_Dose1_jul2021_age65to100", "NY_Dose1_aug2021_age0to17", "NY_Dose1_aug2021_age18to64", "NY_Dose1_aug2021_age65to100", "NY_Dose1_sep2021_age0to17", "NY_Dose1_sep2021_age18to64", "NY_Dose1_sep2021_age65to100", "NY_Dose1_oct2021_age0to17", "NY_Dose1_oct2021_age18to64", "NY_Dose1_oct2021_age65to100", "NY_Dose3_oct2021_0to17", "NY_Dose3_oct2021_18to64", "NY_Dose3_oct2021_65to100", "NY_Dose1_nov2021_age0to17", "NY_Dose1_nov2021_age18to64", "NY_Dose1_nov2021_age65to100", "NY_Dose3_nov2021_0to17", "NY_Dose3_nov2021_18to64", "NY_Dose3_nov2021_65to100", "NY_Dose1_dec2021_age0to17", "NY_Dose1_dec2021_age18to64", "NY_Dose1_dec2021_age65to100", "NY_Dose3_dec2021_0to17", "NY_Dose3_dec2021_18to64", "NY_Dose3_dec2021_65to100", "NY_Dose1_jan2022_age0to17", "NY_Dose1_jan2022_age18to64", "NY_Dose1_jan2022_age65to100", "NY_Dose3_jan2022_0to17", "NY_Dose3_jan2022_18to64", "NY_Dose3_jan2022_65to100", "NY_Dose1_feb2022_age0to17", "NY_Dose1_feb2022_age18to64", "NY_Dose1_feb2022_age65to100", "NY_Dose3_feb2022_0to17", "NY_Dose3_feb2022_18to64", "NY_Dose3_feb2022_65to100", "NY_Dose1_mar2022_age0to17", "NY_Dose1_mar2022_age18to64", "NY_Dose1_mar2022_age65to100", "NY_Dose3_mar2022_0to17", "NY_Dose3_mar2022_18to64", "NY_Dose3_mar2022_65to100", "NY_Dose1_apr2022_age0to17", "NY_Dose1_apr2022_age18to64", "NY_Dose1_apr2022_age65to100", "NY_Dose3_apr2022_0to17", "NY_Dose3_apr2022_18to64", "NY_Dose3_apr2022_65to100", "NY_Dose1_may2022_age0to17", "NY_Dose1_may2022_age18to64", "NY_Dose1_may2022_age65to100", "NY_Dose3_may2022_0to17", "NY_Dose3_may2022_18to64", "NY_Dose3_may2022_65to100", "NY_Dose1_jun2022_age0to17", "NY_Dose1_jun2022_age18to64", "NY_Dose1_jun2022_age65to100", "NY_Dose3_jun2022_0to17", "NY_Dose3_jun2022_18to64", "NY_Dose3_jun2022_65to100", "NY_Dose1_jul2022_age0to17", "NY_Dose1_jul2022_age18to64", "NY_Dose1_jul2022_age65to100", "NY_Dose3_jul2022_0to17", "NY_Dose3_jul2022_18to64", "NY_Dose3_jul2022_65to100", "NY_Dose1_aug2022_age0to17", "NY_Dose1_aug2022_age18to64", "NY_Dose1_aug2022_age65to100", "NY_Dose3_aug2022_0to17", "NY_Dose3_aug2022_18to64", "NY_Dose3_aug2022_65to100", "NY_Dose1_sep2022_age0to17", "NY_Dose1_sep2022_age18to64", "NY_Dose3_sep2022_0to17", "NY_Dose3_sep2022_18to64", "NY_Dose3_sep2022_65to100", "NC_Dose1_jan2021_age18to64", "NC_Dose1_jan2021_age65to100", "NC_Dose1_feb2021_age18to64", "NC_Dose1_feb2021_age65to100", "NC_Dose1_mar2021_age0to17", "NC_Dose1_mar2021_age18to64", "NC_Dose1_mar2021_age65to100", "NC_Dose1_apr2021_age0to17", "NC_Dose1_apr2021_age18to64", "NC_Dose1_apr2021_age65to100", "NC_Dose1_may2021_age0to17", "NC_Dose1_may2021_age18to64", "NC_Dose1_may2021_age65to100", "NC_Dose1_jun2021_age0to17", "NC_Dose1_jun2021_age18to64", "NC_Dose1_jun2021_age65to100", "NC_Dose1_jul2021_age0to17", "NC_Dose1_jul2021_age18to64", "NC_Dose1_jul2021_age65to100", "NC_Dose1_aug2021_age0to17", "NC_Dose1_aug2021_age18to64", "NC_Dose1_aug2021_age65to100", "NC_Dose1_sep2021_age0to17", "NC_Dose1_sep2021_age18to64", "NC_Dose1_sep2021_age65to100", "NC_Dose1_oct2021_age0to17", "NC_Dose1_oct2021_age18to64", "NC_Dose1_oct2021_age65to100", "NC_Dose3_oct2021_0to17", "NC_Dose3_oct2021_18to64", "NC_Dose3_oct2021_65to100", "NC_Dose1_nov2021_age0to17", "NC_Dose1_nov2021_age18to64", "NC_Dose1_nov2021_age65to100", "NC_Dose3_nov2021_0to17", "NC_Dose3_nov2021_18to64", "NC_Dose3_nov2021_65to100", "NC_Dose1_dec2021_age0to17", "NC_Dose1_dec2021_age18to64", "NC_Dose1_dec2021_age65to100", "NC_Dose3_dec2021_0to17", "NC_Dose3_dec2021_18to64", "NC_Dose3_dec2021_65to100", "NC_Dose1_jan2022_age0to17", "NC_Dose1_jan2022_age18to64", "NC_Dose1_jan2022_age65to100", "NC_Dose3_jan2022_0to17", "NC_Dose3_jan2022_18to64", "NC_Dose3_jan2022_65to100", "NC_Dose1_feb2022_age0to17", "NC_Dose1_feb2022_age18to64", "NC_Dose1_feb2022_age65to100", "NC_Dose3_feb2022_0to17", "NC_Dose3_feb2022_18to64", "NC_Dose3_feb2022_65to100", "NC_Dose1_mar2022_age0to17", "NC_Dose1_mar2022_age18to64", "NC_Dose1_mar2022_age65to100", "NC_Dose3_mar2022_0to17", "NC_Dose3_mar2022_18to64", "NC_Dose3_mar2022_65to100", "NC_Dose1_apr2022_age0to17", "NC_Dose1_apr2022_age18to64", "NC_Dose1_apr2022_age65to100", "NC_Dose3_apr2022_0to17", "NC_Dose3_apr2022_18to64", "NC_Dose3_apr2022_65to100", "NC_Dose1_may2022_age0to17", "NC_Dose1_may2022_age18to64", "NC_Dose1_may2022_age65to100", "NC_Dose3_may2022_0to17", "NC_Dose3_may2022_18to64", "NC_Dose3_may2022_65to100", "NC_Dose1_jun2022_age0to17", "NC_Dose1_jun2022_age18to64", "NC_Dose1_jun2022_age65to100", "NC_Dose3_jun2022_0to17", "NC_Dose3_jun2022_18to64", "NC_Dose3_jun2022_65to100", "NC_Dose1_jul2022_age0to17", "NC_Dose1_jul2022_age18to64", "NC_Dose1_jul2022_age65to100", "NC_Dose3_jul2022_0to17", "NC_Dose3_jul2022_18to64", "NC_Dose3_jul2022_65to100", "NC_Dose1_aug2022_age0to17", "NC_Dose1_aug2022_age18to64", "NC_Dose1_aug2022_age65to100", "NC_Dose3_aug2022_0to17", "NC_Dose3_aug2022_18to64", "NC_Dose3_aug2022_65to100", "NC_Dose1_sep2022_age0to17", "NC_Dose1_sep2022_age18to64", "NC_Dose1_sep2022_age65to100", "NC_Dose3_sep2022_0to17", "NC_Dose3_sep2022_18to64", "NC_Dose3_sep2022_65to100", "ND_Dose1_jan2021_age18to64", "ND_Dose1_jan2021_age65to100", "ND_Dose1_feb2021_age0to17", "ND_Dose1_feb2021_age18to64", "ND_Dose1_feb2021_age65to100", "ND_Dose1_mar2021_age0to17", "ND_Dose1_mar2021_age18to64", "ND_Dose1_mar2021_age65to100", "ND_Dose1_apr2021_age0to17", "ND_Dose1_apr2021_age18to64", "ND_Dose1_apr2021_age65to100", "ND_Dose1_may2021_age0to17", "ND_Dose1_may2021_age18to64", "ND_Dose1_may2021_age65to100", "ND_Dose1_jun2021_age0to17", "ND_Dose1_jun2021_age18to64", "ND_Dose1_jun2021_age65to100", "ND_Dose1_jul2021_age0to17", "ND_Dose1_jul2021_age18to64", "ND_Dose1_jul2021_age65to100", "ND_Dose1_aug2021_age0to17", "ND_Dose1_aug2021_age18to64", "ND_Dose1_aug2021_age65to100", "ND_Dose1_sep2021_age0to17", "ND_Dose1_sep2021_age18to64", "ND_Dose1_sep2021_age65to100", "ND_Dose1_oct2021_age0to17", "ND_Dose1_oct2021_age18to64", "ND_Dose1_oct2021_age65to100", "ND_Dose3_oct2021_0to17", "ND_Dose3_oct2021_18to64", "ND_Dose3_oct2021_65to100", "ND_Dose1_nov2021_age0to17", "ND_Dose1_nov2021_age18to64", "ND_Dose1_nov2021_age65to100", "ND_Dose3_nov2021_0to17", "ND_Dose3_nov2021_18to64", "ND_Dose3_nov2021_65to100", "ND_Dose1_dec2021_age0to17", "ND_Dose1_dec2021_age18to64", "ND_Dose1_dec2021_age65to100", "ND_Dose3_dec2021_0to17", "ND_Dose3_dec2021_18to64", "ND_Dose3_dec2021_65to100", "ND_Dose1_jan2022_age0to17", "ND_Dose1_jan2022_age18to64", "ND_Dose1_jan2022_age65to100", "ND_Dose3_jan2022_0to17", "ND_Dose3_jan2022_18to64", "ND_Dose3_jan2022_65to100", "ND_Dose1_feb2022_age0to17", "ND_Dose1_feb2022_age18to64", "ND_Dose1_feb2022_age65to100", "ND_Dose3_feb2022_0to17", "ND_Dose3_feb2022_18to64", "ND_Dose3_feb2022_65to100", "ND_Dose1_mar2022_age0to17", "ND_Dose1_mar2022_age18to64", "ND_Dose1_mar2022_age65to100", "ND_Dose3_mar2022_0to17", "ND_Dose3_mar2022_18to64", "ND_Dose3_mar2022_65to100", "ND_Dose1_apr2022_age0to17", "ND_Dose1_apr2022_age18to64", "ND_Dose1_apr2022_age65to100", "ND_Dose3_apr2022_0to17", "ND_Dose3_apr2022_18to64", "ND_Dose3_apr2022_65to100", "ND_Dose1_may2022_age0to17", "ND_Dose1_may2022_age18to64", "ND_Dose1_may2022_age65to100", "ND_Dose3_may2022_0to17", "ND_Dose3_may2022_18to64", "ND_Dose3_may2022_65to100", "ND_Dose1_jun2022_age0to17", "ND_Dose1_jun2022_age18to64", "ND_Dose1_jun2022_age65to100", "ND_Dose3_jun2022_0to17", "ND_Dose3_jun2022_18to64", "ND_Dose3_jun2022_65to100", "ND_Dose1_jul2022_age0to17", "ND_Dose1_jul2022_age18to64", "ND_Dose1_jul2022_age65to100", "ND_Dose3_jul2022_0to17", "ND_Dose3_jul2022_18to64", "ND_Dose3_jul2022_65to100", "ND_Dose1_aug2022_age0to17", "ND_Dose1_aug2022_age18to64", "ND_Dose1_aug2022_age65to100", "ND_Dose3_aug2022_0to17", "ND_Dose3_aug2022_18to64", "ND_Dose3_aug2022_65to100", "ND_Dose1_sep2022_age0to17", "ND_Dose1_sep2022_age18to64", "ND_Dose1_sep2022_age65to100", "ND_Dose3_sep2022_0to17", "ND_Dose3_sep2022_18to64", "ND_Dose3_sep2022_65to100", "OH_Dose1_jan2021_age18to64", "OH_Dose1_jan2021_age65to100", "OH_Dose1_feb2021_age0to17", "OH_Dose1_feb2021_age18to64", "OH_Dose1_feb2021_age65to100", "OH_Dose1_mar2021_age0to17", "OH_Dose1_mar2021_age18to64", "OH_Dose1_mar2021_age65to100", "OH_Dose1_apr2021_age0to17", "OH_Dose1_apr2021_age18to64", "OH_Dose1_apr2021_age65to100", "OH_Dose1_may2021_age0to17", "OH_Dose1_may2021_age18to64", "OH_Dose1_may2021_age65to100", "OH_Dose1_jun2021_age0to17", "OH_Dose1_jun2021_age18to64", "OH_Dose1_jun2021_age65to100", "OH_Dose1_jul2021_age0to17", "OH_Dose1_jul2021_age18to64", "OH_Dose1_jul2021_age65to100", "OH_Dose1_aug2021_age0to17", "OH_Dose1_aug2021_age18to64", "OH_Dose1_aug2021_age65to100", "OH_Dose1_sep2021_age0to17", "OH_Dose1_sep2021_age18to64", "OH_Dose1_sep2021_age65to100", "OH_Dose1_oct2021_age0to17", "OH_Dose1_oct2021_age18to64", "OH_Dose1_oct2021_age65to100", "OH_Dose3_oct2021_0to17", "OH_Dose3_oct2021_18to64", "OH_Dose3_oct2021_65to100", "OH_Dose1_nov2021_age0to17", "OH_Dose1_nov2021_age18to64", "OH_Dose1_nov2021_age65to100", "OH_Dose3_nov2021_0to17", "OH_Dose3_nov2021_18to64", "OH_Dose3_nov2021_65to100", "OH_Dose1_dec2021_age0to17", "OH_Dose1_dec2021_age18to64", "OH_Dose1_dec2021_age65to100", "OH_Dose3_dec2021_0to17", "OH_Dose3_dec2021_18to64", "OH_Dose3_dec2021_65to100", "OH_Dose1_jan2022_age0to17", "OH_Dose1_jan2022_age18to64", "OH_Dose1_jan2022_age65to100", "OH_Dose3_jan2022_0to17", "OH_Dose3_jan2022_18to64", "OH_Dose3_jan2022_65to100", "OH_Dose1_feb2022_age0to17", "OH_Dose1_feb2022_age18to64", "OH_Dose1_feb2022_age65to100", "OH_Dose3_feb2022_0to17", "OH_Dose3_feb2022_18to64", "OH_Dose3_feb2022_65to100", "OH_Dose1_mar2022_age0to17", "OH_Dose1_mar2022_age18to64", "OH_Dose1_mar2022_age65to100", "OH_Dose3_mar2022_0to17", "OH_Dose3_mar2022_18to64", "OH_Dose3_mar2022_65to100", "OH_Dose1_apr2022_age0to17", "OH_Dose1_apr2022_age18to64", "OH_Dose1_apr2022_age65to100", "OH_Dose3_apr2022_0to17", "OH_Dose3_apr2022_18to64", "OH_Dose3_apr2022_65to100", "OH_Dose1_may2022_age0to17", "OH_Dose1_may2022_age18to64", "OH_Dose1_may2022_age65to100", "OH_Dose3_may2022_0to17", "OH_Dose3_may2022_18to64", "OH_Dose3_may2022_65to100", "OH_Dose1_jun2022_age0to17", "OH_Dose1_jun2022_age18to64", "OH_Dose1_jun2022_age65to100", "OH_Dose3_jun2022_0to17", "OH_Dose3_jun2022_18to64", "OH_Dose3_jun2022_65to100", "OH_Dose1_jul2022_age0to17", "OH_Dose1_jul2022_age18to64", "OH_Dose1_jul2022_age65to100", "OH_Dose3_jul2022_0to17", "OH_Dose3_jul2022_18to64", "OH_Dose3_jul2022_65to100", "OH_Dose1_aug2022_age0to17", "OH_Dose1_aug2022_age18to64", "OH_Dose1_aug2022_age65to100", "OH_Dose3_aug2022_0to17", "OH_Dose3_aug2022_18to64", "OH_Dose3_aug2022_65to100", "OH_Dose1_sep2022_age0to17", "OH_Dose1_sep2022_age18to64", "OH_Dose1_sep2022_age65to100", "OH_Dose3_sep2022_0to17", "OH_Dose3_sep2022_18to64", "OH_Dose3_sep2022_65to100", "OK_Dose1_jan2021_age18to64", "OK_Dose1_jan2021_age65to100", "OK_Dose1_feb2021_age0to17", "OK_Dose1_feb2021_age18to64", "OK_Dose1_feb2021_age65to100", "OK_Dose1_mar2021_age0to17", "OK_Dose1_mar2021_age18to64", "OK_Dose1_mar2021_age65to100", "OK_Dose1_apr2021_age0to17", "OK_Dose1_apr2021_age18to64", "OK_Dose1_apr2021_age65to100", "OK_Dose1_may2021_age0to17", "OK_Dose1_may2021_age18to64", "OK_Dose1_may2021_age65to100", "OK_Dose1_jun2021_age0to17", "OK_Dose1_jun2021_age18to64", "OK_Dose1_jun2021_age65to100", "OK_Dose1_jul2021_age0to17", "OK_Dose1_jul2021_age18to64", "OK_Dose1_jul2021_age65to100", "OK_Dose1_aug2021_age0to17", "OK_Dose1_aug2021_age18to64", "OK_Dose1_aug2021_age65to100", "OK_Dose1_sep2021_age0to17", "OK_Dose1_sep2021_age18to64", "OK_Dose1_sep2021_age65to100", "OK_Dose1_oct2021_age0to17", "OK_Dose1_oct2021_age18to64", "OK_Dose1_oct2021_age65to100", "OK_Dose3_oct2021_0to17", "OK_Dose3_oct2021_18to64", "OK_Dose3_oct2021_65to100", "OK_Dose1_nov2021_age0to17", "OK_Dose1_nov2021_age18to64", "OK_Dose1_nov2021_age65to100", "OK_Dose3_nov2021_0to17", "OK_Dose3_nov2021_18to64", "OK_Dose3_nov2021_65to100", "OK_Dose1_dec2021_age0to17", "OK_Dose1_dec2021_age18to64", "OK_Dose1_dec2021_age65to100", "OK_Dose3_dec2021_0to17", "OK_Dose3_dec2021_18to64", "OK_Dose3_dec2021_65to100", "OK_Dose1_jan2022_age0to17", "OK_Dose1_jan2022_age18to64", "OK_Dose1_jan2022_age65to100", "OK_Dose3_jan2022_0to17", "OK_Dose3_jan2022_18to64", "OK_Dose3_jan2022_65to100", "OK_Dose1_feb2022_age0to17", "OK_Dose1_feb2022_age18to64", "OK_Dose1_feb2022_age65to100", "OK_Dose3_feb2022_0to17", "OK_Dose3_feb2022_18to64", "OK_Dose3_feb2022_65to100", "OK_Dose1_mar2022_age0to17", "OK_Dose1_mar2022_age18to64", "OK_Dose1_mar2022_age65to100", "OK_Dose3_mar2022_0to17", "OK_Dose3_mar2022_18to64", "OK_Dose3_mar2022_65to100", "OK_Dose1_apr2022_age0to17", "OK_Dose1_apr2022_age18to64", "OK_Dose1_apr2022_age65to100", "OK_Dose3_apr2022_0to17", "OK_Dose3_apr2022_18to64", "OK_Dose3_apr2022_65to100", "OK_Dose1_may2022_age0to17", "OK_Dose1_may2022_age18to64", "OK_Dose1_may2022_age65to100", "OK_Dose3_may2022_0to17", "OK_Dose3_may2022_18to64", "OK_Dose3_may2022_65to100", "OK_Dose1_jun2022_age0to17", "OK_Dose1_jun2022_age18to64", "OK_Dose1_jun2022_age65to100", "OK_Dose3_jun2022_0to17", "OK_Dose3_jun2022_18to64", "OK_Dose3_jun2022_65to100", "OK_Dose1_jul2022_age0to17", "OK_Dose1_jul2022_age18to64", "OK_Dose1_jul2022_age65to100", "OK_Dose3_jul2022_0to17", "OK_Dose3_jul2022_18to64", "OK_Dose3_jul2022_65to100", "OK_Dose1_aug2022_age0to17", "OK_Dose1_aug2022_age18to64", "OK_Dose1_aug2022_age65to100", "OK_Dose3_aug2022_0to17", "OK_Dose3_aug2022_18to64", "OK_Dose3_aug2022_65to100", "OK_Dose1_sep2022_age0to17", "OK_Dose1_sep2022_age18to64", "OK_Dose1_sep2022_age65to100", "OK_Dose3_sep2022_0to17", "OK_Dose3_sep2022_18to64", "OK_Dose3_sep2022_65to100", "OR_Dose1_jan2021_age18to64", "OR_Dose1_jan2021_age65to100", "OR_Dose1_feb2021_age0to17", "OR_Dose1_feb2021_age18to64", "OR_Dose1_feb2021_age65to100", "OR_Dose1_mar2021_age0to17", "OR_Dose1_mar2021_age18to64", "OR_Dose1_mar2021_age65to100", "OR_Dose1_apr2021_age0to17", "OR_Dose1_apr2021_age18to64", "OR_Dose1_apr2021_age65to100", "OR_Dose1_may2021_age0to17", "OR_Dose1_may2021_age18to64", "OR_Dose1_may2021_age65to100", "OR_Dose1_jun2021_age0to17", "OR_Dose1_jun2021_age18to64", "OR_Dose1_jun2021_age65to100", "OR_Dose1_jul2021_age0to17", "OR_Dose1_jul2021_age18to64", "OR_Dose1_jul2021_age65to100", "OR_Dose1_aug2021_age0to17", "OR_Dose1_aug2021_age18to64", "OR_Dose1_aug2021_age65to100", "OR_Dose1_sep2021_age0to17", "OR_Dose1_sep2021_age18to64", "OR_Dose1_sep2021_age65to100", "OR_Dose1_oct2021_age0to17", "OR_Dose1_oct2021_age18to64", "OR_Dose1_oct2021_age65to100", "OR_Dose3_oct2021_0to17", "OR_Dose3_oct2021_18to64", "OR_Dose3_oct2021_65to100", "OR_Dose1_nov2021_age0to17", "OR_Dose1_nov2021_age18to64", "OR_Dose1_nov2021_age65to100", "OR_Dose3_nov2021_0to17", "OR_Dose3_nov2021_18to64", "OR_Dose3_nov2021_65to100", "OR_Dose1_dec2021_age0to17", "OR_Dose1_dec2021_age18to64", "OR_Dose1_dec2021_age65to100", "OR_Dose3_dec2021_0to17", "OR_Dose3_dec2021_18to64", "OR_Dose3_dec2021_65to100", "OR_Dose1_jan2022_age0to17", "OR_Dose1_jan2022_age18to64", "OR_Dose1_jan2022_age65to100", "OR_Dose3_jan2022_0to17", "OR_Dose3_jan2022_18to64", "OR_Dose3_jan2022_65to100", "OR_Dose1_feb2022_age0to17", "OR_Dose1_feb2022_age18to64", "OR_Dose1_feb2022_age65to100", "OR_Dose3_feb2022_0to17", "OR_Dose3_feb2022_18to64", "OR_Dose3_feb2022_65to100", "OR_Dose1_mar2022_age0to17", "OR_Dose1_mar2022_age18to64", "OR_Dose1_mar2022_age65to100", "OR_Dose3_mar2022_0to17", "OR_Dose3_mar2022_18to64", "OR_Dose3_mar2022_65to100", "OR_Dose1_apr2022_age0to17", "OR_Dose1_apr2022_age18to64", "OR_Dose1_apr2022_age65to100", "OR_Dose3_apr2022_0to17", "OR_Dose3_apr2022_18to64", "OR_Dose3_apr2022_65to100", "OR_Dose1_may2022_age0to17", "OR_Dose1_may2022_age18to64", "OR_Dose1_may2022_age65to100", "OR_Dose3_may2022_0to17", "OR_Dose3_may2022_18to64", "OR_Dose3_may2022_65to100", "OR_Dose1_jun2022_age0to17", "OR_Dose1_jun2022_age18to64", "OR_Dose1_jun2022_age65to100", "OR_Dose3_jun2022_0to17", "OR_Dose3_jun2022_18to64", "OR_Dose3_jun2022_65to100", "OR_Dose1_jul2022_age0to17", "OR_Dose1_jul2022_age65to100", "OR_Dose3_jul2022_0to17", "OR_Dose3_jul2022_18to64", "OR_Dose3_jul2022_65to100", "OR_Dose1_aug2022_age0to17", "OR_Dose1_aug2022_age65to100", "OR_Dose3_aug2022_0to17", "OR_Dose3_aug2022_18to64", "OR_Dose3_aug2022_65to100", "OR_Dose1_sep2022_age0to17", "OR_Dose1_sep2022_age65to100", "OR_Dose3_sep2022_0to17", "OR_Dose3_sep2022_18to64", "OR_Dose3_sep2022_65to100", "PA_Dose1_jan2021_age18to64", "PA_Dose1_jan2021_age65to100", "PA_Dose1_feb2021_age0to17", "PA_Dose1_feb2021_age18to64", "PA_Dose1_feb2021_age65to100", "PA_Dose1_mar2021_age0to17", "PA_Dose1_mar2021_age18to64", "PA_Dose1_mar2021_age65to100", "PA_Dose1_apr2021_age0to17", "PA_Dose1_apr2021_age18to64", "PA_Dose1_apr2021_age65to100", "PA_Dose1_may2021_age0to17", "PA_Dose1_may2021_age18to64", "PA_Dose1_may2021_age65to100", "PA_Dose1_jun2021_age0to17", "PA_Dose1_jun2021_age18to64", "PA_Dose1_jun2021_age65to100", "PA_Dose1_jul2021_age0to17", "PA_Dose1_jul2021_age18to64", "PA_Dose1_aug2021_age0to17", "PA_Dose1_aug2021_age18to64", "PA_Dose1_sep2021_age0to17", "PA_Dose1_sep2021_age18to64", "PA_Dose1_oct2021_age0to17", "PA_Dose1_oct2021_age18to64", "PA_Dose3_oct2021_0to17", "PA_Dose3_oct2021_18to64", "PA_Dose3_oct2021_65to100", "PA_Dose1_nov2021_age0to17", "PA_Dose1_nov2021_age18to64", "PA_Dose1_nov2021_age65to100", "PA_Dose3_nov2021_0to17", "PA_Dose3_nov2021_18to64", "PA_Dose3_nov2021_65to100", "PA_Dose1_dec2021_age0to17", "PA_Dose1_dec2021_age18to64", "PA_Dose1_dec2021_age65to100", "PA_Dose3_dec2021_0to17", "PA_Dose3_dec2021_18to64", "PA_Dose3_dec2021_65to100", "PA_Dose1_jan2022_age0to17", "PA_Dose1_jan2022_age18to64", "PA_Dose1_jan2022_age65to100", "PA_Dose3_jan2022_0to17", "PA_Dose3_jan2022_18to64", "PA_Dose3_jan2022_65to100", "PA_Dose1_feb2022_age0to17", "PA_Dose1_feb2022_age18to64", "PA_Dose1_feb2022_age65to100", "PA_Dose3_feb2022_0to17", "PA_Dose3_feb2022_18to64", "PA_Dose3_feb2022_65to100", "PA_Dose1_mar2022_age0to17", "PA_Dose1_mar2022_age18to64", "PA_Dose1_mar2022_age65to100", "PA_Dose3_mar2022_0to17", "PA_Dose3_mar2022_18to64", "PA_Dose3_mar2022_65to100", "PA_Dose1_apr2022_age0to17", "PA_Dose1_apr2022_age18to64", "PA_Dose1_apr2022_age65to100", "PA_Dose3_apr2022_0to17", "PA_Dose3_apr2022_18to64", "PA_Dose3_apr2022_65to100", "PA_Dose1_may2022_age0to17", "PA_Dose1_may2022_age18to64", "PA_Dose1_may2022_age65to100", "PA_Dose3_may2022_0to17", "PA_Dose3_may2022_18to64", "PA_Dose1_jun2022_age0to17", "PA_Dose1_jun2022_age18to64", "PA_Dose1_jun2022_age65to100", "PA_Dose3_jun2022_0to17", "PA_Dose3_jun2022_18to64", "PA_Dose1_jul2022_age0to17", "PA_Dose1_jul2022_age18to64", "PA_Dose1_jul2022_age65to100", "PA_Dose3_jul2022_0to17", "PA_Dose3_jul2022_18to64", "PA_Dose1_aug2022_age0to17", "PA_Dose1_aug2022_age18to64", "PA_Dose1_aug2022_age65to100", "PA_Dose3_aug2022_0to17", "PA_Dose3_aug2022_18to64", "PA_Dose1_sep2022_age0to17", "PA_Dose1_sep2022_age18to64", "PA_Dose1_sep2022_age65to100", "PA_Dose3_sep2022_0to17", "PA_Dose3_sep2022_18to64", "PA_Dose3_sep2022_65to100", "RI_Dose1_jan2021_age18to64", "RI_Dose1_jan2021_age65to100", "RI_Dose1_feb2021_age0to17", "RI_Dose1_feb2021_age18to64", "RI_Dose1_feb2021_age65to100", "RI_Dose1_mar2021_age0to17", "RI_Dose1_mar2021_age18to64", "RI_Dose1_mar2021_age65to100", "RI_Dose1_apr2021_age0to17", "RI_Dose1_apr2021_age18to64", "RI_Dose1_apr2021_age65to100", "RI_Dose1_may2021_age0to17", "RI_Dose1_may2021_age18to64", "RI_Dose1_may2021_age65to100", "RI_Dose1_jun2021_age0to17", "RI_Dose1_jun2021_age18to64", "RI_Dose1_jun2021_age65to100", "RI_Dose1_jul2021_age0to17", "RI_Dose1_jul2021_age18to64", "RI_Dose1_jul2021_age65to100", "RI_Dose1_aug2021_age0to17", "RI_Dose1_aug2021_age18to64", "RI_Dose1_aug2021_age65to100", "RI_Dose1_sep2021_age0to17", "RI_Dose1_sep2021_age18to64", "RI_Dose1_sep2021_age65to100", "RI_Dose1_oct2021_age0to17", "RI_Dose1_oct2021_age18to64", "RI_Dose1_oct2021_age65to100", "RI_Dose3_oct2021_0to17", "RI_Dose3_oct2021_18to64", "RI_Dose3_oct2021_65to100", "RI_Dose1_nov2021_age0to17", "RI_Dose1_nov2021_age18to64", "RI_Dose1_nov2021_age65to100", "RI_Dose3_nov2021_0to17", "RI_Dose3_nov2021_18to64", "RI_Dose3_nov2021_65to100", "RI_Dose1_dec2021_age0to17", "RI_Dose1_dec2021_age18to64", "RI_Dose1_dec2021_age65to100", "RI_Dose3_dec2021_0to17", "RI_Dose3_dec2021_18to64", "RI_Dose3_dec2021_65to100", "RI_Dose1_jan2022_age0to17", "RI_Dose1_jan2022_age18to64", "RI_Dose1_jan2022_age65to100", "RI_Dose3_jan2022_0to17", "RI_Dose3_jan2022_18to64", "RI_Dose3_jan2022_65to100", "RI_Dose1_feb2022_age0to17", "RI_Dose1_feb2022_age18to64", "RI_Dose1_feb2022_age65to100", "RI_Dose3_feb2022_0to17", "RI_Dose3_feb2022_18to64", "RI_Dose3_feb2022_65to100", "RI_Dose1_mar2022_age0to17", "RI_Dose1_mar2022_age18to64", "RI_Dose1_mar2022_age65to100", "RI_Dose3_mar2022_0to17", "RI_Dose3_mar2022_18to64", "RI_Dose3_mar2022_65to100", "RI_Dose1_apr2022_age0to17", "RI_Dose1_apr2022_age18to64", "RI_Dose1_apr2022_age65to100", "RI_Dose3_apr2022_0to17", "RI_Dose3_apr2022_18to64", "RI_Dose3_apr2022_65to100", "RI_Dose1_may2022_age0to17", "RI_Dose1_may2022_age18to64", "RI_Dose1_may2022_age65to100", "RI_Dose3_may2022_0to17", "RI_Dose3_may2022_18to64", "RI_Dose3_may2022_65to100", "RI_Dose1_jun2022_age0to17", "RI_Dose1_jun2022_age18to64", "RI_Dose3_jun2022_0to17", "RI_Dose3_jun2022_18to64", "RI_Dose3_jun2022_65to100", "RI_Dose1_jul2022_age0to17", "RI_Dose1_jul2022_age18to64", "RI_Dose1_jul2022_age65to100", "RI_Dose3_jul2022_0to17", "RI_Dose3_jul2022_18to64", "RI_Dose3_jul2022_65to100", "RI_Dose1_aug2022_age0to17", "RI_Dose1_aug2022_age18to64", "RI_Dose3_aug2022_0to17", "RI_Dose3_aug2022_18to64", "RI_Dose1_sep2022_age0to17", "RI_Dose1_sep2022_age18to64", "RI_Dose3_sep2022_0to17", "RI_Dose3_sep2022_18to64", "SC_Dose1_jan2021_age18to64", "SC_Dose1_jan2021_age65to100", "SC_Dose1_feb2021_age0to17", "SC_Dose1_feb2021_age18to64", "SC_Dose1_feb2021_age65to100", "SC_Dose1_mar2021_age0to17", "SC_Dose1_mar2021_age18to64", "SC_Dose1_mar2021_age65to100", "SC_Dose1_apr2021_age0to17", "SC_Dose1_apr2021_age18to64", "SC_Dose1_apr2021_age65to100", "SC_Dose1_may2021_age0to17", "SC_Dose1_may2021_age18to64", "SC_Dose1_may2021_age65to100", "SC_Dose1_jun2021_age0to17", "SC_Dose1_jun2021_age18to64", "SC_Dose1_jun2021_age65to100", "SC_Dose1_jul2021_age0to17", "SC_Dose1_jul2021_age18to64", "SC_Dose1_jul2021_age65to100", "SC_Dose1_aug2021_age0to17", "SC_Dose1_aug2021_age18to64", "SC_Dose1_aug2021_age65to100", "SC_Dose1_sep2021_age0to17", "SC_Dose1_sep2021_age18to64", "SC_Dose1_sep2021_age65to100", "SC_Dose1_oct2021_age0to17", "SC_Dose1_oct2021_age18to64", "SC_Dose1_oct2021_age65to100", "SC_Dose3_oct2021_0to17", "SC_Dose3_oct2021_18to64", "SC_Dose3_oct2021_65to100", "SC_Dose1_nov2021_age0to17", "SC_Dose1_nov2021_age18to64", "SC_Dose1_nov2021_age65to100", "SC_Dose3_nov2021_0to17", "SC_Dose3_nov2021_18to64", "SC_Dose3_nov2021_65to100", "SC_Dose1_dec2021_age0to17", "SC_Dose1_dec2021_age18to64", "SC_Dose1_dec2021_age65to100", "SC_Dose3_dec2021_0to17", "SC_Dose3_dec2021_18to64", "SC_Dose3_dec2021_65to100", "SC_Dose1_jan2022_age0to17", "SC_Dose1_jan2022_age18to64", "SC_Dose1_jan2022_age65to100", "SC_Dose3_jan2022_0to17", "SC_Dose3_jan2022_18to64", "SC_Dose3_jan2022_65to100", "SC_Dose1_feb2022_age0to17", "SC_Dose1_feb2022_age18to64", "SC_Dose1_feb2022_age65to100", "SC_Dose3_feb2022_0to17", "SC_Dose3_feb2022_18to64", "SC_Dose3_feb2022_65to100", "SC_Dose1_mar2022_age0to17", "SC_Dose1_mar2022_age18to64", "SC_Dose1_mar2022_age65to100", "SC_Dose3_mar2022_0to17", "SC_Dose3_mar2022_18to64", "SC_Dose3_mar2022_65to100", "SC_Dose1_apr2022_age0to17", "SC_Dose1_apr2022_age18to64", "SC_Dose1_apr2022_age65to100", "SC_Dose3_apr2022_0to17", "SC_Dose3_apr2022_18to64", "SC_Dose3_apr2022_65to100", "SC_Dose1_may2022_age0to17", "SC_Dose1_may2022_age18to64", "SC_Dose1_may2022_age65to100", "SC_Dose3_may2022_0to17", "SC_Dose3_may2022_18to64", "SC_Dose3_may2022_65to100", "SC_Dose1_jun2022_age0to17", "SC_Dose1_jun2022_age18to64", "SC_Dose1_jun2022_age65to100", "SC_Dose3_jun2022_0to17", "SC_Dose3_jun2022_18to64", "SC_Dose3_jun2022_65to100", "SC_Dose1_jul2022_age0to17", "SC_Dose1_jul2022_age18to64", "SC_Dose1_jul2022_age65to100", "SC_Dose3_jul2022_0to17", "SC_Dose3_jul2022_18to64", "SC_Dose3_jul2022_65to100", "SC_Dose1_aug2022_age0to17", "SC_Dose1_aug2022_age18to64", "SC_Dose1_aug2022_age65to100", "SC_Dose3_aug2022_0to17", "SC_Dose3_aug2022_18to64", "SC_Dose3_aug2022_65to100", "SC_Dose1_sep2022_age0to17", "SC_Dose1_sep2022_age18to64", "SC_Dose1_sep2022_age65to100", "SC_Dose3_sep2022_0to17", "SC_Dose3_sep2022_18to64", "SC_Dose3_sep2022_65to100", "SD_Dose1_jan2021_age18to64", "SD_Dose1_jan2021_age65to100", "SD_Dose1_feb2021_age0to17", "SD_Dose1_feb2021_age18to64", "SD_Dose1_feb2021_age65to100", "SD_Dose1_mar2021_age0to17", "SD_Dose1_mar2021_age18to64", "SD_Dose1_mar2021_age65to100", "SD_Dose1_apr2021_age0to17", "SD_Dose1_apr2021_age18to64", "SD_Dose1_apr2021_age65to100", "SD_Dose1_may2021_age0to17", "SD_Dose1_may2021_age18to64", "SD_Dose1_may2021_age65to100", "SD_Dose1_jun2021_age0to17", "SD_Dose1_jun2021_age18to64", "SD_Dose1_jun2021_age65to100", "SD_Dose1_jul2021_age0to17", "SD_Dose1_jul2021_age18to64", "SD_Dose1_jul2021_age65to100", "SD_Dose1_aug2021_age0to17", "SD_Dose1_aug2021_age18to64", "SD_Dose1_aug2021_age65to100", "SD_Dose1_sep2021_age0to17", "SD_Dose1_sep2021_age18to64", "SD_Dose1_sep2021_age65to100", "SD_Dose1_oct2021_age0to17", "SD_Dose1_oct2021_age18to64", "SD_Dose1_oct2021_age65to100", "SD_Dose3_oct2021_0to17", "SD_Dose3_oct2021_18to64", "SD_Dose3_oct2021_65to100", "SD_Dose1_nov2021_age0to17", "SD_Dose1_nov2021_age18to64", "SD_Dose1_nov2021_age65to100", "SD_Dose3_nov2021_0to17", "SD_Dose3_nov2021_18to64", "SD_Dose3_nov2021_65to100", "SD_Dose1_dec2021_age0to17", "SD_Dose1_dec2021_age18to64", "SD_Dose1_dec2021_age65to100", "SD_Dose3_dec2021_0to17", "SD_Dose3_dec2021_18to64", "SD_Dose3_dec2021_65to100", "SD_Dose1_jan2022_age0to17", "SD_Dose1_jan2022_age18to64", "SD_Dose1_jan2022_age65to100", "SD_Dose3_jan2022_0to17", "SD_Dose3_jan2022_18to64", "SD_Dose3_jan2022_65to100", "SD_Dose1_feb2022_age0to17", "SD_Dose1_feb2022_age18to64", "SD_Dose1_feb2022_age65to100", "SD_Dose3_feb2022_0to17", "SD_Dose3_feb2022_18to64", "SD_Dose3_feb2022_65to100", "SD_Dose1_mar2022_age0to17", "SD_Dose1_mar2022_age18to64", "SD_Dose1_mar2022_age65to100", "SD_Dose3_mar2022_0to17", "SD_Dose3_mar2022_18to64", "SD_Dose3_mar2022_65to100", "SD_Dose1_apr2022_age0to17", "SD_Dose1_apr2022_age18to64", "SD_Dose1_apr2022_age65to100", "SD_Dose3_apr2022_0to17", "SD_Dose3_apr2022_18to64", "SD_Dose3_apr2022_65to100", "SD_Dose1_may2022_age0to17", "SD_Dose1_may2022_age18to64", "SD_Dose1_may2022_age65to100", "SD_Dose3_may2022_0to17", "SD_Dose3_may2022_18to64", "SD_Dose3_may2022_65to100", "SD_Dose1_jun2022_age0to17", "SD_Dose1_jun2022_age18to64", "SD_Dose3_jun2022_0to17", "SD_Dose3_jun2022_18to64", "SD_Dose3_jun2022_65to100", "SD_Dose1_jul2022_age0to17", "SD_Dose1_jul2022_age18to64", "SD_Dose1_jul2022_age65to100", "SD_Dose3_jul2022_0to17", "SD_Dose3_jul2022_18to64", "SD_Dose3_jul2022_65to100", "SD_Dose1_aug2022_age0to17", "SD_Dose1_aug2022_age18to64", "SD_Dose3_aug2022_0to17", "SD_Dose3_aug2022_18to64", "SD_Dose1_sep2022_age0to17", "SD_Dose1_sep2022_age18to64", "SD_Dose3_sep2022_0to17", "SD_Dose3_sep2022_18to64", "TN_Dose1_jan2021_age18to64", "TN_Dose1_jan2021_age65to100", "TN_Dose1_feb2021_age0to17", "TN_Dose1_feb2021_age18to64", "TN_Dose1_feb2021_age65to100", "TN_Dose1_mar2021_age0to17", "TN_Dose1_mar2021_age18to64", "TN_Dose1_mar2021_age65to100", "TN_Dose1_apr2021_age0to17", "TN_Dose1_apr2021_age18to64", "TN_Dose1_apr2021_age65to100", "TN_Dose1_may2021_age0to17", "TN_Dose1_may2021_age18to64", "TN_Dose1_may2021_age65to100", "TN_Dose1_jun2021_age0to17", "TN_Dose1_jun2021_age18to64", "TN_Dose1_jun2021_age65to100", "TN_Dose1_jul2021_age0to17", "TN_Dose1_jul2021_age18to64", "TN_Dose1_jul2021_age65to100", "TN_Dose1_aug2021_age0to17", "TN_Dose1_aug2021_age18to64", "TN_Dose1_aug2021_age65to100", "TN_Dose1_sep2021_age0to17", "TN_Dose1_sep2021_age18to64", "TN_Dose1_sep2021_age65to100", "TN_Dose1_oct2021_age0to17", "TN_Dose1_oct2021_age18to64", "TN_Dose1_oct2021_age65to100", "TN_Dose3_oct2021_0to17", "TN_Dose3_oct2021_18to64", "TN_Dose3_oct2021_65to100", "TN_Dose1_nov2021_age0to17", "TN_Dose1_nov2021_age18to64", "TN_Dose1_nov2021_age65to100", "TN_Dose3_nov2021_0to17", "TN_Dose3_nov2021_18to64", "TN_Dose3_nov2021_65to100", "TN_Dose1_dec2021_age0to17", "TN_Dose1_dec2021_age18to64", "TN_Dose1_dec2021_age65to100", "TN_Dose3_dec2021_0to17", "TN_Dose3_dec2021_18to64", "TN_Dose3_dec2021_65to100", "TN_Dose1_jan2022_age0to17", "TN_Dose1_jan2022_age18to64", "TN_Dose1_jan2022_age65to100", "TN_Dose3_jan2022_0to17", "TN_Dose3_jan2022_18to64", "TN_Dose3_jan2022_65to100", "TN_Dose1_feb2022_age0to17", "TN_Dose1_feb2022_age18to64", "TN_Dose1_feb2022_age65to100", "TN_Dose3_feb2022_0to17", "TN_Dose3_feb2022_18to64", "TN_Dose3_feb2022_65to100", "TN_Dose1_mar2022_age0to17", "TN_Dose1_mar2022_age18to64", "TN_Dose1_mar2022_age65to100", "TN_Dose3_mar2022_0to17", "TN_Dose3_mar2022_18to64", "TN_Dose3_mar2022_65to100", "TN_Dose1_apr2022_age0to17", "TN_Dose1_apr2022_age18to64", "TN_Dose1_apr2022_age65to100", "TN_Dose3_apr2022_0to17", "TN_Dose3_apr2022_18to64", "TN_Dose3_apr2022_65to100", "TN_Dose1_may2022_age0to17", "TN_Dose1_may2022_age18to64", "TN_Dose1_may2022_age65to100", "TN_Dose3_may2022_0to17", "TN_Dose3_may2022_18to64", "TN_Dose3_may2022_65to100", "TN_Dose1_jun2022_age0to17", "TN_Dose1_jun2022_age18to64", "TN_Dose1_jun2022_age65to100", "TN_Dose3_jun2022_0to17", "TN_Dose3_jun2022_18to64", "TN_Dose3_jun2022_65to100", "TN_Dose1_jul2022_age0to17", "TN_Dose1_jul2022_age18to64", "TN_Dose1_jul2022_age65to100", "TN_Dose3_jul2022_0to17", "TN_Dose3_jul2022_18to64", "TN_Dose3_jul2022_65to100", "TN_Dose1_aug2022_age0to17", "TN_Dose1_aug2022_age18to64", "TN_Dose1_aug2022_age65to100", "TN_Dose3_aug2022_0to17", "TN_Dose3_aug2022_18to64", "TN_Dose3_aug2022_65to100", "TN_Dose1_sep2022_age0to17", "TN_Dose1_sep2022_age18to64", "TN_Dose1_sep2022_age65to100", "TN_Dose3_sep2022_0to17", "TN_Dose3_sep2022_18to64", "TN_Dose3_sep2022_65to100", "TX_Dose1_jan2021_age18to64", "TX_Dose1_jan2021_age65to100", "TX_Dose1_feb2021_age0to17", "TX_Dose1_feb2021_age18to64", "TX_Dose1_feb2021_age65to100", "TX_Dose1_mar2021_age0to17", "TX_Dose1_mar2021_age18to64", "TX_Dose1_mar2021_age65to100", "TX_Dose1_apr2021_age0to17", "TX_Dose1_apr2021_age18to64", "TX_Dose1_apr2021_age65to100", "TX_Dose1_may2021_age0to17", "TX_Dose1_may2021_age18to64", "TX_Dose1_may2021_age65to100", "TX_Dose1_jun2021_age0to17", "TX_Dose1_jun2021_age18to64", "TX_Dose1_jun2021_age65to100", "TX_Dose1_jul2021_age0to17", "TX_Dose1_jul2021_age18to64", "TX_Dose1_jul2021_age65to100", "TX_Dose1_aug2021_age0to17", "TX_Dose1_aug2021_age18to64", "TX_Dose1_aug2021_age65to100", "TX_Dose1_sep2021_age0to17", "TX_Dose1_sep2021_age18to64", "TX_Dose1_sep2021_age65to100", "TX_Dose1_oct2021_age0to17", "TX_Dose1_oct2021_age18to64", "TX_Dose1_oct2021_age65to100", "TX_Dose3_oct2021_0to17", "TX_Dose3_oct2021_18to64", "TX_Dose3_oct2021_65to100", "TX_Dose1_nov2021_age0to17", "TX_Dose1_nov2021_age18to64", "TX_Dose1_nov2021_age65to100", "TX_Dose3_nov2021_0to17", "TX_Dose3_nov2021_18to64", "TX_Dose3_nov2021_65to100", "TX_Dose1_dec2021_age0to17", "TX_Dose1_dec2021_age18to64", "TX_Dose1_dec2021_age65to100", "TX_Dose3_dec2021_0to17", "TX_Dose3_dec2021_18to64", "TX_Dose3_dec2021_65to100", "TX_Dose1_jan2022_age0to17", "TX_Dose1_jan2022_age18to64", "TX_Dose1_jan2022_age65to100", "TX_Dose3_jan2022_0to17", "TX_Dose3_jan2022_18to64", "TX_Dose3_jan2022_65to100", "TX_Dose1_feb2022_age0to17", "TX_Dose1_feb2022_age18to64", "TX_Dose1_feb2022_age65to100", "TX_Dose3_feb2022_0to17", "TX_Dose3_feb2022_18to64", "TX_Dose3_feb2022_65to100", "TX_Dose1_mar2022_age0to17", "TX_Dose1_mar2022_age18to64", "TX_Dose1_mar2022_age65to100", "TX_Dose3_mar2022_0to17", "TX_Dose3_mar2022_18to64", "TX_Dose3_mar2022_65to100", "TX_Dose1_apr2022_age0to17", "TX_Dose1_apr2022_age18to64", "TX_Dose1_apr2022_age65to100", "TX_Dose3_apr2022_0to17", "TX_Dose3_apr2022_18to64", "TX_Dose3_apr2022_65to100", "TX_Dose1_may2022_age0to17", "TX_Dose1_may2022_age18to64", "TX_Dose1_may2022_age65to100", "TX_Dose3_may2022_0to17", "TX_Dose3_may2022_18to64", "TX_Dose3_may2022_65to100", "TX_Dose1_jun2022_age0to17", "TX_Dose1_jun2022_age18to64", "TX_Dose1_jun2022_age65to100", "TX_Dose3_jun2022_0to17", "TX_Dose3_jun2022_18to64", "TX_Dose3_jun2022_65to100", "TX_Dose1_jul2022_age0to17", "TX_Dose1_jul2022_age18to64", "TX_Dose1_jul2022_age65to100", "TX_Dose3_jul2022_0to17", "TX_Dose3_jul2022_18to64", "TX_Dose3_jul2022_65to100", "TX_Dose1_aug2022_age0to17", "TX_Dose1_aug2022_age18to64", "TX_Dose1_aug2022_age65to100", "TX_Dose3_aug2022_0to17", "TX_Dose3_aug2022_18to64", "TX_Dose3_aug2022_65to100", "TX_Dose1_sep2022_age0to17", "TX_Dose1_sep2022_age18to64", "TX_Dose1_sep2022_age65to100", "TX_Dose3_sep2022_0to17", "TX_Dose3_sep2022_18to64", "TX_Dose3_sep2022_65to100", "UT_Dose1_jan2021_age18to64", "UT_Dose1_jan2021_age65to100", "UT_Dose1_feb2021_age0to17", "UT_Dose1_feb2021_age18to64", "UT_Dose1_feb2021_age65to100", "UT_Dose1_mar2021_age0to17", "UT_Dose1_mar2021_age18to64", "UT_Dose1_mar2021_age65to100", "UT_Dose1_apr2021_age0to17", "UT_Dose1_apr2021_age18to64", "UT_Dose1_apr2021_age65to100", "UT_Dose1_may2021_age0to17", "UT_Dose1_may2021_age18to64", "UT_Dose1_may2021_age65to100", "UT_Dose1_jun2021_age0to17", "UT_Dose1_jun2021_age18to64", "UT_Dose1_jun2021_age65to100", "UT_Dose1_jul2021_age0to17", "UT_Dose1_jul2021_age18to64", "UT_Dose1_jul2021_age65to100", "UT_Dose1_aug2021_age0to17", "UT_Dose1_aug2021_age18to64", "UT_Dose1_aug2021_age65to100", "UT_Dose1_sep2021_age0to17", "UT_Dose1_sep2021_age18to64", "UT_Dose1_sep2021_age65to100", "UT_Dose1_oct2021_age0to17", "UT_Dose1_oct2021_age18to64", "UT_Dose1_oct2021_age65to100", "UT_Dose3_oct2021_0to17", "UT_Dose3_oct2021_18to64", "UT_Dose3_oct2021_65to100", "UT_Dose1_nov2021_age0to17", "UT_Dose1_nov2021_age18to64", "UT_Dose1_nov2021_age65to100", "UT_Dose3_nov2021_0to17", "UT_Dose3_nov2021_18to64", "UT_Dose3_nov2021_65to100", "UT_Dose1_dec2021_age0to17", "UT_Dose1_dec2021_age18to64", "UT_Dose1_dec2021_age65to100", "UT_Dose3_dec2021_0to17", "UT_Dose3_dec2021_18to64", "UT_Dose3_dec2021_65to100", "UT_Dose1_jan2022_age0to17", "UT_Dose1_jan2022_age18to64", "UT_Dose1_jan2022_age65to100", "UT_Dose3_jan2022_0to17", "UT_Dose3_jan2022_18to64", "UT_Dose3_jan2022_65to100", "UT_Dose1_feb2022_age0to17", "UT_Dose1_feb2022_age18to64", "UT_Dose1_feb2022_age65to100", "UT_Dose3_feb2022_0to17", "UT_Dose3_feb2022_18to64", "UT_Dose3_feb2022_65to100", "UT_Dose1_mar2022_age0to17", "UT_Dose1_mar2022_age18to64", "UT_Dose1_mar2022_age65to100", "UT_Dose3_mar2022_0to17", "UT_Dose3_mar2022_18to64", "UT_Dose3_mar2022_65to100", "UT_Dose1_apr2022_age0to17", "UT_Dose1_apr2022_age18to64", "UT_Dose1_apr2022_age65to100", "UT_Dose3_apr2022_0to17", "UT_Dose3_apr2022_18to64", "UT_Dose3_apr2022_65to100", "UT_Dose1_may2022_age0to17", "UT_Dose1_may2022_age18to64", "UT_Dose1_may2022_age65to100", "UT_Dose3_may2022_0to17", "UT_Dose3_may2022_18to64", "UT_Dose3_may2022_65to100", "UT_Dose1_jun2022_age0to17", "UT_Dose1_jun2022_age18to64", "UT_Dose1_jun2022_age65to100", "UT_Dose3_jun2022_0to17", "UT_Dose3_jun2022_18to64", "UT_Dose3_jun2022_65to100", "UT_Dose1_jul2022_age0to17", "UT_Dose1_jul2022_age18to64", "UT_Dose1_jul2022_age65to100", "UT_Dose3_jul2022_0to17", "UT_Dose3_jul2022_18to64", "UT_Dose3_jul2022_65to100", "UT_Dose1_aug2022_age0to17", "UT_Dose1_aug2022_age18to64", "UT_Dose1_aug2022_age65to100", "UT_Dose3_aug2022_0to17", "UT_Dose3_aug2022_18to64", "UT_Dose3_aug2022_65to100", "UT_Dose1_sep2022_age0to17", "UT_Dose1_sep2022_age18to64", "UT_Dose1_sep2022_age65to100", "UT_Dose3_sep2022_0to17", "UT_Dose3_sep2022_18to64", "UT_Dose3_sep2022_65to100", "VT_Dose1_jan2021_age18to64", "VT_Dose1_jan2021_age65to100", "VT_Dose1_feb2021_age0to17", "VT_Dose1_feb2021_age18to64", "VT_Dose1_feb2021_age65to100", "VT_Dose1_mar2021_age0to17", "VT_Dose1_mar2021_age18to64", "VT_Dose1_mar2021_age65to100", "VT_Dose1_apr2021_age0to17", "VT_Dose1_apr2021_age18to64", "VT_Dose1_apr2021_age65to100", "VT_Dose1_may2021_age0to17", "VT_Dose1_may2021_age18to64", "VT_Dose1_may2021_age65to100", "VT_Dose1_jun2021_age0to17", "VT_Dose1_jun2021_age18to64", "VT_Dose1_jun2021_age65to100", "VT_Dose1_jul2021_age0to17", "VT_Dose1_jul2021_age18to64", "VT_Dose1_aug2021_age0to17", "VT_Dose1_aug2021_age18to64", "VT_Dose1_sep2021_age0to17", "VT_Dose1_sep2021_age18to64", "VT_Dose1_oct2021_age0to17", "VT_Dose1_oct2021_age18to64", "VT_Dose3_oct2021_0to17", "VT_Dose3_oct2021_18to64", "VT_Dose3_oct2021_65to100", "VT_Dose1_nov2021_age0to17", "VT_Dose1_nov2021_age18to64", "VT_Dose1_nov2021_age65to100", "VT_Dose3_nov2021_0to17", "VT_Dose3_nov2021_18to64", "VT_Dose3_nov2021_65to100", "VT_Dose1_dec2021_age0to17", "VT_Dose1_dec2021_age18to64", "VT_Dose1_dec2021_age65to100", "VT_Dose3_dec2021_0to17", "VT_Dose3_dec2021_18to64", "VT_Dose3_dec2021_65to100", "VT_Dose1_jan2022_age0to17", "VT_Dose1_jan2022_age18to64", "VT_Dose1_jan2022_age65to100", "VT_Dose3_jan2022_0to17", "VT_Dose3_jan2022_18to64", "VT_Dose3_jan2022_65to100", "VT_Dose1_feb2022_age0to17", "VT_Dose1_feb2022_age18to64", "VT_Dose1_feb2022_age65to100", "VT_Dose3_feb2022_0to17", "VT_Dose3_feb2022_18to64", "VT_Dose3_feb2022_65to100", "VT_Dose1_mar2022_age0to17", "VT_Dose1_mar2022_age18to64", "VT_Dose1_mar2022_age65to100", "VT_Dose3_mar2022_0to17", "VT_Dose3_mar2022_18to64", "VT_Dose3_mar2022_65to100", "VT_Dose1_apr2022_age0to17", "VT_Dose1_apr2022_age18to64", "VT_Dose1_apr2022_age65to100", "VT_Dose3_apr2022_0to17", "VT_Dose3_apr2022_18to64", "VT_Dose1_may2022_age0to17", "VT_Dose1_may2022_age18to64", "VT_Dose1_may2022_age65to100", "VT_Dose3_may2022_0to17", "VT_Dose3_may2022_18to64", "VT_Dose1_jun2022_age0to17", "VT_Dose1_jun2022_age18to64", "VT_Dose1_jun2022_age65to100", "VT_Dose3_jun2022_0to17", "VT_Dose3_jun2022_18to64", "VT_Dose1_jul2022_age0to17", "VT_Dose1_jul2022_age18to64", "VT_Dose3_jul2022_0to17", "VT_Dose3_jul2022_18to64", "VT_Dose1_aug2022_age0to17", "VT_Dose1_aug2022_age18to64", "VT_Dose3_aug2022_0to17", "VT_Dose3_aug2022_18to64", "VT_Dose1_sep2022_age0to17", "VT_Dose1_sep2022_age18to64", "VT_Dose3_sep2022_0to17", "VT_Dose3_sep2022_18to64", "VA_Dose1_jan2021_age18to64", "VA_Dose1_jan2021_age65to100", "VA_Dose1_feb2021_age0to17", "VA_Dose1_feb2021_age18to64", "VA_Dose1_feb2021_age65to100", "VA_Dose1_mar2021_age0to17", "VA_Dose1_mar2021_age18to64", "VA_Dose1_mar2021_age65to100", "VA_Dose1_apr2021_age0to17", "VA_Dose1_apr2021_age18to64", "VA_Dose1_apr2021_age65to100", "VA_Dose1_may2021_age0to17", "VA_Dose1_may2021_age18to64", "VA_Dose1_may2021_age65to100", "VA_Dose1_jun2021_age0to17", "VA_Dose1_jun2021_age18to64", "VA_Dose1_jun2021_age65to100", "VA_Dose1_jul2021_age0to17", "VA_Dose1_jul2021_age18to64", "VA_Dose1_jul2021_age65to100", "VA_Dose1_aug2021_age0to17", "VA_Dose1_aug2021_age18to64", "VA_Dose1_aug2021_age65to100", "VA_Dose1_sep2021_age0to17", "VA_Dose1_sep2021_age18to64", "VA_Dose1_sep2021_age65to100", "VA_Dose1_oct2021_age0to17", "VA_Dose1_oct2021_age18to64", "VA_Dose1_oct2021_age65to100", "VA_Dose3_oct2021_0to17", "VA_Dose3_oct2021_18to64", "VA_Dose3_oct2021_65to100", "VA_Dose1_nov2021_age0to17", "VA_Dose1_nov2021_age18to64", "VA_Dose1_nov2021_age65to100", "VA_Dose3_nov2021_0to17", "VA_Dose3_nov2021_18to64", "VA_Dose3_nov2021_65to100", "VA_Dose1_dec2021_age0to17", "VA_Dose1_dec2021_age18to64", "VA_Dose1_dec2021_age65to100", "VA_Dose3_dec2021_0to17", "VA_Dose3_dec2021_18to64", "VA_Dose3_dec2021_65to100", "VA_Dose1_jan2022_age0to17", "VA_Dose1_jan2022_age18to64", "VA_Dose1_jan2022_age65to100", "VA_Dose3_jan2022_0to17", "VA_Dose3_jan2022_18to64", "VA_Dose3_jan2022_65to100", "VA_Dose1_feb2022_age0to17", "VA_Dose1_feb2022_age18to64", "VA_Dose1_feb2022_age65to100", "VA_Dose3_feb2022_0to17", "VA_Dose3_feb2022_18to64", "VA_Dose3_feb2022_65to100", "VA_Dose1_mar2022_age0to17", "VA_Dose1_mar2022_age18to64", "VA_Dose1_mar2022_age65to100", "VA_Dose3_mar2022_0to17", "VA_Dose3_mar2022_18to64", "VA_Dose3_mar2022_65to100", "VA_Dose1_apr2022_age0to17", "VA_Dose1_apr2022_age18to64", "VA_Dose1_apr2022_age65to100", "VA_Dose3_apr2022_0to17", "VA_Dose3_apr2022_18to64", "VA_Dose3_apr2022_65to100", "VA_Dose1_may2022_age0to17", "VA_Dose1_may2022_age18to64", "VA_Dose1_may2022_age65to100", "VA_Dose3_may2022_0to17", "VA_Dose3_may2022_18to64", "VA_Dose3_may2022_65to100", "VA_Dose1_jun2022_age0to17", "VA_Dose1_jun2022_age18to64", "VA_Dose1_jun2022_age65to100", "VA_Dose3_jun2022_0to17", "VA_Dose3_jun2022_18to64", "VA_Dose3_jun2022_65to100", "VA_Dose1_jul2022_age0to17", "VA_Dose1_jul2022_age18to64", "VA_Dose1_jul2022_age65to100", "VA_Dose3_jul2022_0to17", "VA_Dose3_jul2022_18to64", "VA_Dose3_jul2022_65to100", "VA_Dose1_aug2022_age0to17", "VA_Dose1_aug2022_age18to64", "VA_Dose1_aug2022_age65to100", "VA_Dose3_aug2022_0to17", "VA_Dose3_aug2022_18to64", "VA_Dose3_aug2022_65to100", "VA_Dose1_sep2022_age0to17", "VA_Dose1_sep2022_age18to64", "VA_Dose1_sep2022_age65to100", "VA_Dose3_sep2022_0to17", "VA_Dose3_sep2022_18to64", "VA_Dose3_sep2022_65to100", "WA_Dose1_jan2021_age18to64", "WA_Dose1_jan2021_age65to100", "WA_Dose1_feb2021_age0to17", "WA_Dose1_feb2021_age18to64", "WA_Dose1_feb2021_age65to100", "WA_Dose1_mar2021_age0to17", "WA_Dose1_mar2021_age18to64", "WA_Dose1_mar2021_age65to100", "WA_Dose1_apr2021_age0to17", "WA_Dose1_apr2021_age18to64", "WA_Dose1_apr2021_age65to100", "WA_Dose1_may2021_age0to17", "WA_Dose1_may2021_age18to64", "WA_Dose1_may2021_age65to100", "WA_Dose1_jun2021_age0to17", "WA_Dose1_jun2021_age18to64", "WA_Dose1_jun2021_age65to100", "WA_Dose1_jul2021_age0to17", "WA_Dose1_jul2021_age18to64", "WA_Dose1_jul2021_age65to100", "WA_Dose1_aug2021_age0to17", "WA_Dose1_aug2021_age18to64", "WA_Dose1_aug2021_age65to100", "WA_Dose1_sep2021_age0to17", "WA_Dose1_sep2021_age18to64", "WA_Dose1_sep2021_age65to100", "WA_Dose1_oct2021_age0to17", "WA_Dose1_oct2021_age18to64", "WA_Dose1_oct2021_age65to100", "WA_Dose3_oct2021_0to17", "WA_Dose3_oct2021_18to64", "WA_Dose3_oct2021_65to100", "WA_Dose1_nov2021_age0to17", "WA_Dose1_nov2021_age18to64", "WA_Dose1_nov2021_age65to100", "WA_Dose3_nov2021_0to17", "WA_Dose3_nov2021_18to64", "WA_Dose3_nov2021_65to100", "WA_Dose1_dec2021_age0to17", "WA_Dose1_dec2021_age18to64", "WA_Dose1_dec2021_age65to100", "WA_Dose3_dec2021_0to17", "WA_Dose3_dec2021_18to64", "WA_Dose3_dec2021_65to100", "WA_Dose1_jan2022_age0to17", "WA_Dose1_jan2022_age18to64", "WA_Dose1_jan2022_age65to100", "WA_Dose3_jan2022_0to17", "WA_Dose3_jan2022_18to64", "WA_Dose3_jan2022_65to100", "WA_Dose1_feb2022_age0to17", "WA_Dose1_feb2022_age18to64", "WA_Dose1_feb2022_age65to100", "WA_Dose3_feb2022_0to17", "WA_Dose3_feb2022_18to64", "WA_Dose3_feb2022_65to100", "WA_Dose1_mar2022_age0to17", "WA_Dose1_mar2022_age18to64", "WA_Dose1_mar2022_age65to100", "WA_Dose3_mar2022_0to17", "WA_Dose3_mar2022_18to64", "WA_Dose3_mar2022_65to100", "WA_Dose1_apr2022_age0to17", "WA_Dose1_apr2022_age18to64", "WA_Dose1_apr2022_age65to100", "WA_Dose3_apr2022_0to17", "WA_Dose3_apr2022_18to64", "WA_Dose3_apr2022_65to100", "WA_Dose1_may2022_age0to17", "WA_Dose1_may2022_age18to64", "WA_Dose1_may2022_age65to100", "WA_Dose3_may2022_0to17", "WA_Dose3_may2022_18to64", "WA_Dose3_may2022_65to100", "WA_Dose1_jun2022_age0to17", "WA_Dose1_jun2022_age18to64", "WA_Dose1_jun2022_age65to100", "WA_Dose3_jun2022_0to17", "WA_Dose3_jun2022_18to64", "WA_Dose3_jun2022_65to100", "WA_Dose1_jul2022_age0to17", "WA_Dose1_jul2022_age18to64", "WA_Dose1_jul2022_age65to100", "WA_Dose3_jul2022_0to17", "WA_Dose3_jul2022_18to64", "WA_Dose3_jul2022_65to100", "WA_Dose1_aug2022_age0to17", "WA_Dose1_aug2022_age18to64", "WA_Dose1_aug2022_age65to100", "WA_Dose3_aug2022_0to17", "WA_Dose3_aug2022_18to64", "WA_Dose3_aug2022_65to100", "WA_Dose1_sep2022_age0to17", "WA_Dose1_sep2022_age18to64", "WA_Dose1_sep2022_age65to100", "WA_Dose3_sep2022_0to17", "WA_Dose3_sep2022_18to64", "WA_Dose3_sep2022_65to100", "WV_Dose1_jan2021_age18to64", "WV_Dose1_jan2021_age65to100", "WV_Dose1_feb2021_age0to17", "WV_Dose1_feb2021_age18to64", "WV_Dose1_feb2021_age65to100", "WV_Dose1_mar2021_age0to17", "WV_Dose1_mar2021_age18to64", "WV_Dose1_mar2021_age65to100", "WV_Dose1_apr2021_age0to17", "WV_Dose1_apr2021_age18to64", "WV_Dose1_apr2021_age65to100", "WV_Dose1_may2021_age0to17", "WV_Dose1_may2021_age18to64", "WV_Dose1_may2021_age65to100", "WV_Dose1_jun2021_age0to17", "WV_Dose1_jun2021_age18to64", "WV_Dose1_jun2021_age65to100", "WV_Dose1_jul2021_age0to17", "WV_Dose1_jul2021_age18to64", "WV_Dose1_jul2021_age65to100", "WV_Dose1_aug2021_age0to17", "WV_Dose1_aug2021_age18to64", "WV_Dose1_aug2021_age65to100", "WV_Dose1_sep2021_age0to17", "WV_Dose1_sep2021_age18to64", "WV_Dose1_sep2021_age65to100", "WV_Dose1_oct2021_age0to17", "WV_Dose1_oct2021_age18to64", "WV_Dose1_oct2021_age65to100", "WV_Dose3_oct2021_0to17", "WV_Dose3_oct2021_18to64", "WV_Dose3_oct2021_65to100", "WV_Dose1_nov2021_age0to17", "WV_Dose1_nov2021_age18to64", "WV_Dose1_nov2021_age65to100", "WV_Dose3_nov2021_0to17", "WV_Dose3_nov2021_18to64", "WV_Dose3_nov2021_65to100", "WV_Dose1_dec2021_age0to17", "WV_Dose1_dec2021_age18to64", "WV_Dose1_dec2021_age65to100", "WV_Dose3_dec2021_0to17", "WV_Dose3_dec2021_18to64", "WV_Dose3_dec2021_65to100", "WV_Dose1_jan2022_age0to17", "WV_Dose1_jan2022_age18to64", "WV_Dose1_jan2022_age65to100", "WV_Dose3_jan2022_0to17", "WV_Dose3_jan2022_18to64", "WV_Dose3_jan2022_65to100", "WV_Dose1_feb2022_age0to17", "WV_Dose1_feb2022_age18to64", "WV_Dose1_feb2022_age65to100", "WV_Dose3_feb2022_0to17", "WV_Dose3_feb2022_18to64", "WV_Dose3_feb2022_65to100", "WV_Dose1_mar2022_age0to17", "WV_Dose1_mar2022_age18to64", "WV_Dose1_mar2022_age65to100", "WV_Dose3_mar2022_0to17", "WV_Dose3_mar2022_18to64", "WV_Dose3_mar2022_65to100", "WV_Dose1_apr2022_age0to17", "WV_Dose1_apr2022_age18to64", "WV_Dose1_apr2022_age65to100", "WV_Dose3_apr2022_0to17", "WV_Dose3_apr2022_18to64", "WV_Dose3_apr2022_65to100", "WV_Dose1_may2022_age0to17", "WV_Dose1_may2022_age18to64", "WV_Dose1_may2022_age65to100", "WV_Dose3_may2022_0to17", "WV_Dose3_may2022_18to64", "WV_Dose3_may2022_65to100", "WV_Dose1_jun2022_age0to17", "WV_Dose1_jun2022_age18to64", "WV_Dose1_jun2022_age65to100", "WV_Dose3_jun2022_0to17", "WV_Dose3_jun2022_18to64", "WV_Dose3_jun2022_65to100", "WV_Dose1_jul2022_age0to17", "WV_Dose1_jul2022_age18to64", "WV_Dose1_jul2022_age65to100", "WV_Dose3_jul2022_0to17", "WV_Dose3_jul2022_18to64", "WV_Dose3_jul2022_65to100", "WV_Dose1_aug2022_age0to17", "WV_Dose1_aug2022_age18to64", "WV_Dose1_aug2022_age65to100", "WV_Dose3_aug2022_0to17", "WV_Dose3_aug2022_18to64", "WV_Dose3_aug2022_65to100", "WV_Dose1_sep2022_age0to17", "WV_Dose1_sep2022_age18to64", "WV_Dose1_sep2022_age65to100", "WV_Dose3_sep2022_0to17", "WV_Dose3_sep2022_18to64", "WV_Dose3_sep2022_65to100", "WI_Dose1_jan2021_age18to64", "WI_Dose1_jan2021_age65to100", "WI_Dose1_feb2021_age0to17", "WI_Dose1_feb2021_age18to64", "WI_Dose1_feb2021_age65to100", "WI_Dose1_mar2021_age0to17", "WI_Dose1_mar2021_age18to64", "WI_Dose1_mar2021_age65to100", "WI_Dose1_apr2021_age0to17", "WI_Dose1_apr2021_age18to64", "WI_Dose1_apr2021_age65to100", "WI_Dose1_may2021_age0to17", "WI_Dose1_may2021_age18to64", "WI_Dose1_may2021_age65to100", "WI_Dose1_jun2021_age0to17", "WI_Dose1_jun2021_age18to64", "WI_Dose1_jun2021_age65to100", "WI_Dose1_jul2021_age0to17", "WI_Dose1_jul2021_age18to64", "WI_Dose1_jul2021_age65to100", "WI_Dose1_aug2021_age0to17", "WI_Dose1_aug2021_age18to64", "WI_Dose1_aug2021_age65to100", "WI_Dose1_sep2021_age0to17", "WI_Dose1_sep2021_age18to64", "WI_Dose1_sep2021_age65to100", "WI_Dose1_oct2021_age0to17", "WI_Dose1_oct2021_age18to64", "WI_Dose1_oct2021_age65to100", "WI_Dose3_oct2021_0to17", "WI_Dose3_oct2021_18to64", "WI_Dose3_oct2021_65to100", "WI_Dose1_nov2021_age0to17", "WI_Dose1_nov2021_age18to64", "WI_Dose1_nov2021_age65to100", "WI_Dose3_nov2021_0to17", "WI_Dose3_nov2021_18to64", "WI_Dose3_nov2021_65to100", "WI_Dose1_dec2021_age0to17", "WI_Dose1_dec2021_age18to64", "WI_Dose1_dec2021_age65to100", "WI_Dose3_dec2021_0to17", "WI_Dose3_dec2021_18to64", "WI_Dose3_dec2021_65to100", "WI_Dose1_jan2022_age0to17", "WI_Dose1_jan2022_age18to64", "WI_Dose1_jan2022_age65to100", "WI_Dose3_jan2022_0to17", "WI_Dose3_jan2022_18to64", "WI_Dose3_jan2022_65to100", "WI_Dose1_feb2022_age0to17", "WI_Dose1_feb2022_age18to64", "WI_Dose1_feb2022_age65to100", "WI_Dose3_feb2022_0to17", "WI_Dose3_feb2022_18to64", "WI_Dose3_feb2022_65to100", "WI_Dose1_mar2022_age0to17", "WI_Dose1_mar2022_age18to64", "WI_Dose1_mar2022_age65to100", "WI_Dose3_mar2022_0to17", "WI_Dose3_mar2022_18to64", "WI_Dose3_mar2022_65to100", "WI_Dose1_apr2022_age0to17", "WI_Dose1_apr2022_age18to64", "WI_Dose1_apr2022_age65to100", "WI_Dose3_apr2022_0to17", "WI_Dose3_apr2022_18to64", "WI_Dose3_apr2022_65to100", "WI_Dose1_may2022_age0to17", "WI_Dose1_may2022_age18to64", "WI_Dose1_may2022_age65to100", "WI_Dose3_may2022_0to17", "WI_Dose3_may2022_18to64", "WI_Dose3_may2022_65to100", "WI_Dose1_jun2022_age0to17", "WI_Dose1_jun2022_age18to64", "WI_Dose1_jun2022_age65to100", "WI_Dose3_jun2022_0to17", "WI_Dose3_jun2022_18to64", "WI_Dose3_jun2022_65to100", "WI_Dose1_jul2022_age0to17", "WI_Dose1_jul2022_age18to64", "WI_Dose1_jul2022_age65to100", "WI_Dose3_jul2022_0to17", "WI_Dose3_jul2022_18to64", "WI_Dose3_jul2022_65to100", "WI_Dose1_aug2022_age0to17", "WI_Dose1_aug2022_age18to64", "WI_Dose1_aug2022_age65to100", "WI_Dose3_aug2022_0to17", "WI_Dose3_aug2022_18to64", "WI_Dose3_aug2022_65to100", "WI_Dose1_sep2022_age0to17", "WI_Dose1_sep2022_age18to64", "WI_Dose1_sep2022_age65to100", "WI_Dose3_sep2022_0to17", "WI_Dose3_sep2022_18to64", "WI_Dose3_sep2022_65to100", "WY_Dose1_jan2021_age18to64", "WY_Dose1_jan2021_age65to100", "WY_Dose1_feb2021_age0to17", "WY_Dose1_feb2021_age18to64", "WY_Dose1_feb2021_age65to100", "WY_Dose1_mar2021_age0to17", "WY_Dose1_mar2021_age18to64", "WY_Dose1_mar2021_age65to100", "WY_Dose1_apr2021_age0to17", "WY_Dose1_apr2021_age18to64", "WY_Dose1_apr2021_age65to100", "WY_Dose1_may2021_age0to17", "WY_Dose1_may2021_age18to64", "WY_Dose1_may2021_age65to100", "WY_Dose1_jun2021_age0to17", "WY_Dose1_jun2021_age18to64", "WY_Dose1_jun2021_age65to100", "WY_Dose1_jul2021_age0to17", "WY_Dose1_jul2021_age18to64", "WY_Dose1_jul2021_age65to100", "WY_Dose1_aug2021_age0to17", "WY_Dose1_aug2021_age18to64", "WY_Dose1_aug2021_age65to100", "WY_Dose1_sep2021_age0to17", "WY_Dose1_sep2021_age18to64", "WY_Dose1_sep2021_age65to100", "WY_Dose1_oct2021_age0to17", "WY_Dose1_oct2021_age18to64", "WY_Dose1_oct2021_age65to100", "WY_Dose3_oct2021_0to17", "WY_Dose3_oct2021_18to64", "WY_Dose3_oct2021_65to100", "WY_Dose1_nov2021_age0to17", "WY_Dose1_nov2021_age18to64", "WY_Dose1_nov2021_age65to100", "WY_Dose3_nov2021_0to17", "WY_Dose3_nov2021_18to64", "WY_Dose3_nov2021_65to100", "WY_Dose1_dec2021_age0to17", "WY_Dose1_dec2021_age18to64", "WY_Dose1_dec2021_age65to100", "WY_Dose3_dec2021_0to17", "WY_Dose3_dec2021_18to64", "WY_Dose3_dec2021_65to100", "WY_Dose1_jan2022_age0to17", "WY_Dose1_jan2022_age18to64", "WY_Dose1_jan2022_age65to100", "WY_Dose3_jan2022_0to17", "WY_Dose3_jan2022_18to64", "WY_Dose3_jan2022_65to100", "WY_Dose1_feb2022_age0to17", "WY_Dose1_feb2022_age18to64", "WY_Dose1_feb2022_age65to100", "WY_Dose3_feb2022_0to17", "WY_Dose3_feb2022_18to64", "WY_Dose3_feb2022_65to100", "WY_Dose1_mar2022_age0to17", "WY_Dose1_mar2022_age18to64", "WY_Dose1_mar2022_age65to100", "WY_Dose3_mar2022_0to17", "WY_Dose3_mar2022_18to64", "WY_Dose3_mar2022_65to100", "WY_Dose1_apr2022_age0to17", "WY_Dose1_apr2022_age18to64", "WY_Dose1_apr2022_age65to100", "WY_Dose3_apr2022_0to17", "WY_Dose3_apr2022_18to64", "WY_Dose3_apr2022_65to100", "WY_Dose1_may2022_age0to17", "WY_Dose1_may2022_age18to64", "WY_Dose1_may2022_age65to100", "WY_Dose3_may2022_0to17", "WY_Dose3_may2022_18to64", "WY_Dose3_may2022_65to100", "WY_Dose1_jun2022_age0to17", "WY_Dose1_jun2022_age18to64", "WY_Dose1_jun2022_age65to100", "WY_Dose3_jun2022_0to17", "WY_Dose3_jun2022_18to64", "WY_Dose3_jun2022_65to100", "WY_Dose1_jul2022_age0to17", "WY_Dose1_jul2022_age18to64", "WY_Dose1_jul2022_age65to100", "WY_Dose3_jul2022_0to17", "WY_Dose3_jul2022_18to64", "WY_Dose3_jul2022_65to100", "WY_Dose1_aug2022_age0to17", "WY_Dose1_aug2022_age18to64", "WY_Dose1_aug2022_age65to100", "WY_Dose3_aug2022_0to17", "WY_Dose3_aug2022_18to64", "WY_Dose3_aug2022_65to100", "WY_Dose1_sep2022_age0to17", "WY_Dose1_sep2022_age18to64", "WY_Dose1_sep2022_age65to100", "WY_Dose3_sep2022_0to17", "WY_Dose3_sep2022_18to64", "WY_Dose3_sep2022_65to100"] inference: - template: Stacked + template: StackedModifier scenarios: ["local_variance", "local_variance_chi3", "NPI", "seasonal", "vaccination"] incidCshift: - template: Stacked + template: StackedModifier scenarios: ["AL_incidCshift1_NEW", "AL_incidCshift2_NEW", "AL_incidCshiftOm_NEW", "AK_incidCshift_NEW", "AK_incidCshiftOm_NEW", "AZ_incidCshift1_NEW", "AZ_incidCshift2_NEW", "AZ_incidCshiftOm_NEW", "AR_incidCshift_NEW", "AR_incidCshiftOm_NEW", "CA_incidCshift1_NEW", "CA_incidCshift2_NEW", "CA_incidCshiftOm_NEW", "CO_incidCshift1_NEW", "CO_incidCshift2_NEW", "CO_incidCshiftOm_NEW", "CT_incidCshift1_NEW", "CT_incidCshift2_NEW", "CT_incidCshiftOm_NEW", "DE_incidCshift1_NEW", "DE_incidCshift2_NEW", "DE_incidCshiftOm_NEW", "DC_incidCshift1_NEW", "DC_incidCshift2_NEW", "DC_incidCshiftOm_NEW", "FL_incidCshift1_NEW", "FL_incidCshift2_NEW", "FL_incidCshiftOm_NEW", "GA_incidCshift1_NEW", "GA_incidCshift2_NEW", "GA_incidCshiftOm_NEW", "HI_incidCshift_NEW", "HI_incidCshiftOm_NEW", "ID_incidCshift_NEW", "ID_incidCshiftOm_NEW", "IL_incidCshift1_NEW", "IL_incidCshift2_NEW", "IL_incidCshiftOm_NEW", "IN_incidCshift1_NEW", "IN_incidCshift2_NEW", "IN_incidCshiftOm_NEW", "IA_incidCshift1_NEW", "IA_incidCshift2_NEW", "IA_incidCshiftOm_NEW", "KS_incidCshift_NEW", "KS_incidCshiftOm_NEW", "KY_incidCshift1_NEW", "KY_incidCshift2_NEW", "KY_incidCshiftOm_NEW", "LA_incidCshift1_NEW", "LA_incidCshift2_NEW", "LA_incidCshiftOm_NEW", "ME_incidCshift1_NEW", "ME_incidCshift2_NEW", "ME_incidCshiftOm_NEW", "MD_incidCshift1_NEW", "MD_incidCshift2_NEW", "MD_incidCshiftOm_NEW", "MA_incidCshift1_NEW", "MA_incidCshift2_NEW", "MA_incidCshiftOm_NEW", "MI_incidCshift1_NEW", "MI_incidCshift2_NEW", "MI_incidCshiftOm_NEW", "MN_incidCshift1_NEW", "MN_incidCshift2_NEW", "MN_incidCshiftOm_NEW", "MS_incidCshift1_NEW", "MS_incidCshift2_NEW", "MS_incidCshiftOm_NEW", "MO_incidCshift1_NEW", "MO_incidCshift2_NEW", "MO_incidCshiftOm_NEW", "MT_incidCshift_NEW", "MT_incidCshiftOm_NEW", "NE_incidCshift1_NEW", "NE_incidCshift2_NEW", "NE_incidCshiftOm_NEW", "NV_incidCshift1_NEW", "NV_incidCshift2_NEW", "NV_incidCshiftOm_NEW", "NH_incidCshift1_NEW", "NH_incidCshift2_NEW", "NH_incidCshiftOm_NEW", "NJ_incidCshift1_NEW", "NJ_incidCshift2_NEW", "NJ_incidCshiftOm_NEW", "NM_incidCshift1_NEW", "NM_incidCshift2_NEW", "NM_incidCshiftOm_NEW", "NY_incidCshift1_NEW", "NY_incidCshift2_NEW", "NY_incidCshiftOm_NEW", "NC_incidCshift1_NEW", "NC_incidCshift2_NEW", "NC_incidCshiftOm_NEW", "ND_incidCshift1_NEW", "ND_incidCshift2_NEW", "ND_incidCshiftOm_NEW", "OH_incidCshift1_NEW", "OH_incidCshift2_NEW", "OH_incidCshiftOm_NEW", "OK_incidCshift1_NEW", "OK_incidCshift2_NEW", "OK_incidCshiftOm_NEW", "OR_incidCshift1_NEW", "OR_incidCshift2_NEW", "OR_incidCshiftOm_NEW", "PA_incidCshift1_NEW", "PA_incidCshift2_NEW", "PA_incidCshiftOm_NEW", "RI_incidCshift1_NEW", "RI_incidCshift2_NEW", "RI_incidCshiftOm_NEW", "SC_incidCshift1_NEW", "SC_incidCshift2_NEW", "SC_incidCshiftOm_NEW", "SD_incidCshift1_NEW", "SD_incidCshift2_NEW", "SD_incidCshiftOm_NEW", "TN_incidCshift_NEW", "TN_incidCshiftOm_NEW", "TX_incidCshift_NEW", "TX_incidCshiftOm_NEW", "UT_incidCshift_NEW", "UT_incidCshiftOm_NEW", "VT_incidCshift_NEW", "VT_incidCshiftOm_NEW", "VA_incidCshift1_NEW", "VA_incidCshift2_NEW", "VA_incidCshiftOm_NEW", "WA_incidCshift1_NEW", "WA_incidCshift2_NEW", "WA_incidCshiftOm_NEW", "WV_incidCshift_NEW", "WV_incidCshiftOm_NEW", "WI_incidCshift1_NEW", "WI_incidCshift2_NEW", "WI_incidCshiftOm_NEW", "WY_incidCshift_NEW", "WY_incidCshiftOm_NEW"] outcome_interventions: - template: Stacked + template: StackedModifier scenarios: ["incidCshift"] AL_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2020-01-01 period_end_date: 2020-05-14 value: @@ -55871,9 +55869,9 @@ interventions: a: -1 b: 1 AL_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2020-05-15 period_end_date: 2021-11-30 value: @@ -55889,9 +55887,9 @@ interventions: a: -1 b: 1 AL_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["01000"] + subpop: ["01000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -55907,9 +55905,9 @@ interventions: a: -1 b: 1 AK_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -55925,9 +55923,9 @@ interventions: a: -1 b: 1 AK_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["02000"] + subpop: ["02000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -55943,9 +55941,9 @@ interventions: a: -1 b: 1 AZ_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -55961,9 +55959,9 @@ interventions: a: -1 b: 1 AZ_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -55979,9 +55977,9 @@ interventions: a: -1 b: 1 AZ_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["04000"] + subpop: ["04000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -55997,9 +55995,9 @@ interventions: a: -1 b: 1 AR_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -56015,9 +56013,9 @@ interventions: a: -1 b: 1 AR_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["05000"] + subpop: ["05000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56033,9 +56031,9 @@ interventions: a: -1 b: 1 CA_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -56051,9 +56049,9 @@ interventions: a: -1 b: 1 CA_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -56069,9 +56067,9 @@ interventions: a: -1 b: 1 CA_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["06000"] + subpop: ["06000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56087,9 +56085,9 @@ interventions: a: -1 b: 1 CO_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -56105,9 +56103,9 @@ interventions: a: -1 b: 1 CO_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -56123,9 +56121,9 @@ interventions: a: -1 b: 1 CO_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["08000"] + subpop: ["08000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56141,9 +56139,9 @@ interventions: a: -1 b: 1 CT_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2020-01-01 period_end_date: 2020-07-14 value: @@ -56159,9 +56157,9 @@ interventions: a: -1 b: 1 CT_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2020-07-15 period_end_date: 2021-11-30 value: @@ -56177,9 +56175,9 @@ interventions: a: -1 b: 1 CT_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["09000"] + subpop: ["09000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56195,9 +56193,9 @@ interventions: a: -1 b: 1 DE_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2020-01-01 period_end_date: 2020-06-14 value: @@ -56213,9 +56211,9 @@ interventions: a: -1 b: 1 DE_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2020-06-15 period_end_date: 2021-11-30 value: @@ -56231,9 +56229,9 @@ interventions: a: -1 b: 1 DE_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["10000"] + subpop: ["10000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56249,9 +56247,9 @@ interventions: a: -1 b: 1 DC_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2020-01-01 period_end_date: 2020-07-14 value: @@ -56267,9 +56265,9 @@ interventions: a: -1 b: 1 DC_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2020-07-15 period_end_date: 2021-11-30 value: @@ -56285,9 +56283,9 @@ interventions: a: -1 b: 1 DC_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["11000"] + subpop: ["11000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56303,9 +56301,9 @@ interventions: a: -1 b: 1 FL_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2020-01-01 period_end_date: 2020-10-10 value: @@ -56321,9 +56319,9 @@ interventions: a: -1 b: 1 FL_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2020-10-11 period_end_date: 2021-11-30 value: @@ -56339,9 +56337,9 @@ interventions: a: -1 b: 1 FL_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["12000"] + subpop: ["12000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56357,9 +56355,9 @@ interventions: a: -1 b: 1 GA_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2020-01-01 period_end_date: 2020-06-14 value: @@ -56375,9 +56373,9 @@ interventions: a: -1 b: 1 GA_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2020-06-15 period_end_date: 2021-11-30 value: @@ -56393,9 +56391,9 @@ interventions: a: -1 b: 1 GA_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["13000"] + subpop: ["13000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56411,9 +56409,9 @@ interventions: a: -1 b: 1 HI_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -56429,9 +56427,9 @@ interventions: a: -1 b: 1 HI_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["15000"] + subpop: ["15000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56447,9 +56445,9 @@ interventions: a: -1 b: 1 ID_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -56465,9 +56463,9 @@ interventions: a: -1 b: 1 ID_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["16000"] + subpop: ["16000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56483,9 +56481,9 @@ interventions: a: -1 b: 1 IL_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2020-01-01 period_end_date: 2020-06-30 value: @@ -56501,9 +56499,9 @@ interventions: a: -1 b: 1 IL_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2020-07-01 period_end_date: 2021-11-30 value: @@ -56519,9 +56517,9 @@ interventions: a: -1 b: 1 IL_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["17000"] + subpop: ["17000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56537,9 +56535,9 @@ interventions: a: -1 b: 1 IN_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2020-01-01 period_end_date: 2020-06-14 value: @@ -56555,9 +56553,9 @@ interventions: a: -1 b: 1 IN_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2020-06-15 period_end_date: 2021-11-30 value: @@ -56573,9 +56571,9 @@ interventions: a: -1 b: 1 IN_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["18000"] + subpop: ["18000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56591,9 +56589,9 @@ interventions: a: -1 b: 1 IA_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -56609,9 +56607,9 @@ interventions: a: -1 b: 1 IA_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -56627,9 +56625,9 @@ interventions: a: -1 b: 1 IA_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["19000"] + subpop: ["19000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56645,9 +56643,9 @@ interventions: a: -1 b: 1 KS_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -56663,9 +56661,9 @@ interventions: a: -1 b: 1 KS_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["20000"] + subpop: ["20000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56681,9 +56679,9 @@ interventions: a: -1 b: 1 KY_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2020-01-01 period_end_date: 2020-06-30 value: @@ -56699,9 +56697,9 @@ interventions: a: -1 b: 1 KY_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2020-07-01 period_end_date: 2021-11-30 value: @@ -56717,9 +56715,9 @@ interventions: a: -1 b: 1 KY_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["21000"] + subpop: ["21000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56735,9 +56733,9 @@ interventions: a: -1 b: 1 LA_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -56753,9 +56751,9 @@ interventions: a: -1 b: 1 LA_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -56771,9 +56769,9 @@ interventions: a: -1 b: 1 LA_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["22000"] + subpop: ["22000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56789,9 +56787,9 @@ interventions: a: -1 b: 1 ME_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -56807,9 +56805,9 @@ interventions: a: -1 b: 1 ME_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -56825,9 +56823,9 @@ interventions: a: -1 b: 1 ME_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["23000"] + subpop: ["23000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56843,9 +56841,9 @@ interventions: a: -1 b: 1 MD_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2020-01-01 period_end_date: 2020-06-30 value: @@ -56861,9 +56859,9 @@ interventions: a: -1 b: 1 MD_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2020-07-01 period_end_date: 2021-11-30 value: @@ -56879,9 +56877,9 @@ interventions: a: -1 b: 1 MD_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["24000"] + subpop: ["24000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56897,9 +56895,9 @@ interventions: a: -1 b: 1 MA_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2020-01-01 period_end_date: 2020-09-14 value: @@ -56915,9 +56913,9 @@ interventions: a: -1 b: 1 MA_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2020-09-15 period_end_date: 2021-11-30 value: @@ -56933,9 +56931,9 @@ interventions: a: -1 b: 1 MA_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["25000"] + subpop: ["25000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -56951,9 +56949,9 @@ interventions: a: -1 b: 1 MI_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2020-01-01 period_end_date: 2020-06-14 value: @@ -56969,9 +56967,9 @@ interventions: a: -1 b: 1 MI_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2020-06-15 period_end_date: 2021-11-30 value: @@ -56987,9 +56985,9 @@ interventions: a: -1 b: 1 MI_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["26000"] + subpop: ["26000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57005,9 +57003,9 @@ interventions: a: -1 b: 1 MN_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2020-01-01 period_end_date: 2020-06-14 value: @@ -57023,9 +57021,9 @@ interventions: a: -1 b: 1 MN_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2020-06-15 period_end_date: 2021-11-30 value: @@ -57041,9 +57039,9 @@ interventions: a: -1 b: 1 MN_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["27000"] + subpop: ["27000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57059,9 +57057,9 @@ interventions: a: -1 b: 1 MS_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2020-01-01 period_end_date: 2020-06-14 value: @@ -57077,9 +57075,9 @@ interventions: a: -1 b: 1 MS_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2020-06-15 period_end_date: 2021-11-30 value: @@ -57095,9 +57093,9 @@ interventions: a: -1 b: 1 MS_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["28000"] + subpop: ["28000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57113,9 +57111,9 @@ interventions: a: -1 b: 1 MO_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2020-01-01 period_end_date: 2020-06-14 value: @@ -57131,9 +57129,9 @@ interventions: a: -1 b: 1 MO_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2020-06-15 period_end_date: 2021-11-30 value: @@ -57149,9 +57147,9 @@ interventions: a: -1 b: 1 MO_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["29000"] + subpop: ["29000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57167,9 +57165,9 @@ interventions: a: -1 b: 1 MT_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -57185,9 +57183,9 @@ interventions: a: -1 b: 1 MT_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["30000"] + subpop: ["30000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57203,9 +57201,9 @@ interventions: a: -1 b: 1 NE_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2020-01-01 period_end_date: 2020-06-14 value: @@ -57221,9 +57219,9 @@ interventions: a: -1 b: 1 NE_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2020-06-15 period_end_date: 2021-11-30 value: @@ -57239,9 +57237,9 @@ interventions: a: -1 b: 1 NE_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["31000"] + subpop: ["31000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57257,9 +57255,9 @@ interventions: a: -1 b: 1 NV_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -57275,9 +57273,9 @@ interventions: a: -1 b: 1 NV_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -57293,9 +57291,9 @@ interventions: a: -1 b: 1 NV_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["32000"] + subpop: ["32000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57311,9 +57309,9 @@ interventions: a: -1 b: 1 NH_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2020-01-01 period_end_date: 2020-07-14 value: @@ -57329,9 +57327,9 @@ interventions: a: -1 b: 1 NH_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2020-07-15 period_end_date: 2021-11-30 value: @@ -57347,9 +57345,9 @@ interventions: a: -1 b: 1 NH_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["33000"] + subpop: ["33000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57365,9 +57363,9 @@ interventions: a: -1 b: 1 NJ_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2020-01-01 period_end_date: 2020-06-30 value: @@ -57383,9 +57381,9 @@ interventions: a: -1 b: 1 NJ_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2020-07-01 period_end_date: 2021-11-30 value: @@ -57401,9 +57399,9 @@ interventions: a: -1 b: 1 NJ_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["34000"] + subpop: ["34000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57419,9 +57417,9 @@ interventions: a: -1 b: 1 NM_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2020-01-01 period_end_date: 2020-06-14 value: @@ -57437,9 +57435,9 @@ interventions: a: -1 b: 1 NM_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2020-06-15 period_end_date: 2021-11-30 value: @@ -57455,9 +57453,9 @@ interventions: a: -1 b: 1 NM_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["35000"] + subpop: ["35000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57473,9 +57471,9 @@ interventions: a: -1 b: 1 NY_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2020-01-01 period_end_date: 2020-06-30 value: @@ -57491,9 +57489,9 @@ interventions: a: -1 b: 1 NY_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2020-07-01 period_end_date: 2021-11-30 value: @@ -57509,9 +57507,9 @@ interventions: a: -1 b: 1 NY_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["36000"] + subpop: ["36000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57527,9 +57525,9 @@ interventions: a: -1 b: 1 NC_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2020-01-01 period_end_date: 2020-05-14 value: @@ -57545,9 +57543,9 @@ interventions: a: -1 b: 1 NC_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2020-05-15 period_end_date: 2021-11-30 value: @@ -57563,9 +57561,9 @@ interventions: a: -1 b: 1 NC_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["37000"] + subpop: ["37000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57581,9 +57579,9 @@ interventions: a: -1 b: 1 ND_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -57599,9 +57597,9 @@ interventions: a: -1 b: 1 ND_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -57617,9 +57615,9 @@ interventions: a: -1 b: 1 ND_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["38000"] + subpop: ["38000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57635,9 +57633,9 @@ interventions: a: -1 b: 1 OH_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -57653,9 +57651,9 @@ interventions: a: -1 b: 1 OH_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -57671,9 +57669,9 @@ interventions: a: -1 b: 1 OH_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["39000"] + subpop: ["39000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57689,9 +57687,9 @@ interventions: a: -1 b: 1 OK_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -57707,9 +57705,9 @@ interventions: a: -1 b: 1 OK_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -57725,9 +57723,9 @@ interventions: a: -1 b: 1 OK_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["40000"] + subpop: ["40000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57743,9 +57741,9 @@ interventions: a: -1 b: 1 OR_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -57761,9 +57759,9 @@ interventions: a: -1 b: 1 OR_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -57779,9 +57777,9 @@ interventions: a: -1 b: 1 OR_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["41000"] + subpop: ["41000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57797,9 +57795,9 @@ interventions: a: -1 b: 1 PA_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2020-01-01 period_end_date: 2020-06-14 value: @@ -57815,9 +57813,9 @@ interventions: a: -1 b: 1 PA_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2020-06-15 period_end_date: 2021-11-30 value: @@ -57833,9 +57831,9 @@ interventions: a: -1 b: 1 PA_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["42000"] + subpop: ["42000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57851,9 +57849,9 @@ interventions: a: -1 b: 1 RI_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2020-01-01 period_end_date: 2020-06-14 value: @@ -57869,9 +57867,9 @@ interventions: a: -1 b: 1 RI_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2020-06-15 period_end_date: 2021-11-30 value: @@ -57887,9 +57885,9 @@ interventions: a: -1 b: 1 RI_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["44000"] + subpop: ["44000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57905,9 +57903,9 @@ interventions: a: -1 b: 1 SC_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -57923,9 +57921,9 @@ interventions: a: -1 b: 1 SC_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -57941,9 +57939,9 @@ interventions: a: -1 b: 1 SC_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["45000"] + subpop: ["45000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -57959,9 +57957,9 @@ interventions: a: -1 b: 1 SD_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2020-01-01 period_end_date: 2020-07-31 value: @@ -57977,9 +57975,9 @@ interventions: a: -1 b: 1 SD_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2020-08-01 period_end_date: 2021-11-30 value: @@ -57995,9 +57993,9 @@ interventions: a: -1 b: 1 SD_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["46000"] + subpop: ["46000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -58013,9 +58011,9 @@ interventions: a: -1 b: 1 TN_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -58031,9 +58029,9 @@ interventions: a: -1 b: 1 TN_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["47000"] + subpop: ["47000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -58049,9 +58047,9 @@ interventions: a: -1 b: 1 TX_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -58067,9 +58065,9 @@ interventions: a: -1 b: 1 TX_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["48000"] + subpop: ["48000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -58085,9 +58083,9 @@ interventions: a: -1 b: 1 UT_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -58103,9 +58101,9 @@ interventions: a: -1 b: 1 UT_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["49000"] + subpop: ["49000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -58121,9 +58119,9 @@ interventions: a: -1 b: 1 VT_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -58139,9 +58137,9 @@ interventions: a: -1 b: 1 VT_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["50000"] + subpop: ["50000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -58157,9 +58155,9 @@ interventions: a: -1 b: 1 VA_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -58175,9 +58173,9 @@ interventions: a: -1 b: 1 VA_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -58193,9 +58191,9 @@ interventions: a: -1 b: 1 VA_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["51000"] + subpop: ["51000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -58211,9 +58209,9 @@ interventions: a: -1 b: 1 WA_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -58229,9 +58227,9 @@ interventions: a: -1 b: 1 WA_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -58247,9 +58245,9 @@ interventions: a: -1 b: 1 WA_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["53000"] + subpop: ["53000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -58265,9 +58263,9 @@ interventions: a: -1 b: 1 WV_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -58283,9 +58281,9 @@ interventions: a: -1 b: 1 WV_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["54000"] + subpop: ["54000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -58301,9 +58299,9 @@ interventions: a: -1 b: 1 WI_incidCshift1_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2020-01-01 period_end_date: 2020-05-31 value: @@ -58319,9 +58317,9 @@ interventions: a: -1 b: 1 WI_incidCshift2_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2020-06-01 period_end_date: 2021-11-30 value: @@ -58337,9 +58335,9 @@ interventions: a: -1 b: 1 WI_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["55000"] + subpop: ["55000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -58355,9 +58353,9 @@ interventions: a: -1 b: 1 WY_incidCshift_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2020-01-01 period_end_date: 2021-11-30 value: @@ -58373,9 +58371,9 @@ interventions: a: -1 b: 1 WY_incidCshiftOm_NEW: - template: Reduce + template: SinglePeriodModifier parameter: incidItoC_all - affected_geoids: ["56000"] + subpop: ["56000"] period_start_date: 2021-12-01 period_end_date: 2022-09-03 value: @@ -58394,7 +58392,7 @@ interventions: outcomes: method: delayframe param_from_file: FALSE - param_place_file: "usa-geoid-params-output_statelevel_agestrat_R12.parquet" + param_subpop_file: "usa-subpop-params-output_statelevel_agestrat_R12.parquet" scenarios: - med settings: @@ -61458,7 +61456,7 @@ outcomes: interventions: settings: med: - template: Stacked + template: StackedModifier scenarios: ["outcome_interventions"] inference: diff --git a/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml b/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml index 1af1d9754..afb989120 100644 --- a/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml +++ b/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml @@ -16,8 +16,6 @@ compartments: spatial_setup: geodata: geodata_2019_statelevel.csv mobility: mobility_2011-2015_statelevel.csv - popnodes: pop2019est - nodenames: geoid include_in_report: include_in_report state_level: TRUE @@ -54,9 +52,9 @@ interventions: - inference settings: all_independent: - template: Reduce + template: SinglePeriodModifier parameter: r1 - affected_geoids: "all" + subpop: "all" period_start_date: 2020-01-01 period_end_date: 2022-09-03 value: @@ -66,9 +64,9 @@ interventions: a: -1 b: 1 all_together: - template: Reduce + template: SinglePeriodModifier parameter: r2 - affected_geoids: "all" + subpop: "all" spatial_groups: "all" period_start_date: 2020-01-01 period_end_date: 2022-09-03 @@ -79,9 +77,9 @@ interventions: a: -1 b: 1 two_groups: - template: Reduce + template: SinglePeriodModifier parameter: r3 - affected_geoids: "all" + subpop: "all" spatial_groups: - ["01000", "02000"] - ["04000", "06000"] @@ -100,9 +98,9 @@ interventions: a: -1 b: 1 one_group: - template: Reduce + template: SinglePeriodModifier parameter: r4 - affected_geoids: ["01000", "02000", "04000", "06000"] + subpop: ["01000", "02000", "04000", "06000"] spatial_groups: - ["01000", "02000"] period_start_date: 2020-04-04 @@ -115,17 +113,17 @@ interventions: b: 0.9 mt_reduce: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r5 groups: - - affected_geoids: ["09000", "10000"] + - subpop: ["09000", "10000"] spatial_groups: ["09000", "10000"] periods: - start_date: 2020-12-01 end_date: 2020-12-31 - start_date: 2021-12-01 end_date: 2021-12-31 - - affected_geoids: ["01000", "02000", "04000", "06000"] + - subpop: ["01000", "02000", "04000", "06000"] spatial_groups: ["01000","04000"] periods: - start_date: 2020-10-01 @@ -140,17 +138,17 @@ interventions: b: 1 scn_error: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r1 groups: - - affected_geoids: ["09000", "10000"] + - subpop: ["09000", "10000"] spatial_groups: ["09000", "10000"] periods: - start_date: 2020-12-01 end_date: 2020-12-31 - start_date: 2021-12-01 end_date: 2021-12-31 - - affected_geoids: ["01000", "02000", "04000", "06000"] + - subpop: ["01000", "02000", "04000", "06000"] spatial_groups: ["10000"] periods: - start_date: 2021-08-16 @@ -165,8 +163,8 @@ interventions: b: 1 inference: - template: Stacked + template: StackedModifier scenarios: ["all_independent", "all_together", "two_groups", "one_group", "mt_reduce"] error: - template: Stacked + template: StackedModifier scenarios: ["scn_error"] diff --git a/flepimop/gempyor_pkg/tests/npi/data/geodata.csv b/flepimop/gempyor_pkg/tests/npi/data/geodata.csv index f4fa78f6a..2fc052a06 100644 --- a/flepimop/gempyor_pkg/tests/npi/data/geodata.csv +++ b/flepimop/gempyor_pkg/tests/npi/data/geodata.csv @@ -1,4 +1,4 @@ -"geoid","USPS","population" +"subpop","USPS","population" "15005","HI",75 "15007","HI",71377 "15009","HI",165281 diff --git a/flepimop/gempyor_pkg/tests/npi/data/geodata_2019_statelevel.csv b/flepimop/gempyor_pkg/tests/npi/data/geodata_2019_statelevel.csv index f0bbbd8f7..7d053e317 100644 --- a/flepimop/gempyor_pkg/tests/npi/data/geodata_2019_statelevel.csv +++ b/flepimop/gempyor_pkg/tests/npi/data/geodata_2019_statelevel.csv @@ -1,4 +1,4 @@ -USPS,geoid,pop2019est +USPS,subpop,population WY,56000,581024 VT,50000,624313 DC,11000,692683 diff --git a/flepimop/gempyor_pkg/tests/npi/test_npis.py b/flepimop/gempyor_pkg/tests/npi/test_npis.py index 8c8398427..2cdb165e1 100644 --- a/flepimop/gempyor_pkg/tests/npi/test_npis.py +++ b/flepimop/gempyor_pkg/tests/npi/test_npis.py @@ -26,7 +26,7 @@ def test_full_npis_read_write(): os.chdir(os.path.dirname(__file__)) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_npi.yml", run_id=105, prefix="", @@ -57,7 +57,7 @@ def test_full_npis_read_write(): random.seed(10) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_npi.yml", run_id=105, prefix="", @@ -81,7 +81,7 @@ def test_full_npis_read_write(): assert (hnpi_read == hnpi_wrote).all().all() # runs with the new, random NPI - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_npi.yml", run_id=106, prefix="", @@ -105,7 +105,7 @@ def test_full_npis_read_write(): def test_spatial_groups(): - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_test_spatial_group_npi.yml", run_id=105, prefix="", @@ -155,38 +155,38 @@ def test_spatial_groups(): # all independent: r1 df = npi_df[npi_df["npi_name"] == "all_independent"] assert len(df) == inference_simulator.s.nnodes - for g in df["geoid"]: + for g in df["subpop"]: assert "," not in g # all the same: r2 df = npi_df[npi_df["npi_name"] == "all_together"] assert len(df) == 1 - assert set(df["geoid"].iloc[0].split(",")) == set(inference_simulator.s.spatset.nodenames) - assert len(df["geoid"].iloc[0].split(",")) == inference_simulator.s.nnodes + assert set(df["subpop"].iloc[0].split(",")) == set(inference_simulator.s.subpop_struct.subpop_names) + assert len(df["subpop"].iloc[0].split(",")) == inference_simulator.s.nnodes # two groups: r3 df = npi_df[npi_df["npi_name"] == "two_groups"] assert len(df) == inference_simulator.s.nnodes - 2 for g in ["01000", "02000", "04000", "06000"]: - assert g not in df["geoid"] - assert len(df[df["geoid"] == "01000,02000"]) == 1 - assert len(df[df["geoid"] == "04000,06000"]) == 1 + assert g not in df["subpop"] + assert len(df[df["subpop"] == "01000,02000"]) == 1 + assert len(df[df["subpop"] == "04000,06000"]) == 1 # mtr group: r5 df = npi_df[npi_df["npi_name"] == "mt_reduce"] assert len(df) == 4 - assert df.geoid.to_list() == ["09000,10000", "02000", "06000", "01000,04000"] - assert df[df["geoid"] == "09000,10000"]["start_date"].iloc[0] == "2020-12-01,2021-12-01" + assert df.subpop.to_list() == ["09000,10000", "02000", "06000", "01000,04000"] + assert df[df["subpop"] == "09000,10000"]["start_date"].iloc[0] == "2020-12-01,2021-12-01" assert ( - df[df["geoid"] == "01000,04000"]["start_date"].iloc[0] - == df[df["geoid"] == "06000"]["start_date"].iloc[0] + df[df["subpop"] == "01000,04000"]["start_date"].iloc[0] + == df[df["subpop"] == "06000"]["start_date"].iloc[0] == "2020-10-01,2021-10-01" ) def test_spatial_groups(): - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_test_spatial_group_npi.yml", run_id=105, prefix="", @@ -208,7 +208,7 @@ def test_spatial_groups(): out_snpi = pa.Table.from_pandas(snpi_read, preserve_index=False) pa.parquet.write_table(out_snpi, file_paths.create_file_name(106, "", 1, "snpi", "parquet")) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_test_spatial_group_npi.yml", run_id=106, prefix="", @@ -225,9 +225,9 @@ def test_spatial_groups(): snpi_read = pq.read_table(f"{config_path_prefix}model_output/snpi/000000001.106.snpi.parquet").to_pandas() snpi_wrote = pq.read_table(f"{config_path_prefix}model_output/snpi/000000001.107.snpi.parquet").to_pandas() - # now the order can change, so we need to sort by geoid and start_date - snpi_wrote = snpi_wrote.sort_values(by=["geoid", "start_date"]).reset_index(drop=True) - snpi_read = snpi_read.sort_values(by=["geoid", "start_date"]).reset_index(drop=True) + # now the order can change, so we need to sort by subpop and start_date + snpi_wrote = snpi_wrote.sort_values(by=["subpop", "start_date"]).reset_index(drop=True) + snpi_read = snpi_read.sort_values(by=["subpop", "start_date"]).reset_index(drop=True) assert (snpi_read == snpi_wrote).all().all() npi_read = seir.build_npi_SEIR( diff --git a/flepimop/gempyor_pkg/tests/outcomes/config.yml b/flepimop/gempyor_pkg/tests/outcomes/config.yml index 89a277b26..72cf3b3a9 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config.yml @@ -8,8 +8,6 @@ nslots: 1 spatial_setup: geodata: geodata.csv mobility: mobility.csv - popnodes: population - nodenames: geoid census_year: 2018 modeled_states: - HI diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_load.yml b/flepimop/gempyor_pkg/tests/outcomes/config_load.yml index ece1eb9e8..cdd0d15fb 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_load.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_load.yml @@ -8,8 +8,6 @@ nslots: 1 spatial_setup: geodata: geodata.csv mobility: mobility.csv - popnodes: population - nodenames: geoid census_year: 2018 modeled_states: - HI @@ -18,7 +16,7 @@ spatial_setup: outcomes: method: delayframe param_from_file: True - param_place_file: test_rel.parquet + param_subpop_file: test_rel.parquet scenarios: - high_death_rate settings: diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml b/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml index f9a42b040..5b72523e0 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml @@ -8,8 +8,6 @@ nslots: 1 spatial_setup: geodata: geodata.csv mobility: mobility.csv - popnodes: population - nodenames: geoid census_year: 2018 modeled_states: - HI @@ -18,7 +16,7 @@ spatial_setup: outcomes: method: delayframe param_from_file: True - param_place_file: test_rel_subclasses.parquet + param_subpop_file: test_rel_subclasses.parquet subclasses: ['_A', '_B'] scenarios: - high_death_rate diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml b/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml index eee7713b3..5a8d5b949 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml @@ -8,8 +8,6 @@ nslots: 1 spatial_setup: geodata: geodata.csv mobility: mobility.csv - popnodes: population - nodenames: geoid census_year: 2018 modeled_states: - HI @@ -19,69 +17,69 @@ interventions: - None settings: None: - template: ReduceR0 + template: SinglePeriodModifierR0 value: distribution: fixed value: 0 Hduration: - template: Reduce + template: SinglePeriodModifier parameter: "incidH_duration" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 Hdelay: - template: Reduce + template: SinglePeriodModifier parameter: "incidH_delay" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 Hprobability: - template: Reduce + template: SinglePeriodModifier parameter: "incidH_probability" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: 0.5 Ddelay: - template: Reduce + template: SinglePeriodModifier parameter: "incidD_delay" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 Dprobability: - template: Reduce + template: SinglePeriodModifier parameter: "incidD_probability" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 ICUprobability: - template: Reduce + template: SinglePeriodModifier parameter: "incidICU_probability" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 times2D: - template: Stacked + template: StackedModifier scenarios: ["Ddelay", "Dprobability"] times2H: - template: Stacked + template: StackedModifier scenarios: ["Hdelay", "Hprobability", "Hduration"] outcomes: @@ -268,7 +266,7 @@ outcomes: interventions: settings: high_death_rate: - template: Stacked + template: StackedModifier scenarios: ["times2H", "ICUprobability", "times2D"] diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml b/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml index 6f0d5649e..c6bfcc830 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml @@ -8,8 +8,6 @@ nslots: 1 spatial_setup: geodata: geodata.csv mobility: mobility.csv - popnodes: population - nodenames: geoid census_year: 2018 modeled_states: - HI @@ -19,69 +17,69 @@ interventions: - None settings: None: - template: ReduceR0 + template: SinglePeriodModifierR0 value: distribution: fixed value: 0 Hduration: - template: Reduce + template: SinglePeriodModifier parameter: "incidH::duration" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 Hdelay: - template: Reduce + template: SinglePeriodModifier parameter: "incidH::delay" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 Hprobability: - template: Reduce + template: SinglePeriodModifier parameter: "incidH::probability" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: 0.5 Ddelay: - template: Reduce + template: SinglePeriodModifier parameter: "incidD::delay" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 Dprobability: - template: Reduce + template: SinglePeriodModifier parameter: "incidD::probability" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 ICUprobability: - template: Reduce + template: SinglePeriodModifier parameter: "incidICU::probability" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 times2D: - template: Stacked + template: StackedModifier scenarios: ["Ddelay", "Dprobability"] times2H: - template: Stacked + template: StackedModifier scenarios: ["Hdelay", "Hprobability", "Hduration"] @@ -131,5 +129,5 @@ outcomes: interventions: settings: high_death_rate: - template: Stacked + template: StackedModifier scenarios: ["times2H", "ICUprobability", "times2D"] diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml b/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml index 83e1910df..ffbae9ca3 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml @@ -8,8 +8,6 @@ nslots: 1 spatial_setup: geodata: geodata.csv mobility: mobility.csv - popnodes: population - nodenames: geoid census_year: 2018 modeled_states: - HI @@ -19,69 +17,69 @@ interventions: - None settings: None: - template: ReduceR0 + template: SinglePeriodModifierR0 value: distribution: fixed value: 0 Hduration: - template: Reduce + template: SinglePeriodModifier parameter: "hosp_paraM_duRr" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 Hdelay: - template: Reduce + template: SinglePeriodModifier parameter: "hosp_paraM_deLay" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 Hprobability: - template: Reduce + template: SinglePeriodModifier parameter: "hosp_paraM_PROB" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: 0.5 Ddelay: - template: Reduce + template: SinglePeriodModifier parameter: "death_param_DELAY" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 Dprobability: - template: Reduce + template: SinglePeriodModifier parameter: "death_param_prob" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 ICUprobability: - template: Reduce + template: SinglePeriodModifier parameter: "icu_param_PROB" - affected_geoids: "all" + subpop: "all" period_start_date: 2020-04-01 period_end_date: 2020-05-15 value: distribution: fixed value: .5 times2D: - template: Stacked + template: StackedModifier scenarios: ["Ddelay", "Dprobability"] times2H: - template: Stacked + template: StackedModifier scenarios: ["Hdelay", "Hprobability", "Hduration"] @@ -137,5 +135,5 @@ outcomes: interventions: settings: high_death_rate: - template: Stacked + template: StackedModifier scenarios: ["times2H", "ICUprobability", "times2D"] diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml b/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml index 0eccee718..81abe0ba0 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml @@ -8,8 +8,6 @@ nslots: 1 spatial_setup: geodata: geodata.csv mobility: mobility.csv - popnodes: population - nodenames: geoid census_year: 2018 modeled_states: - HI diff --git a/flepimop/gempyor_pkg/tests/outcomes/data/geodata.csv b/flepimop/gempyor_pkg/tests/outcomes/data/geodata.csv index f4fa78f6a..2fc052a06 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/data/geodata.csv +++ b/flepimop/gempyor_pkg/tests/outcomes/data/geodata.csv @@ -1,4 +1,4 @@ -"geoid","USPS","population" +"subpop","USPS","population" "15005","HI",75 "15007","HI",71377 "15009","HI",165281 diff --git a/flepimop/gempyor_pkg/tests/outcomes/data/usa-geoid-params-output.parquet b/flepimop/gempyor_pkg/tests/outcomes/data/usa-geoid-params-output.parquet index ccadc92e18e4ccb80e6931ab74dac039ac8814d0..d08a4fc58f7c327f2d2e9a89d9e52a250bea8d2a 100644 GIT binary patch delta 1204 zcmb_cPfXKL7=JBLU|Dnr-m;RaXta?CW1GVaPktS25C^P{bD5F|bjyUmItSz5g^Mx9 zn>1z~G|>wuE{YMa7(t0w4jwsplamKShzGw{${?Y7@}+(6d#~U3`~ALOo36jEd-<%c zDG6GT*3#{b8oIZ}CFr{lC2yKe`#I0*MgNcUZVwSepDC^7q!S2&M3pA6sK|Hw%4T0 zTy7XE=wsQwXv!y8Q7>_1DNrpzUC6SRj37%;jWjN8+AI}!oD+p78@&ADllfHjIqMA2 zaDwuP@Lp|2gdH{3xUyvpWP@c0WafB0+;YQYPR5_Xmay1OhS=x&yh)g^6fi}S1v zN!-2G#$OeeL|xc$Wd)_wKmXYvKl;j7h9$18!wE~A=p^W@em^%kLS2NqFY$*)SNt(< 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z%o$MC5N$bw2G0r@JP3``2&;4k4=GsCG;2G+$PNHrAUcNicmko^q$lte6}Jg6wo@2L z@JtAtwDmbGw1llrFE7*{4!DrVdSDKK<9iLfz$;{;fhQ+8w1LVKmKi3CQS7w12*9`m z5hhf1vXgbD6F%!aCr%JSnCRWJrfeJ$JDs`lSf8V!$tZPXrG%DOa!YT9-kv20_lNi_ JKoIy}=)aWSOWFVc diff --git a/flepimop/gempyor_pkg/tests/outcomes/make_seir_test_file.py b/flepimop/gempyor_pkg/tests/outcomes/make_seir_test_file.py index 8551774d2..56df652cf 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/make_seir_test_file.py +++ b/flepimop/gempyor_pkg/tests/outcomes/make_seir_test_file.py @@ -50,11 +50,11 @@ b = b[(b["date"] >= "2020-04-01") & (b["date"] <= "2020-05-15")] -geoid = ["15005", "15007", "15009", "15001", "15003"] +subpop = ["15005", "15007", "15009", "15001", "15003"] diffI = np.arange(5) * 2 date_data = datetime.date(2020, 4, 15) for i in range(5): - b.loc[(b["mc_value_type"] == "incidence") & (b["date"] == str(date_data)), geoid[i]] = diffI[i] + b.loc[(b["mc_value_type"] == "incidence") & (b["date"] == str(date_data)), subpop[i]] = diffI[i] pa_df = pa.Table.from_pandas(b, preserve_index=False) pa.parquet.write_table(pa_df, "new_test_no_vacc.parquet") @@ -75,7 +75,7 @@ (b["mc_value_type"] == "incidence") & (b["date"] == str(date_data)) & (b["mc_vaccination_stage"] == "first_dose"), - geoid[i], + subpop[i], ] = ( diffI[i] * 3 ) diff --git a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py index 9cc7cc090..543c32e6e 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py +++ b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py @@ -22,7 +22,7 @@ ### To generate files for this test, see notebook Test Outcomes playbook.ipynb in COVID19_Maryland -geoid = ["15005", "15007", "15009", "15001", "15003"] +subpop = ["15005", "15007", "15009", "15001", "15003"] diffI = np.arange(5) * 2 date_data = datetime.date(2020, 4, 15) subclasses = ["_A", "_B"] @@ -32,7 +32,7 @@ def test_outcome_scenario(): os.chdir(os.path.dirname(__file__)) ## this is redundant but necessary. Why ? - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config.yml", run_id=1, prefix="", @@ -45,33 +45,33 @@ def test_outcome_scenario(): hosp = pq.read_table(f"{config_path_prefix}model_output/hosp/000000001.1.hosp.parquet").to_pandas() hosp.set_index("time", drop=True, inplace=True) - for i, place in enumerate(geoid): + for i, place in enumerate(subpop): for dt in hosp.index: if dt.date() == date_data: - assert hosp[hosp["geoid"] == place]["incidI"][dt] == diffI[i] - assert hosp[hosp["geoid"] == place]["incidH"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 - assert hosp[hosp["geoid"] == place]["incidD"][dt + datetime.timedelta(2)] == diffI[i] * 0.01 - assert hosp[hosp["geoid"] == place]["incidICU"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 * 0.4 + assert hosp[hosp["subpop"] == place]["incidI"][dt] == diffI[i] + assert hosp[hosp["subpop"] == place]["incidH"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 + assert hosp[hosp["subpop"] == place]["incidD"][dt + datetime.timedelta(2)] == diffI[i] * 0.01 + assert hosp[hosp["subpop"] == place]["incidICU"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 * 0.4 for j in range(7): - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7 + j)] == diffI[i] * 0.1 - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7 + 8)] == 0 + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7 + j)] == diffI[i] * 0.1 + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7 + 8)] == 0 elif dt.date() < date_data: - assert hosp[hosp["geoid"] == place]["incidH"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["incidI"][dt] == 0 - assert hosp[hosp["geoid"] == place]["incidD"][dt + datetime.timedelta(2)] == 0 - assert hosp[hosp["geoid"] == place]["incidICU"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidH"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidI"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidD"][dt + datetime.timedelta(2)] == 0 + assert hosp[hosp["subpop"] == place]["incidICU"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7)] == 0 elif dt.date() > (date_data + datetime.timedelta(7)): - assert hosp[hosp["geoid"] == place]["incidH"][dt] == 0 - assert hosp[hosp["geoid"] == place]["incidI"][dt - datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["incidD"][dt - datetime.timedelta(4)] == 0 - assert hosp[hosp["geoid"] == place]["incidICU"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidH"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidI"][dt - datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidD"][dt - datetime.timedelta(4)] == 0 + assert hosp[hosp["subpop"] == place]["incidICU"][dt] == 0 hpar = pq.read_table(f"{config_path_prefix}model_output/hpar/000000001.1.hpar.parquet").to_pandas() - for i, place in enumerate(geoid): + for i, place in enumerate(subpop): assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "probability")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "probability")][ "value" ] ) @@ -79,13 +79,13 @@ def test_outcome_scenario(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "delay")]["value"] + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "delay")]["value"] ) == 7 ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "duration")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "duration")][ "value" ] ) @@ -93,7 +93,7 @@ def test_outcome_scenario(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "probability")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "probability")][ "value" ] ) @@ -101,13 +101,13 @@ def test_outcome_scenario(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "delay")]["value"] + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "delay")]["value"] ) == 2 ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "probability")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "probability")][ "value" ] ) @@ -115,7 +115,7 @@ def test_outcome_scenario(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "delay")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "delay")][ "value" ] ) @@ -125,7 +125,7 @@ def test_outcome_scenario(): def test_outcome_scenario_with_load(): os.chdir(os.path.dirname(__file__)) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_load.yml", run_id=2, prefix="", @@ -140,9 +140,9 @@ def test_outcome_scenario_with_load(): hpar_rel = pq.read_table(f"{config_path_prefix}model_output/hpar/000000001.2.hpar.parquet").to_pandas() for out in ["incidH", "incidD", "incidICU"]: - for i, place in enumerate(geoid): - a = hpar_rel[(hpar_rel["outcome"] == out) & (hpar_rel["geoid"] == place)] - b = hpar_config[(hpar_rel["outcome"] == out) & (hpar_config["geoid"] == place)] + for i, place in enumerate(subpop): + a = hpar_rel[(hpar_rel["outcome"] == out) & (hpar_rel["subpop"] == place)] + b = hpar_config[(hpar_rel["outcome"] == out) & (hpar_config["subpop"] == place)] assert len(a) == len(b) for j in range(len(a)): if b.iloc[j]["quantity"] in ["delay", "duration"]: @@ -161,7 +161,7 @@ def test_outcomes_read_write_hpar(): config.clear() config.read(user=False) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_load.yml", run_id=2, prefix="", @@ -186,7 +186,7 @@ def test_outcomes_read_write_hpar(): def test_outcome_scenario_subclasses(): os.chdir(os.path.dirname(__file__)) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_subclasses.yml", run_id=1, prefix="", @@ -201,71 +201,71 @@ def test_outcome_scenario_subclasses(): hosp = pq.read_table(f"{config_path_prefix}model_output/hosp/000000001.10.hosp.parquet").to_pandas() hosp.set_index("time", drop=True, inplace=True) - for i, place in enumerate(geoid): + for i, place in enumerate(subpop): for dt in hosp.index: if dt.date() == date_data: - assert hosp[hosp["geoid"] == place]["incidI"][dt] == diffI[i] - assert hosp[hosp["geoid"] == place]["incidH"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 * len( + assert hosp[hosp["subpop"] == place]["incidI"][dt] == diffI[i] + assert hosp[hosp["subpop"] == place]["incidH"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 * len( subclasses ) - assert hosp[hosp["geoid"] == place]["incidD"][dt + datetime.timedelta(2)] == diffI[i] * 0.01 * len( + assert hosp[hosp["subpop"] == place]["incidD"][dt + datetime.timedelta(2)] == diffI[i] * 0.01 * len( subclasses ) - assert hosp[hosp["geoid"] == place]["incidICU"][dt + datetime.timedelta(7)] == diffI[ + assert hosp[hosp["subpop"] == place]["incidICU"][dt + datetime.timedelta(7)] == diffI[ i ] * 0.1 * 0.4 * len(subclasses) for j in range(7): - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7 + j)] == diffI[ + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7 + j)] == diffI[ i ] * 0.1 * len(subclasses) - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7 + 8)] == 0 + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7 + 8)] == 0 elif dt.date() < date_data: - assert hosp[hosp["geoid"] == place]["incidH"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["incidI"][dt] == 0 - assert hosp[hosp["geoid"] == place]["incidD"][dt + datetime.timedelta(2)] == 0 - assert hosp[hosp["geoid"] == place]["incidICU"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidH"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidI"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidD"][dt + datetime.timedelta(2)] == 0 + assert hosp[hosp["subpop"] == place]["incidICU"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7)] == 0 elif dt.date() > (date_data + datetime.timedelta(7)): - assert hosp[hosp["geoid"] == place]["incidH"][dt] == 0 - assert hosp[hosp["geoid"] == place]["incidI"][dt - datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["incidD"][dt - datetime.timedelta(4)] == 0 - assert hosp[hosp["geoid"] == place]["incidICU"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidH"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidI"][dt - datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidD"][dt - datetime.timedelta(4)] == 0 + assert hosp[hosp["subpop"] == place]["incidICU"][dt] == 0 for cl in subclasses: - for i, place in enumerate(geoid): + for i, place in enumerate(subpop): for dt in hosp.index: if dt.date() == date_data: - assert hosp[hosp["geoid"] == place][f"incidH{cl}"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 - assert hosp[hosp["geoid"] == place][f"incidD{cl}"][dt + datetime.timedelta(2)] == diffI[i] * 0.01 + assert hosp[hosp["subpop"] == place][f"incidH{cl}"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 + assert hosp[hosp["subpop"] == place][f"incidD{cl}"][dt + datetime.timedelta(2)] == diffI[i] * 0.01 assert ( - hosp[hosp["geoid"] == place][f"incidICU{cl}"][dt + datetime.timedelta(7)] + hosp[hosp["subpop"] == place][f"incidICU{cl}"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 * 0.4 ) for j in range(7): assert ( - hosp[hosp["geoid"] == place][f"hosp_curr{cl}"][dt + datetime.timedelta(7 + j)] + hosp[hosp["subpop"] == place][f"hosp_curr{cl}"][dt + datetime.timedelta(7 + j)] == diffI[i] * 0.1 ) - assert hosp[hosp["geoid"] == place][f"hosp_curr{cl}"][dt + datetime.timedelta(7 + 8)] == 0 + assert hosp[hosp["subpop"] == place][f"hosp_curr{cl}"][dt + datetime.timedelta(7 + 8)] == 0 elif dt.date() < date_data: - assert hosp[hosp["geoid"] == place][f"incidH{cl}"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place][f"incidD{cl}"][dt + datetime.timedelta(2)] == 0 - assert hosp[hosp["geoid"] == place][f"incidICU{cl}"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place][f"hosp_curr{cl}"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place][f"incidH{cl}"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place][f"incidD{cl}"][dt + datetime.timedelta(2)] == 0 + assert hosp[hosp["subpop"] == place][f"incidICU{cl}"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place][f"hosp_curr{cl}"][dt + datetime.timedelta(7)] == 0 elif dt.date() > (date_data + datetime.timedelta(7)): - assert hosp[hosp["geoid"] == place][f"incidH{cl}"][dt] == 0 - assert hosp[hosp["geoid"] == place][f"incidD{cl}"][dt - datetime.timedelta(4)] == 0 - assert hosp[hosp["geoid"] == place][f"incidICU{cl}"][dt] == 0 + assert hosp[hosp["subpop"] == place][f"incidH{cl}"][dt] == 0 + assert hosp[hosp["subpop"] == place][f"incidD{cl}"][dt - datetime.timedelta(4)] == 0 + assert hosp[hosp["subpop"] == place][f"incidICU{cl}"][dt] == 0 hpar = pq.read_table(f"{config_path_prefix}model_output/hpar/000000001.10.hpar.parquet").to_pandas() for cl in subclasses: - for i, place in enumerate(geoid): + for i, place in enumerate(subpop): assert ( float( hpar[ - (hpar["geoid"] == place) + (hpar["subpop"] == place) & (hpar["outcome"] == f"incidH{cl}") & (hpar["quantity"] == "probability") ]["value"] @@ -274,16 +274,18 @@ def test_outcome_scenario_subclasses(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == f"incidH{cl}") & (hpar["quantity"] == "delay")][ - "value" - ] + hpar[ + (hpar["subpop"] == place) & (hpar["outcome"] == f"incidH{cl}") & (hpar["quantity"] == "delay") + ]["value"] ) == 7 ) assert ( float( hpar[ - (hpar["geoid"] == place) & (hpar["outcome"] == f"incidH{cl}") & (hpar["quantity"] == "duration") + (hpar["subpop"] == place) + & (hpar["outcome"] == f"incidH{cl}") + & (hpar["quantity"] == "duration") ]["value"] ) == 7 @@ -291,7 +293,7 @@ def test_outcome_scenario_subclasses(): assert ( float( hpar[ - (hpar["geoid"] == place) + (hpar["subpop"] == place) & (hpar["outcome"] == f"incidD{cl}") & (hpar["quantity"] == "probability") ]["value"] @@ -300,16 +302,16 @@ def test_outcome_scenario_subclasses(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == f"incidD{cl}") & (hpar["quantity"] == "delay")][ - "value" - ] + hpar[ + (hpar["subpop"] == place) & (hpar["outcome"] == f"incidD{cl}") & (hpar["quantity"] == "delay") + ]["value"] ) == 2 ) assert ( float( hpar[ - (hpar["geoid"] == place) + (hpar["subpop"] == place) & (hpar["outcome"] == f"incidICU{cl}") & (hpar["quantity"] == "probability") ]["value"] @@ -319,19 +321,19 @@ def test_outcome_scenario_subclasses(): assert ( float( hpar[ - (hpar["geoid"] == place) & (hpar["outcome"] == f"incidICU{cl}") & (hpar["quantity"] == "delay") + (hpar["subpop"] == place) & (hpar["outcome"] == f"incidICU{cl}") & (hpar["quantity"] == "delay") ]["value"] ) == 0 ) - # assert((hpar[(hpar['geoid']== place) & (hpar['outcome']== f'incidICU{cl}')]['source'] == f'incidH{cl}').all()) - # assert((hpar[(hpar['geoid']== place) & (hpar['outcome']== f'incidH{cl}')]['source'] == f'incidI').all()) + # assert((hpar[(hpar['subpop']== place) & (hpar['outcome']== f'incidICU{cl}')]['source'] == f'incidH{cl}').all()) + # assert((hpar[(hpar['subpop']== place) & (hpar['outcome']== f'incidH{cl}')]['source'] == f'incidI').all()) def test_outcome_scenario_with_load_subclasses(): os.chdir(os.path.dirname(__file__)) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_load_subclasses.yml", run_id=1, prefix="", @@ -347,9 +349,9 @@ def test_outcome_scenario_with_load_subclasses(): hpar_rel = pq.read_table(f"{config_path_prefix}model_output/hpar/000000001.11.hpar.parquet").to_pandas() for cl in subclasses: for out in [f"incidH{cl}", f"incidD{cl}", f"incidICU{cl}"]: - for i, place in enumerate(geoid): - a = hpar_rel[(hpar_rel["outcome"] == out) & (hpar_rel["geoid"] == place)] - b = hpar_config[(hpar_rel["outcome"] == out) & (hpar_config["geoid"] == place)] + for i, place in enumerate(subpop): + a = hpar_rel[(hpar_rel["outcome"] == out) & (hpar_rel["subpop"] == place)] + b = hpar_config[(hpar_rel["outcome"] == out) & (hpar_config["subpop"] == place)] assert len(a) == len(b) for j in range(len(a)): if b.iloc[j]["quantity"] in ["delay", "duration"]: @@ -374,7 +376,7 @@ def test_outcome_scenario_with_load_subclasses(): def test_outcomes_read_write_hpar_subclasses(): os.chdir(os.path.dirname(__file__)) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_load.yml", run_id=1, prefix="", @@ -386,7 +388,7 @@ def test_outcomes_read_write_hpar_subclasses(): outcomes.onerun_delayframe_outcomes(sim_id2write=1, s=inference_simulator.s) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_load.yml", run_id=12, prefix="", @@ -445,7 +447,7 @@ def test_multishift_notstochdelays(): def test_outcomes_npi(): os.chdir(os.path.dirname(__file__)) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_npi.yml", run_id=1, prefix="", @@ -459,34 +461,34 @@ def test_outcomes_npi(): hosp = pq.read_table(f"{config_path_prefix}model_output/hosp/000000001.105.hosp.parquet").to_pandas() hosp.set_index("time", drop=True, inplace=True) # same as config.yaml (doubled, then NPI halve it) - for i, place in enumerate(geoid): + for i, place in enumerate(subpop): for dt in hosp.index: if dt.date() == date_data: - assert hosp[hosp["geoid"] == place]["incidI"][dt] == diffI[i] - assert hosp[hosp["geoid"] == place]["incidH"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 - assert hosp[hosp["geoid"] == place]["incidD"][dt + datetime.timedelta(2)] == diffI[i] * 0.01 - assert hosp[hosp["geoid"] == place]["incidICU"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 * 0.4 + assert hosp[hosp["subpop"] == place]["incidI"][dt] == diffI[i] + assert hosp[hosp["subpop"] == place]["incidH"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 + assert hosp[hosp["subpop"] == place]["incidD"][dt + datetime.timedelta(2)] == diffI[i] * 0.01 + assert hosp[hosp["subpop"] == place]["incidICU"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 * 0.4 for j in range(7): - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7 + j)] == diffI[i] * 0.1 - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7 + 8)] == 0 + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7 + j)] == diffI[i] * 0.1 + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7 + 8)] == 0 elif dt.date() < date_data: - assert hosp[hosp["geoid"] == place]["incidH"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["incidI"][dt] == 0 - assert hosp[hosp["geoid"] == place]["incidD"][dt + datetime.timedelta(2)] == 0 - assert hosp[hosp["geoid"] == place]["incidICU"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidH"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidI"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidD"][dt + datetime.timedelta(2)] == 0 + assert hosp[hosp["subpop"] == place]["incidICU"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7)] == 0 elif dt.date() > (date_data + datetime.timedelta(7)): - assert hosp[hosp["geoid"] == place]["incidH"][dt] == 0 - assert hosp[hosp["geoid"] == place]["incidI"][dt - datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["incidD"][dt - datetime.timedelta(4)] == 0 - assert hosp[hosp["geoid"] == place]["incidICU"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidH"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidI"][dt - datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidD"][dt - datetime.timedelta(4)] == 0 + assert hosp[hosp["subpop"] == place]["incidICU"][dt] == 0 hpar = pq.read_table(f"{config_path_prefix}model_output/hpar/000000001.105.hpar.parquet").to_pandas() # Doubled everything from previous config.yaml - for i, place in enumerate(geoid): + for i, place in enumerate(subpop): assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "probability")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "probability")][ "value" ] ) @@ -494,13 +496,13 @@ def test_outcomes_npi(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "delay")]["value"] + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "delay")]["value"] ) == 7 * 2 ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "duration")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "duration")][ "value" ] ) @@ -508,7 +510,7 @@ def test_outcomes_npi(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "probability")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "probability")][ "value" ] ) @@ -516,13 +518,13 @@ def test_outcomes_npi(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "delay")]["value"] + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "delay")]["value"] ) == 2 * 2 ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "probability")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "probability")][ "value" ] ) @@ -530,7 +532,7 @@ def test_outcomes_npi(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "delay")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "delay")][ "value" ] ) @@ -541,7 +543,7 @@ def test_outcomes_npi(): def test_outcomes_read_write_hnpi(): os.chdir(os.path.dirname(__file__)) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_npi.yml", run_id=105, prefix="", @@ -568,7 +570,7 @@ def test_outcomes_read_write_hnpi(): def test_outcomes_read_write_hnpi2(): os.chdir(os.path.dirname(__file__)) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_npi.yml", run_id=105, prefix="", @@ -592,7 +594,7 @@ def test_outcomes_read_write_hnpi2(): assert (hnpi_read == hnpi_wrote).all().all() # runs with the new, random NPI - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_npi.yml", run_id=106, prefix="", @@ -617,7 +619,7 @@ def test_outcomes_read_write_hnpi2(): def test_outcomes_npi_custom_pname(): os.chdir(os.path.dirname(__file__)) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_npi_custom_pnames.yml", run_id=1, prefix="", @@ -631,34 +633,34 @@ def test_outcomes_npi_custom_pname(): hosp = pq.read_table(f"{config_path_prefix}model_output/hosp/000000001.105.hosp.parquet").to_pandas() hosp.set_index("time", drop=True, inplace=True) # same as config.yaml (doubled, then NPI halve it) - for i, place in enumerate(geoid): + for i, place in enumerate(subpop): for dt in hosp.index: if dt.date() == date_data: - assert hosp[hosp["geoid"] == place]["incidI"][dt] == diffI[i] - assert hosp[hosp["geoid"] == place]["incidH"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 - assert hosp[hosp["geoid"] == place]["incidD"][dt + datetime.timedelta(2)] == diffI[i] * 0.01 - assert hosp[hosp["geoid"] == place]["incidICU"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 * 0.4 + assert hosp[hosp["subpop"] == place]["incidI"][dt] == diffI[i] + assert hosp[hosp["subpop"] == place]["incidH"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 + assert hosp[hosp["subpop"] == place]["incidD"][dt + datetime.timedelta(2)] == diffI[i] * 0.01 + assert hosp[hosp["subpop"] == place]["incidICU"][dt + datetime.timedelta(7)] == diffI[i] * 0.1 * 0.4 for j in range(7): - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7 + j)] == diffI[i] * 0.1 - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7 + 8)] == 0 + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7 + j)] == diffI[i] * 0.1 + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7 + 8)] == 0 elif dt.date() < date_data: - assert hosp[hosp["geoid"] == place]["incidH"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["incidI"][dt] == 0 - assert hosp[hosp["geoid"] == place]["incidD"][dt + datetime.timedelta(2)] == 0 - assert hosp[hosp["geoid"] == place]["incidICU"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["hosp_curr"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidH"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidI"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidD"][dt + datetime.timedelta(2)] == 0 + assert hosp[hosp["subpop"] == place]["incidICU"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["hosp_curr"][dt + datetime.timedelta(7)] == 0 elif dt.date() > (date_data + datetime.timedelta(7)): - assert hosp[hosp["geoid"] == place]["incidH"][dt] == 0 - assert hosp[hosp["geoid"] == place]["incidI"][dt - datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place]["incidD"][dt - datetime.timedelta(4)] == 0 - assert hosp[hosp["geoid"] == place]["incidICU"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidH"][dt] == 0 + assert hosp[hosp["subpop"] == place]["incidI"][dt - datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place]["incidD"][dt - datetime.timedelta(4)] == 0 + assert hosp[hosp["subpop"] == place]["incidICU"][dt] == 0 hpar = pq.read_table(f"{config_path_prefix}model_output/hpar/000000001.105.hpar.parquet").to_pandas() # Doubled everything from previous config.yaml - for i, place in enumerate(geoid): + for i, place in enumerate(subpop): assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "probability")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "probability")][ "value" ] ) @@ -666,13 +668,13 @@ def test_outcomes_npi_custom_pname(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "delay")]["value"] + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "delay")]["value"] ) == 7 * 2 ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "duration")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidH") & (hpar["quantity"] == "duration")][ "value" ] ) @@ -680,7 +682,7 @@ def test_outcomes_npi_custom_pname(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "probability")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "probability")][ "value" ] ) @@ -688,13 +690,13 @@ def test_outcomes_npi_custom_pname(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "delay")]["value"] + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidD") & (hpar["quantity"] == "delay")]["value"] ) == 2 * 2 ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "probability")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "probability")][ "value" ] ) @@ -702,7 +704,7 @@ def test_outcomes_npi_custom_pname(): ) assert ( float( - hpar[(hpar["geoid"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "delay")][ + hpar[(hpar["subpop"] == place) & (hpar["outcome"] == "incidICU") & (hpar["quantity"] == "delay")][ "value" ] ) @@ -713,7 +715,7 @@ def test_outcomes_npi_custom_pname(): def test_outcomes_read_write_hnpi_custom_pname(): os.chdir(os.path.dirname(__file__)) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_npi_custom_pnames.yml", run_id=105, prefix="", @@ -749,7 +751,7 @@ def test_outcomes_read_write_hnpi2_custom_pname(): random.seed(10) - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_npi_custom_pnames.yml", run_id=105, prefix="", @@ -766,7 +768,7 @@ def test_outcomes_read_write_hnpi2_custom_pname(): assert (hnpi_read == hnpi_wrote).all().all() # runs with the new, random NPI - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_npi_custom_pnames.yml", run_id=106, prefix="", @@ -793,7 +795,7 @@ def test_outcomes_pcomp(): os.chdir(os.path.dirname(__file__)) prefix = "" - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config_mc_selection.yml", run_id=110, prefix="", @@ -807,7 +809,7 @@ def test_outcomes_pcomp(): seir = pq.read_table(f"{config_path_prefix}model_output/seir/000000001.105.seir.parquet").to_pandas() seir2 = seir.copy() seir2["mc_vaccination_stage"] = "first_dose" - for pl in geoid: + for pl in subpop: seir2[pl] = seir2[pl] * p_compmult[1] new_seir = pd.concat([seir, seir2]) out_df = pa.Table.from_pandas(new_seir, preserve_index=False) @@ -819,54 +821,54 @@ def test_outcomes_pcomp(): # same as config.yaml (doubled, then NPI halve it) for k, p_comp in enumerate(["0dose", "1dose"]): hosp = hosp_f - for i, place in enumerate(geoid): + for i, place in enumerate(subpop): for dt in hosp.index: if dt.date() == date_data: - assert hosp[hosp["geoid"] == place][f"incidI_{p_comp}"][dt] == diffI[i] * p_compmult[k] + assert hosp[hosp["subpop"] == place][f"incidI_{p_comp}"][dt] == diffI[i] * p_compmult[k] assert ( - hosp[hosp["geoid"] == place][f"incidH_{p_comp}"][dt + datetime.timedelta(7)] + hosp[hosp["subpop"] == place][f"incidH_{p_comp}"][dt + datetime.timedelta(7)] - diffI[i] * 0.1 * p_compmult[k] < 1e-8 ) assert ( - hosp[hosp["geoid"] == place][f"incidD_{p_comp}"][dt + datetime.timedelta(2)] + hosp[hosp["subpop"] == place][f"incidD_{p_comp}"][dt + datetime.timedelta(2)] - diffI[i] * 0.01 * p_compmult[k] < 1e-8 ) assert ( - hosp[hosp["geoid"] == place][f"incidICU_{p_comp}"][dt + datetime.timedelta(7)] + hosp[hosp["subpop"] == place][f"incidICU_{p_comp}"][dt + datetime.timedelta(7)] - diffI[i] * 0.1 * 0.4 * p_compmult[k] < 1e-8 ) for j in range(7): assert ( - hosp[hosp["geoid"] == place][f"incidH_{p_comp}_curr"][dt + datetime.timedelta(7 + j)] + hosp[hosp["subpop"] == place][f"incidH_{p_comp}_curr"][dt + datetime.timedelta(7 + j)] - diffI[i] * 0.1 * p_compmult[k] < 1e-8 ) - assert hosp[hosp["geoid"] == place][f"incidH_{p_comp}_curr"][dt + datetime.timedelta(7 + 8)] == 0 + assert hosp[hosp["subpop"] == place][f"incidH_{p_comp}_curr"][dt + datetime.timedelta(7 + 8)] == 0 elif dt.date() < date_data: - assert hosp[hosp["geoid"] == place][f"incidH_{p_comp}"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place][f"incidI_{p_comp}"][dt] == 0 - assert hosp[hosp["geoid"] == place][f"incidD_{p_comp}"][dt + datetime.timedelta(2)] == 0 - assert hosp[hosp["geoid"] == place][f"incidICU_{p_comp}"][dt + datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place][f"incidH_{p_comp}_curr"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place][f"incidH_{p_comp}"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place][f"incidI_{p_comp}"][dt] == 0 + assert hosp[hosp["subpop"] == place][f"incidD_{p_comp}"][dt + datetime.timedelta(2)] == 0 + assert hosp[hosp["subpop"] == place][f"incidICU_{p_comp}"][dt + datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place][f"incidH_{p_comp}_curr"][dt + datetime.timedelta(7)] == 0 elif dt.date() > (date_data + datetime.timedelta(7)): - assert hosp[hosp["geoid"] == place][f"incidH_{p_comp}"][dt] == 0 - assert hosp[hosp["geoid"] == place][f"incidI_{p_comp}"][dt - datetime.timedelta(7)] == 0 - assert hosp[hosp["geoid"] == place][f"incidD_{p_comp}"][dt - datetime.timedelta(4)] == 0 - assert hosp[hosp["geoid"] == place][f"incidICU_{p_comp}"][dt] == 0 + assert hosp[hosp["subpop"] == place][f"incidH_{p_comp}"][dt] == 0 + assert hosp[hosp["subpop"] == place][f"incidI_{p_comp}"][dt - datetime.timedelta(7)] == 0 + assert hosp[hosp["subpop"] == place][f"incidD_{p_comp}"][dt - datetime.timedelta(4)] == 0 + assert hosp[hosp["subpop"] == place][f"incidICU_{p_comp}"][dt] == 0 hpar_f = pq.read_table(f"{config_path_prefix}model_output/hpar/000000001.111.hpar.parquet").to_pandas() # Doubled everything from previous config.yaml # for k, p_comp in enumerate(["unvaccinated", "first_dose"]): for k, p_comp in enumerate(["0dose", "1dose"]): hpar = hpar_f - for i, place in enumerate(geoid): + for i, place in enumerate(subpop): assert ( float( hpar[ - (hpar["geoid"] == place) + (hpar["subpop"] == place) & (hpar["outcome"] == f"incidH_{p_comp}") & (hpar["quantity"] == "probability") ]["value"] @@ -876,7 +878,7 @@ def test_outcomes_pcomp(): assert ( float( hpar[ - (hpar["geoid"] == place) + (hpar["subpop"] == place) & (hpar["outcome"] == f"incidH_{p_comp}") & (hpar["quantity"] == "delay") ]["value"] @@ -886,7 +888,7 @@ def test_outcomes_pcomp(): assert ( float( hpar[ - (hpar["geoid"] == place) + (hpar["subpop"] == place) & (hpar["outcome"] == f"incidH_{p_comp}") & (hpar["quantity"] == "duration") ]["value"] @@ -896,7 +898,7 @@ def test_outcomes_pcomp(): assert ( float( hpar[ - (hpar["geoid"] == place) + (hpar["subpop"] == place) & (hpar["outcome"] == f"incidD_{p_comp}") & (hpar["quantity"] == "probability") ]["value"] @@ -906,7 +908,7 @@ def test_outcomes_pcomp(): assert ( float( hpar[ - (hpar["geoid"] == place) + (hpar["subpop"] == place) & (hpar["outcome"] == f"incidD_{p_comp}") & (hpar["quantity"] == "delay") ]["value"] @@ -916,7 +918,7 @@ def test_outcomes_pcomp(): assert ( float( hpar[ - (hpar["geoid"] == place) + (hpar["subpop"] == place) & 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--- a/flepimop/gempyor_pkg/tests/seir/data/config.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config.yml @@ -9,8 +9,6 @@ nslots: 15 spatial_setup: geodata: geodata.csv mobility: mobility.txt - popnodes: population - nodenames: geoid seeding: method: FolderDraw @@ -83,7 +81,7 @@ interventions: - Scenario2 settings: None: - template: Reduce + template: SinglePeriodModifier parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-15 @@ -91,7 +89,7 @@ interventions: distribution: fixed value: 0 Wuhan: - template: Reduce + template: SinglePeriodModifier parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-15 @@ -100,24 +98,24 @@ interventions: low: .14 high: .33 KansasCity: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - periods: - start_date: 2020-04-01 end_date: 2020-05-15 - affected_geoids: "all" + subpop: "all" value: distribution: uniform low: .04 high: .23 Scenario1: - template: Stacked + template: StackedModifier scenarios: - KansasCity - Wuhan - None Scenario2: - template: Stacked + template: StackedModifier scenarios: - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml index b1188208d..932fa382a 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml @@ -8,8 +8,6 @@ nslots: 15 spatial_setup: geodata: geodata.csv mobility: mobility.txt - popnodes: population - nodenames: geoid compartments: infection_stage: ["S", "E", "I1", "I2", "I3", "R"] diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format_with_covariates.yml b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format_with_covariates.yml index ca413a484..9afd97a54 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format_with_covariates.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format_with_covariates.yml @@ -8,8 +8,6 @@ nslots: 15 spatial_setup: geodata: geodata.csv mobility: mobility.txt - popnodes: population - nodenames: geoid compartments: infection_stage: ["S", "E", "I1", "I2", "I3", "R"] diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml index 065fee13d..b884d8a54 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml @@ -8,8 +8,6 @@ nslots: 15 spatial_setup: geodata: geodata.csv mobility: mobility.txt - popnodes: population - nodenames: geoid seeding: method: FolderDraw @@ -116,7 +114,7 @@ interventions: - Scenario2 settings: None: - template: Reduce + template: SinglePeriodModifier parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-15 @@ -124,7 +122,7 @@ interventions: distribution: fixed value: 0 Wuhan: - template: Reduce + template: SinglePeriodModifier parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-15 @@ -133,24 +131,24 @@ interventions: low: .14 high: .33 KansasCity: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - periods: - start_date: 2020-04-01 end_date: 2020-05-15 - affected_geoids: "all" + subpop: "all" value: distribution: uniform low: .04 high: .23 Scenario1: - template: Stacked + template: StackedModifier scenarios: - KansasCity - Wuhan - None Scenario2: - template: Stacked + template: StackedModifier scenarios: - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml b/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml index 16922d6d9..3dea2721c 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml @@ -9,8 +9,6 @@ spatial_setup: base_path: data geodata: geodata.csv mobility: mobility.txt - popnodes: population - nodenames: geoid initial_conditions: method: InitialConditionsFolderDraw @@ -82,7 +80,7 @@ interventions: - Scenario2 settings: None: - template: Reduce + template: SinglePeriodModifier parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-16 @@ -90,7 +88,7 @@ interventions: distribution: fixed value: 0 Wuhan: - template: Reduce + template: SinglePeriodModifier parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-16 @@ -99,7 +97,7 @@ interventions: low: .14 high: .33 KansasCity: - template: ReduceR0 + template: SinglePeriodModifierR0 parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-16 @@ -108,11 +106,11 @@ interventions: low: .04 high: .23 Scenario1: - template: Stacked + template: StackedModifier scenarios: - Wuhan - None Scenario2: - template: Stacked + template: StackedModifier scenarios: - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml b/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml index a9a5de805..e11cdf53e 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml @@ -9,8 +9,6 @@ spatial_setup: base_path: data geodata: geodata.csv mobility: mobility.txt - popnodes: population - nodenames: geoid initial_conditions: method: InitialConditionsFolderDraw @@ -82,7 +80,7 @@ interventions: - Scenario2 settings: None: - template: Reduce + template: SinglePeriodModifier parameter: r0 period_start_date: 2020-04-02 period_end_date: 2020-05-16 @@ -90,49 +88,49 @@ interventions: distribution: fixed value: 0 Wuhan: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - periods: - start_date: 2020-04-02 end_date: 2020-05-16 - affected_geoids: "all" + subpop: "all" value: distribution: uniform low: .14 high: .33 KansasCity: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - periods: - start_date: 2020-04-02 end_date: 2020-05-16 - affected_geoids: "all" + subpop: "all" value: distribution: uniform low: .04 high: .23 BrandNew: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - periods: - start_date: 2020-04-02 end_date: 2020-05-16 - affected_geoids: "all" + subpop: "all" value: distribution: uniform low: .2 high: .25 Scenario1: - template: Stacked + template: StackedModifier scenarios: - BrandNew - KansasCity - Wuhan - None Scenario2: - template: Stacked + template: StackedModifier scenarios: - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml b/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml index 2fde529a4..c496f2cba 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml @@ -9,8 +9,6 @@ spatial_setup: base_path: data geodata: geodata.csv mobility: mobility.csv - popnodes: population - nodenames: geoid census_year: 2018 modeled_states: - HI @@ -101,22 +99,22 @@ interventions: - Scenario2 settings: None: - template: ReduceR0 + template: SinglePeriodModifierR0 value: distribution: fixed value: 0 Place1: - template: Reduce + template: SinglePeriodModifier parameter: r0 value: distribution: uniform low: .14 high: .33 Place2: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - - affected_geoids: "all" + - subpop: "all" periods: - start_date: "2020-04-01" end_date: "2020-04-15" @@ -127,7 +125,7 @@ interventions: low: .14 high: .33 Dose1: - template: Reduce + template: SinglePeriodModifier parameter: "transition_rate0" period_start_date: 2020-04-10 period_end_date: 2020-04-10 @@ -135,7 +133,7 @@ interventions: distribution: fixed value: 0.9 Dose2: - template: Reduce + template: SinglePeriodModifier parameter: "transition_rate1" period_start_date: 2020-04-11 period_end_date: 2020-04-11 @@ -143,18 +141,18 @@ interventions: distribution: fixed value: 0.9 vaccination: - template: Stacked + template: StackedModifier scenarios: - Dose1 - Dose2 Scenario_vacc: - template: Stacked + template: StackedModifier scenarios: - Place1 - Place2 - vaccination Scenario_novacc: - template: Stacked + template: StackedModifier scenarios: - Place1 - Place2 diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_resume.yml b/flepimop/gempyor_pkg/tests/seir/data/config_resume.yml index 81f748153..ed111ed0e 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_resume.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_resume.yml @@ -10,8 +10,6 @@ spatial_setup: base_path: data geodata: geodata.csv mobility: mobility.txt - popnodes: population - nodenames: geoid seeding: method: InitialConditionsFolderDraw @@ -82,7 +80,7 @@ interventions: - Scenario2 settings: None: - template: Reduce + template: SinglePeriodModifier parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-16 @@ -90,7 +88,7 @@ interventions: distribution: fixed value: 0 Wuhan: - template: Reduce + template: SinglePeriodModifier parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-16 @@ -99,7 +97,7 @@ interventions: low: .14 high: .33 KansasCity: - template: ReduceR0 + template: SinglePeriodModifierR0 parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-16 @@ -108,11 +106,11 @@ interventions: low: .04 high: .23 Scenario1: - template: Stacked + template: StackedModifier scenarios: - Wuhan - None Scenario2: - template: Stacked + template: StackedModifier scenarios: - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/geodata.csv b/flepimop/gempyor_pkg/tests/seir/data/geodata.csv index 3021e87ac..9566ab0a3 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/geodata.csv +++ b/flepimop/gempyor_pkg/tests/seir/data/geodata.csv @@ -1,3 +1,3 @@ -geoid,population,include_in_report +subpop,population,include_in_report 10001,1000,TRUE 20002,2000,FALSE diff --git a/flepimop/gempyor_pkg/tests/seir/dev_new_test.py b/flepimop/gempyor_pkg/tests/seir/dev_new_test.py index 4b56fed95..fc724d598 100644 --- a/flepimop/gempyor_pkg/tests/seir/dev_new_test.py +++ b/flepimop/gempyor_pkg/tests/seir/dev_new_test.py @@ -24,7 +24,7 @@ config.clear() config.read(user=False) config.set_file(f"{DATA_DIR}/config_compartmental_model_format_with_covariates.yml") - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{DATA_DIR}/config_compartmental_model_format_with_covariates.yml", run_id=1, prefix="", @@ -34,7 +34,7 @@ ) # p = parameters.Parameters( - # parameter_config=config["seir"]["parameters"], config_version="v2") + # parameter_config=config["seir"]["parameters"]) p = inference_simulator.s.parameters p_draw = p.parameters_quick_draw(n_days=inference_simulator.s.n_days, nnodes=inference_simulator.s.nnodes) @@ -51,7 +51,7 @@ assert (p_draw[p.pnames2pindex["R0s"]] == initial_df.values).all() - ### test what happen when the order of geoids is not respected (expected: reput them in order) + ### test what happen when the order of subpops is not respected (expected: reput them in order) ### test what happens with incomplete data (expected: fail) diff --git a/flepimop/gempyor_pkg/tests/seir/interface.ipynb b/flepimop/gempyor_pkg/tests/seir/interface.ipynb index 738f1d50d..1ecaf0a17 100644 --- a/flepimop/gempyor_pkg/tests/seir/interface.ipynb +++ b/flepimop/gempyor_pkg/tests/seir/interface.ipynb @@ -46,7 +46,7 @@ ], "source": [ "config_filepath = \"../tests/npi/config_npi.yml\"\n", - "gempyor_simulator = gempyor.InferenceSimulator(\n", + "gempyor_simulator = gempyor.GempyorSimulator(\n", " config_path=config_filepath,\n", " run_id=\"test_run_id\",\n", " prefix=\"test_prefix/\",\n", diff --git a/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test.seed.csv b/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test.seed.csv index ca8adac5d..f5c136e11 100644 --- a/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test.seed.csv +++ b/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test.seed.csv @@ -1,3 +1,3 @@ -date,place,amount,source_infection_stage,source_vaccination_stage,source_variant_type,destination_infection_stage,destination_vaccination_stage, destination_variant_type +date,subpop,amount,source_infection_stage,source_vaccination_stage,source_variant_type,destination_infection_stage,destination_vaccination_stage, destination_variant_type 2020-01-31,10001,40,S,unvaccinated,var0,E,unvaccinated,var0 2020-01-31,20002,10,S,unvaccinated,var0,E,unvaccinated,var0 diff --git a/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test_SeedOneNode.seed.csv b/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test_SeedOneNode.seed.csv index 0a39d5981..a17e58c5e 100644 --- a/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test_SeedOneNode.seed.csv +++ b/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test_SeedOneNode.seed.csv @@ -1,3 +1,3 @@ -date,place,amount,source_infection_stage,source_vaccination_stage,source_variant_type,destination_infection_stage,destination_vaccination_stage, destination_variant_type +date,subpop,amount,source_infection_stage,source_vaccination_stage,source_variant_type,destination_infection_stage,destination_vaccination_stage, destination_variant_type 2020-01-31,10001,10,S,unvaccinated,var0,E,unvaccinated,var0 2020-02-01,10001,50,S,unvaccinated,var0,E,unvaccinated,var0 diff --git a/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test_parallel.seed.csv b/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test_parallel.seed.csv index 58c31a7ab..48399be91 100644 --- a/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test_parallel.seed.csv +++ b/flepimop/gempyor_pkg/tests/seir/model_output/seed/000000100.test_parallel.seed.csv @@ -1,3 +1,3 @@ -date,place,amount,source_infection_stage,source_vaccination_stage,destination_infection_stage,destination_vaccination_stage +date,subpop,amount,source_infection_stage,source_vaccination_stage,destination_infection_stage,destination_vaccination_stage 2020-04-01,10001,10,S,unvaccinated,E,unvaccinated 2020-04-02,10001,50,S,unvaccinated,E,unvaccinated diff --git a/flepimop/gempyor_pkg/tests/seir/test_compartments.py b/flepimop/gempyor_pkg/tests/seir/test_compartments.py index c5034a00f..4a2f86d61 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_compartments.py +++ b/flepimop/gempyor_pkg/tests/seir/test_compartments.py @@ -10,7 +10,7 @@ import pyarrow.parquet as pq import filecmp -from gempyor import compartments, seir, NPI, file_paths, setup +from gempyor import compartments, seir, NPI, file_paths, setup, subpopulation_structure from gempyor.utils import config @@ -65,12 +65,12 @@ def test_Setup_has_compartments_component(): config.read(user=False) config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_values", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( @@ -78,7 +78,6 @@ def test_Setup_has_compartments_component(): spatial_setup=ss, nslots=1, npi_scenario="None", - config_version="v3", npi_config_seir=config["interventions"]["settings"]["None"], parameters_config=config["seir"]["parameters"], seir_config=config["seir"], @@ -100,7 +99,6 @@ def test_Setup_has_compartments_component(): spatial_setup=ss, nslots=1, npi_scenario="None", - config_version="v3", npi_config_seir=config["interventions"]["settings"]["None"], parameters_config=config["seir"]["parameters"], seir_config=config["seir"], diff --git a/flepimop/gempyor_pkg/tests/seir/test_new_seir.py b/flepimop/gempyor_pkg/tests/seir/test_new_seir.py index b1755b211..f6880b71a 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_new_seir.py +++ b/flepimop/gempyor_pkg/tests/seir/test_new_seir.py @@ -8,7 +8,7 @@ import pyarrow.parquet as pq from functools import reduce -from gempyor import setup, seir, NPI, file_paths, compartments +from gempyor import setup, seir, NPI, file_paths, compartments, subpopulation_structure from gempyor.utils import config @@ -19,12 +19,12 @@ def test_constant_population(): config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( @@ -47,7 +47,7 @@ def test_constant_population(): initial_conditions = s.seedingAndIC.draw_ic(sim_id=0, setup=s) seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) - npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) + npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) parameters = s.parameters.parameters_quick_draw(n_days=s.n_days, nnodes=s.nnodes) parameter_names = [x for x in s.parameters.pnames] diff --git a/flepimop/gempyor_pkg/tests/seir/test_parameters.py b/flepimop/gempyor_pkg/tests/seir/test_parameters.py index 1310aef64..c10ce34bd 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_parameters.py +++ b/flepimop/gempyor_pkg/tests/seir/test_parameters.py @@ -10,7 +10,7 @@ import pyarrow.parquet as pq import filecmp -from gempyor import setup, seir, NPI, file_paths, parameters +from gempyor import setup, seir, NPI, file_paths, parameters, subpopulation_structure from gempyor.utils import config, write_df, read_df @@ -23,12 +23,12 @@ def test_parameters_from_config_plus_read_write(): config.read(user=False) config.set_file(f"{DATA_DIR}/config_compartmental_model_format.yml") # Would be better to build a setup - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) index = 1 @@ -39,7 +39,6 @@ def test_parameters_from_config_plus_read_write(): spatial_setup=ss, nslots=1, npi_scenario="None", - config_version="v3", npi_config_seir=config["interventions"]["settings"]["None"], parameters_config=config["seir"]["parameters"], seeding_config=config["seeding"], @@ -59,8 +58,7 @@ def test_parameters_from_config_plus_read_write(): parameter_config=config["seir"]["parameters"], ti=s.ti, tf=s.tf, - nodenames=s.spatset.nodenames, - config_version="v3", + subpop_names=s.subpop_struct.subpop_names, ) n_days = 10 nnodes = 5 @@ -69,8 +67,7 @@ def test_parameters_from_config_plus_read_write(): parameter_config=config["seir"]["parameters"], ti=s.ti, tf=s.tf, - nodenames=s.spatset.nodenames, - config_version="v3", + subpop_names=s.subpop_struct.subpop_names, ) p_draw = p.parameters_quick_draw(n_days=10, nnodes=5) # test shape @@ -82,8 +79,7 @@ def test_parameters_from_config_plus_read_write(): parameter_config=config["seir"]["parameters"], ti=s.ti, tf=s.tf, - nodenames=s.spatset.nodenames, - config_version="v3", + subpop_names=s.subpop_struct.subpop_names, ) p_load = rhs.parameters_load(param_df=read_df("test_pwrite.parquet"), n_days=n_days, nnodes=nnodes) @@ -95,12 +91,12 @@ def test_parameters_quick_draw_old(): config.read(user=False) config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) index = 1 run_id = "test_parameter" @@ -115,7 +111,6 @@ def test_parameters_quick_draw_old(): seeding_config=config["seeding"], ti=config["start_date"].as_date(), tf=config["end_date"].as_date(), - config_version="v3", interactive=True, write_csv=False, first_sim_index=index, @@ -130,8 +125,7 @@ def test_parameters_quick_draw_old(): parameter_config=config["seir"]["parameters"], ti=s.ti, tf=s.tf, - nodenames=s.spatset.nodenames, - config_version="v3", + subpop_names=s.subpop_struct.subpop_names, ) ### Check that the object is well constructed: @@ -169,12 +163,12 @@ def test_parameters_from_timeserie_file(): config.clear() config.read(user=False) config.set_file(f"{DATA_DIR}/config_compartmental_model_format.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) index = 1 run_id = "test_parameter" @@ -184,7 +178,6 @@ def test_parameters_from_timeserie_file(): spatial_setup=ss, nslots=1, npi_scenario="None", - config_version="v3", npi_config_seir=config["interventions"]["settings"]["None"], parameters_config=config["seir"]["parameters"], seeding_config=config["seeding"], @@ -204,8 +197,7 @@ def test_parameters_from_timeserie_file(): parameter_config=config["seir"]["parameters"], ti=s.ti, tf=s.tf, - nodenames=s.spatset.nodenames, - config_version="v3", + subpop_names=s.subpop_struct.subpop_names, ) n_days = 10 nnodes = 5 @@ -214,8 +206,7 @@ def test_parameters_from_timeserie_file(): parameter_config=config["seir"]["parameters"], ti=s.ti, tf=s.tf, - nodenames=s.spatset.nodenames, - config_version="v3", + subpop_names=s.subpop_struct.subpop_names, ) p_draw = p.parameters_quick_draw(n_days=10, nnodes=5) # test shape @@ -227,8 +218,7 @@ def test_parameters_from_timeserie_file(): parameter_config=config["seir"]["parameters"], ti=s.ti, tf=s.tf, - nodenames=s.spatset.nodenames, - config_version="v3", + subpop_names=s.subpop_struct.subpop_names, ) p_load = rhs.parameters_load(param_df=read_df("test_pwrite.parquet"), n_days=n_days, nnodes=nnodes) diff --git a/flepimop/gempyor_pkg/tests/seir/test_seir.py b/flepimop/gempyor_pkg/tests/seir/test_seir.py index 4402e8051..22f86d5ea 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_seir.py +++ b/flepimop/gempyor_pkg/tests/seir/test_seir.py @@ -8,7 +8,7 @@ import pyarrow as pa import pyarrow.parquet as pq -from gempyor import setup, seir, NPI, file_paths +from gempyor import setup, seir, NPI, file_paths, subpopulation_structure from gempyor.utils import config @@ -20,12 +20,12 @@ def test_check_values(): os.chdir(os.path.dirname(__file__)) config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_values", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( @@ -73,12 +73,12 @@ def test_check_values(): def test_constant_population_legacy_integration(): config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) first_sim_index = 1 @@ -108,7 +108,7 @@ def test_constant_population_legacy_integration(): seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) - npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) + npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) params = s.parameters.parameters_quick_draw(s.n_days, s.nnodes) params = s.parameters.parameters_reduce(params, npi) @@ -285,12 +285,12 @@ def test_steps_SEIR_nb_simple_spread_with_txt_matrices(): print("test mobility with txt matrices") config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) first_sim_index = 1 @@ -319,7 +319,7 @@ def test_steps_SEIR_nb_simple_spread_with_txt_matrices(): seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) - npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) + npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) params = s.parameters.parameters_quick_draw(s.n_days, s.nnodes) params = s.parameters.parameters_reduce(params, npi) @@ -370,12 +370,12 @@ def test_steps_SEIR_nb_simple_spread_with_csv_matrices(): config.set_file(f"{DATA_DIR}/config.yml") print("test mobility with csv matrices") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) first_sim_index = 1 @@ -405,7 +405,7 @@ def test_steps_SEIR_nb_simple_spread_with_csv_matrices(): seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) - npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) + npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) params = s.parameters.parameters_quick_draw(s.n_days, s.nnodes) params = s.parameters.parameters_reduce(params, npi) @@ -440,12 +440,12 @@ def test_steps_SEIR_no_spread(): print("test mobility with no spread") config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) first_sim_index = 1 @@ -476,7 +476,7 @@ def test_steps_SEIR_no_spread(): s.mobility.data = s.mobility.data * 0 - npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) + npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) params = s.parameters.parameters_quick_draw(s.n_days, s.nnodes) params = s.parameters.parameters_reduce(params, npi) @@ -541,12 +541,12 @@ def test_continuation_resume(): spatial_base_path = pathlib.Path(config["data_path"].get()) s = setup.Setup( setup_name=config["name"].get() + "_" + str(npi_scenario), - spatial_setup=setup.SpatialSetup( + spatial_setup=subpopulation_structure.SubpopulationStructure( setup_name=config["setup_name"].get(), geodata_file=spatial_base_path / spatial_config["geodata"].get(), mobility_file=spatial_base_path / spatial_config["mobility"].get(), - popnodes_key=spatial_config["popnodes"].get(), - nodenames_key=spatial_config["nodenames"].get(), + popnodes_key="population", + subpop_names_key="subpop", ), nslots=nslots, npi_scenario=npi_scenario, @@ -591,12 +591,12 @@ def test_continuation_resume(): spatial_base_path = pathlib.Path(config["data_path"].get()) s = setup.Setup( setup_name=config["name"].get() + "_" + str(npi_scenario), - spatial_setup=setup.SpatialSetup( + spatial_setup=subpopulation_structure.SubpopulationStructure( setup_name=config["setup_name"].get(), geodata_file=spatial_base_path / spatial_config["geodata"].get(), mobility_file=spatial_base_path / spatial_config["mobility"].get(), - popnodes_key=spatial_config["popnodes"].get(), - nodenames_key=spatial_config["nodenames"].get(), + popnodes_key="population", + subpop_names_key="subpop", ), nslots=nslots, npi_scenario=npi_scenario, @@ -659,12 +659,12 @@ def test_inference_resume(): spatial_base_path = pathlib.Path(config["data_path"].get()) s = setup.Setup( setup_name=config["name"].get() + "_" + str(npi_scenario), - spatial_setup=setup.SpatialSetup( + spatial_setup=subpopulation_structure.SubpopulationStructure( setup_name=config["setup_name"].get(), geodata_file=spatial_base_path / spatial_config["geodata"].get(), mobility_file=spatial_base_path / spatial_config["mobility"].get(), - popnodes_key=spatial_config["popnodes"].get(), - nodenames_key=spatial_config["nodenames"].get(), + popnodes_key="population", + subpop_names_key="subpop", ), nslots=nslots, npi_scenario=npi_scenario, @@ -704,12 +704,12 @@ def test_inference_resume(): spatial_base_path = pathlib.Path(config["data_path"].get()) s = setup.Setup( setup_name=config["name"].get() + "_" + str(npi_scenario), - spatial_setup=setup.SpatialSetup( + spatial_setup=subpopulation_structure.SubpopulationStructure( setup_name=config["setup_name"].get(), geodata_file=spatial_base_path / spatial_config["geodata"].get(), mobility_file=spatial_base_path / spatial_config["mobility"].get(), - popnodes_key=spatial_config["popnodes"].get(), - nodenames_key=spatial_config["nodenames"].get(), + popnodes_key="population", + subpop_names_key="subpop", ), nslots=nslots, npi_scenario=npi_scenario, @@ -752,12 +752,12 @@ def test_parallel_compartments_with_vacc(): os.chdir(os.path.dirname(__file__)) config.set_file(f"{DATA_DIR}/config_parallel.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) first_sim_index = 1 @@ -787,7 +787,7 @@ def test_parallel_compartments_with_vacc(): seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) - npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) + npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) params = s.parameters.parameters_quick_draw(s.n_days, s.nnodes) params = s.parameters.parameters_reduce(params, npi) @@ -846,12 +846,12 @@ def test_parallel_compartments_no_vacc(): os.chdir(os.path.dirname(__file__)) config.set_file(f"{DATA_DIR}/config_parallel.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) first_sim_index = 1 @@ -882,7 +882,7 @@ def test_parallel_compartments_no_vacc(): seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) - npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) + npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) params = s.parameters.parameters_quick_draw(s.n_days, s.nnodes) params = s.parameters.parameters_reduce(params, npi) diff --git a/flepimop/gempyor_pkg/tests/seir/test_setup.py b/flepimop/gempyor_pkg/tests/seir/test_setup.py index e7e5d3630..df045ea80 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_setup.py +++ b/flepimop/gempyor_pkg/tests/seir/test_setup.py @@ -5,7 +5,7 @@ import pytest import confuse -from gempyor import setup, parameters +from gempyor import setup, subpopulation_structure, parameters from gempyor.utils import config @@ -15,14 +15,14 @@ os.chdir(os.path.dirname(__file__)) -class TestSetup: - def test_Setup_success(self): - ss = setup.SpatialSetup( +class TestSubpopulationStructure: + def test_SubpopulationStructure_success(self): + ss = subpopulation_structure.SubpopulationStructure( setup_name=TEST_SETUP_NAME, geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( setup_name = TEST_SETUP_NAME, @@ -54,78 +54,78 @@ def test_Setup_success(self): def test_tf_is_ahead_of_ti_fail(self): # time to finish (tf) is ahead of time to start(ti) error with pytest.raises(ValueError, match=r".*tf.*less.*"): - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", - ) - s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-03-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - npi_scenario=None, - config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - outcomes_config={}, - outcome_scenario=None, - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id=None, - in_prefix=None, - out_run_id=None, - out_prefix=None, - stoch_traj_flag=False, - ) + ss = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + subpop_names_key="subpop", + ) + s = setup.Setup( + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-03-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + npi_scenario=None, + # config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={}, + outcome_scenario=None, + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, + ) def test_w_config_seir_exists_success(self): # if seir_config is None and config["seir"].exists() then update config.clear() config.read(user=False) config.set_file(f"{DATA_DIR}/config_seir.yml") - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", + ss = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - npi_scenario=None, - config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + # config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, # parameters_config={"alpha":{"value":{"distribution":"fixed","value":.9}}}, - parameters_config={}, - seir_config=None, - outcomes_config={}, - outcome_scenario=None, - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id=None, - in_prefix=None, - out_run_id=None, - out_prefix=None, - stoch_traj_flag=False, + parameters_config={}, + seir_config=None, + outcomes_config={}, + outcome_scenario=None, + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, ) assert s.seir_config != None @@ -139,38 +139,38 @@ def test_w_config_seir_integration_method_rk4_1_success(self): config.clear() config.read(user=False) config.set_file(f"{DATA_DIR}/config_seir_integration_method_rk4_1.yml") - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", + ss = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - npi_scenario=None, - config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - outcomes_config={}, - outcome_scenario=None, - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id=None, - in_prefix=None, - out_run_id=None, - out_prefix=None, - stoch_traj_flag=False, + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + # config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={}, + outcome_scenario=None, + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, ) assert s.integration_method == "rk4.jit" @@ -181,38 +181,38 @@ def test_w_config_seir_integration_method_rk4_2_success(self): config.clear() config.read(user=False) config.set_file(f"{DATA_DIR}/config_seir_integration_method_rk4_2.yml") - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", + ss = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - npi_scenario=None, - config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - outcomes_config={}, - outcome_scenario=None, - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id=None, - in_prefix=None, - out_run_id=None, - out_prefix=None, - stoch_traj_flag=False, + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + # config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={}, + outcome_scenario=None, + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, ) assert s.integration_method == "rk4.jit" @@ -221,38 +221,38 @@ def test_w_config_seir_no_integration_success(self): config.clear() config.read(user=False) config.set_file(f"{DATA_DIR}/config_seir_no_integration.yml") - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", + ss = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - npi_scenario=None, - config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - outcomes_config={}, - outcome_scenario=None, - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id=None, - in_prefix=None, - out_run_id=None, - out_prefix=None, - stoch_traj_flag=False, + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + # config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={}, + outcome_scenario=None, + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, ) assert s.integration_method == "rk4.jit" @@ -264,21 +264,21 @@ def test_w_config_seir_unknown_integration_method_fail(self): config.clear() config.read(user=False) config.set_file(f"{DATA_DIR}/config_seir_unknown_integration.yml") - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", - ) + ss = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + subpop_names_key="subpop", + ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), # first_sim_index=1, - ) + ) # print(s.integration_method) def test_w_config_seir_integration_but_no_dt_success(self): @@ -286,27 +286,27 @@ def test_w_config_seir_integration_but_no_dt_success(self): config.clear() config.read(user=False) config.set_file(f"{DATA_DIR}/config_seir_no_dt.yml") - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", + ss = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - npi_scenario=None, - config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - dt=None, # step size, in days + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + # config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + dt=None, # step size, in days ) assert s.dt == 2.0 @@ -323,13 +323,13 @@ def test_w_config_seir_old_integration_method_fail(self): geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( setup_name = TEST_SETUP_NAME, spatial_setup =ss, nslots = 1, - config_version="v2", + # config_version="v2", ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), ) @@ -344,7 +344,7 @@ def test_w_config_seir_config_version_not_provided_fail(self): geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( setup_name = TEST_SETUP_NAME, @@ -353,7 +353,7 @@ def test_w_config_seir_config_version_not_provided_fail(self): ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), npi_scenario=None, - config_version="v1", + # config_version="v1", npi_config_seir={}, seeding_config={}, initial_conditions_config={}, @@ -368,37 +368,37 @@ def test_w_config_compartments_and_seir_config_not_None_success(self): config.clear() config.read(user=False) config.set_file(f"{DATA_DIR}/config_compartment.yml") - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", + ss = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - npi_scenario=None, - config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - dt=None, # step size, in days + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + # config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + dt=None, # step size, in days ) def test_config_outcome_config_and_scenario_success(self): # if outcome_config and outcome_scenario were set - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name=TEST_SETUP_NAME, geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( setup_name = TEST_SETUP_NAME, @@ -407,62 +407,80 @@ def test_config_outcome_config_and_scenario_success(self): ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), npi_scenario=None, - config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, parameters_config={}, seir_config=None, dt=None, # step size, in days - outcomes_config={"interventions":{"settings":{"None": - {"template":"Reduce", - "parameter":"r0", - "value": - { - "distribution":"fixed", - "value":0 - } - } - }}}, - outcome_scenario="None", # caution! selected the defined "None" - write_csv=True, + outcomes_config= + { + "interventions": + { + "settings": + { + "None": + { + "template":"Reduce", + "parameter":"r0", + "value": + { + "distribution":"fixed", + "value":0 + } + } + } + } + }, + outcome_scenario="None", # caution! selected the defined "None" + write_csv=True, ) assert s.npi_config_outcomes == s.outcomes_config["interventions"]["settings"]["None"] assert s.extension == "csv" def test_config_write_csv_and_write_parquet_success(self): # if both write_csv and write_parquet are True - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", + ss = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - npi_scenario=None, - config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - dt=None, # step size, in days - outcomes_config={"interventions":{"settings":{"None": - {"template":"Reduce", - "parameter":"r0", - "value": - { - "distribution":"fixed", - "value":0 - } - } - }}}, + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + # config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + dt=None, # step size, in days + outcomes_config= + { + "interventions": + { + "settings": + { + "None": + { + "template":"Reduce", + "parameter":"r0", + "value": + { + "distribution":"fixed", + "value":0 + } + } + } + } + }, outcome_scenario="None", # caution! selected the defined "None" write_csv=True, write_parquet=True, @@ -474,27 +492,27 @@ def test_w_config_seir_exists_and_outcomes_config(self): config.clear() config.read(user=False) config.set_file(f"{DATA_DIR}/config_seir.yml") - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", + ss = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + popnodes_key="population", + subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - npi_scenario=None, - config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - outcomes_config={"interventions":{"settings":{"None": + setup_name = TEST_SETUP_NAME, + spatial_setup =ss, + nslots = 1, + ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + npi_scenario=None, + # config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + outcomes_config={"interventions":{"settings":{"None": {"template":"Reduce", "parameter":"r0", "value": @@ -503,18 +521,18 @@ def test_w_config_seir_exists_and_outcomes_config(self): "value":0 } } - }}}, - outcome_scenario="None", - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id="in_run_id_0", - in_prefix=None, - out_run_id="out_run_id_0", - out_prefix=None, - stoch_traj_flag=False, + }}}, + outcome_scenario="None", + interactive=True, + write_csv=False, + write_parquet=False, + dt=None, # step size, in days + first_sim_index=1, + in_run_id="in_run_id_0", + in_prefix=None, + out_run_id="out_run_id_0", + out_prefix=None, + stoch_traj_flag=False, ) #s.get_input_filename(ftype="spar", sim_id=0, extension_override="") os.path.isfile(DATA_DIR+s.get_input_filename(ftype="seir", sim_id=0)) @@ -545,12 +563,12 @@ def test_w_config_seir_exists_and_outcomes_config(self): ''' def test_SpatialSetup_npz_success3(self): - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name=TEST_SETUP_NAME, geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.npz", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) def test_SpatialSetup_wihout_mobility_success3(self): ss = setup.SpatialSetup( @@ -558,38 +576,18 @@ def test_SpatialSetup_wihout_mobility_success3(self): geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility0.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) def test_bad_popnodes_key_fail(self): # Bad popnodes_key error with pytest.raises(ValueError, match=r".*popnodes_key.*"): - setup.SpatialSetup( + subpopulation_structure.SubpopulationStructure( setup_name=TEST_SETUP_NAME, geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility_small.txt", popnodes_key="wrong", - nodenames_key="geoid", - ) - - def test_population_0_nodes_fail(self): - with pytest.raises(ValueError, match=r".*population.*zero.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata0.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", - ) - - def test_mobility_fileformat_fail(self): - with pytest.raises(ValueError, match=r".*Mobility.*longform.*matrix.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility", - popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) def test_bad_nodenames_key_fail(self): @@ -599,37 +597,37 @@ def test_bad_nodenames_key_fail(self): geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="wrong", + subpop_names_key="wrong", ) def test_duplicate_nodenames_key_fail(self): with pytest.raises(ValueError, match=r".*duplicate.*"): - setup.SpatialSetup( + subpopulation_structure.SubpopulationStructure( setup_name=TEST_SETUP_NAME, geodata_file=f"{DATA_DIR}/geodata_dup.csv", mobility_file=f"{DATA_DIR}/mobility.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) ''' def test_mobility_shape_in_npz_fail(self): with pytest.raises(ValueError, match=r".*mobility.*Actual.*"): - setup.SpatialSetup( + subpopulation_structure.SubpopulationStructure( setup_name=TEST_SETUP_NAME, geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility_2x3.npz", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) ''' def test_mobility_dimensions_fail(self): with pytest.raises(ValueError, match=r".*mobility.*dimensions.*"): - setup.SpatialSetup( + subpopulation_structure.SubpopulationStructure( setup_name=TEST_SETUP_NAME, geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility_small.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) def test_mobility_same_ori_dest_fail(self): @@ -639,17 +637,17 @@ def test_mobility_same_ori_dest_fail(self): geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility_same_ori_dest.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) def test_mobility_too_big_fail(self): with pytest.raises(ValueError, match=r".*mobility.*population.*"): - setup.SpatialSetup( + subpopulation_structure.SubpopulationStructure( setup_name=TEST_SETUP_NAME, geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility_big.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) def test_mobility_data_exceeded_fail(self): with pytest.raises(ValueError, match=r".*mobility.*exceed.*"): diff --git a/flepimop/main_scripts/create_seeding.R b/flepimop/main_scripts/create_seeding.R index a085af059..b52681044 100644 --- a/flepimop/main_scripts/create_seeding.R +++ b/flepimop/main_scripts/create_seeding.R @@ -15,7 +15,7 @@ # spatial_setup: # geodata: -# nodenames: +# subpop: # # seeding: # lambda_file: @@ -24,7 +24,7 @@ # # ## Input Data # -# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::nodenames} that denotes the geoids +# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::subpop} that denotes the subpop # # ## Output Data # @@ -153,15 +153,15 @@ if (seed_variants) { ## Check some data attributes: ## This is a hack: -if ("geoid" %in% names(cases_deaths)) { - cases_deaths$FIPS <- cases_deaths$geoid +if ("subpop" %in% names(cases_deaths)) { + cases_deaths$FIPS <- cases_deaths$subpop warning("Changing FIPS name in seeding. This is a hack") } if ("date" %in% names(cases_deaths)) { cases_deaths$Update <- cases_deaths$date warning("Changing Update name in seeding. This is a hack") } -obs_nodename <- config$spatial_setup$nodenames +obs_subpop <- config$spatial_setup$subpop required_column_names <- NULL check_required_names <- function(df, cols, msg) { @@ -272,12 +272,12 @@ geodata <- flepicommon::load_geodata_file( TRUE ) -all_geoids <- geodata[[config$spatial_setup$nodenames]] +all_subpop <- geodata[[config$spatial_setup$subpop]] incident_cases <- incident_cases %>% - dplyr::filter(FIPS %in% all_geoids) %>% + dplyr::filter(FIPS %in% all_subpop) %>% dplyr::select(!!!required_column_names) incident_cases <- incident_cases %>% filter(value>0) @@ -307,7 +307,7 @@ incident_cases <- incident_cases %>% dplyr::ungroup() %>% dplyr::select(!!!rlang::syms(required_column_names)) -names(incident_cases)[1:3] <- c("place", "date", "amount") +names(incident_cases)[1:3] <- c("subpop", "date", "amount") incident_cases <- incident_cases %>% dplyr::filter(!is.na(amount) | !is.na(date)) @@ -332,12 +332,12 @@ if ("compartments" %in% names(config) & "pop_seed_file" %in% names(config[["seed seeding_pop$no_perturb <- TRUE } seeding_pop <- seeding_pop %>% - dplyr::filter(place %in% all_geoids) %>% + dplyr::filter(subpop %in% all_subpop) %>% dplyr::select(!!!colnames(incident_cases)) incident_cases <- incident_cases %>% dplyr::bind_rows(seeding_pop) %>% - dplyr::arrange(place, date) + dplyr::arrange(subpop, date) } @@ -346,7 +346,7 @@ if ("compartments" %in% names(config) & "pop_seed_file" %in% names(config[["seed if (max(incident_cases$date) < lubridate::as_date(config$start_date)){ incident_cases <- incident_cases %>% - group_by(place) %>% + group_by(subpop) %>% filter(date == min(date)) %>% distinct() %>% ungroup() %>% diff --git a/flepimop/main_scripts/create_seeding_added.R b/flepimop/main_scripts/create_seeding_added.R index efcff2b01..ccfee8f89 100644 --- a/flepimop/main_scripts/create_seeding_added.R +++ b/flepimop/main_scripts/create_seeding_added.R @@ -15,7 +15,7 @@ # spatial_setup: # geodata: -# nodenames: +# subpop: # # seeding: # lambda_file: @@ -24,7 +24,7 @@ # # ## Input Data # -# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::nodenames} that denotes the geoids +# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::subpop} that denotes the subpop # # ## Output Data # @@ -151,15 +151,15 @@ if (seed_variants) { ## Check some data attributes: ## This is a hack: -if ("geoid" %in% names(cases_deaths)) { - cases_deaths$FIPS <- cases_deaths$geoid +if ("subpop" %in% names(cases_deaths)) { + cases_deaths$FIPS <- cases_deaths$subpop warning("Changing FIPS name in seeding. This is a hack") } if ("date" %in% names(cases_deaths)) { cases_deaths$Update <- cases_deaths$date warning("Changing Update name in seeding. This is a hack") } -obs_nodename <- config$spatial_setup$nodenames +obs_subpop <- config$spatial_setup$subpop required_column_names <- NULL check_required_names <- function(df, cols, msg) { @@ -270,12 +270,12 @@ geodata <- flepicommon::load_geodata_file( TRUE ) -all_geoids <- geodata[[config$spatial_setup$nodenames]] +all_subpop <- geodata[[config$spatial_setup$subpop]] incident_cases <- incident_cases %>% - dplyr::filter(FIPS %in% all_geoids) %>% + dplyr::filter(FIPS %in% all_subpop) %>% dplyr::select(!!!required_column_names) incident_cases <- incident_cases %>% filter(value>0) @@ -305,7 +305,7 @@ incident_cases <- incident_cases %>% dplyr::ungroup() %>% dplyr::select(!!!rlang::syms(required_column_names)) -names(incident_cases)[1:3] <- c("place", "date", "amount") +names(incident_cases)[1:3] <- c("subpop", "date", "amount") incident_cases <- incident_cases %>% dplyr::filter(!is.na(amount) | !is.na(date)) @@ -332,12 +332,12 @@ if (!("no_perturb" %in% colnames(incident_cases))){ # seeding_pop$no_perturb <- TRUE # } # seeding_pop <- seeding_pop %>% -# dplyr::filter(place %in% all_geoids) %>% +# dplyr::filter(subpop %in% all_subpop) %>% # dplyr::select(!!!colnames(incident_cases)) # # incident_cases <- incident_cases %>% # dplyr::bind_rows(seeding_pop) %>% -# dplyr::arrange(place, date) +# dplyr::arrange(subpop, date) # } diff --git a/flepimop/main_scripts/inference_slot.R b/flepimop/main_scripts/inference_slot.R index 38c1f1f2b..41b6d708c 100644 --- a/flepimop/main_scripts/inference_slot.R +++ b/flepimop/main_scripts/inference_slot.R @@ -32,7 +32,7 @@ option_list = list( optparse::make_option(c("-L", "--reset_chimeric_on_accept"), action = "store", default = Sys.getenv("FLEPI_RESET_CHIMERICS", FALSE), type = 'logical', help = 'Should the chimeric parameters get reset to global parameters when a global acceptance occurs'), optparse::make_option(c("-M", "--memory_profiling"), action = "store", default = Sys.getenv("FLEPI_MEM_PROFILE", FALSE), type = 'logical', help = 'Should the memory profiling be run during iterations'), optparse::make_option(c("-P", "--memory_profiling_iters"), action = "store", default = Sys.getenv("FLEPI_MEM_PROF_ITERS", 100), type = 'integer', help = 'If doing memory profiling, after every X iterations run the profiler'), - optparse::make_option(c("-g", "--geoid_len"), action="store", default=Sys.getenv("GEOID_LENGTH", 5), type='integer', help = "number of digits in geoid") + optparse::make_option(c("-g", "--subpop_len"), action="store", default=Sys.getenv("SUBPOP_LENGTH", 5), type='integer', help = "number of digits in subpop") ) parser=optparse::OptionParser(option_list=option_list) @@ -99,10 +99,10 @@ suppressMessages( config$data_path, config$spatial_setup$geodata, sep = "/" ), - geoid_len = opt$geoid_len + subpop_len = opt$subpop_len ) ) -obs_nodename <- config$spatial_setup$nodenames +obs_subpop <- config$spatial_setup$subpop ##Load simulations per slot from config if not defined on command line ##command options take precedence @@ -163,7 +163,7 @@ if (is.null(config$inference$gt_source)){ } gt_scale <- ifelse(state_level, "US state", "US county") -fips_codes_ <- geodata[[obs_nodename]] +fips_codes_ <- geodata[[obs_subpop]] gt_start_date <- lubridate::ymd(config$start_date) if (opt$ground_truth_start != "") { @@ -193,7 +193,7 @@ if (config$inference$do_inference){ # obs <- inference::get_ground_truth( # data_path = data_path, # fips_codes = fips_codes_, - # fips_column_name = obs_nodename, + # fips_column_name = obs_subpop, # start_date = gt_start_date, # end_date = gt_end_date, # gt_source = gt_source, @@ -210,16 +210,16 @@ if (config$inference$do_inference){ dplyr::filter(FIPS %in% fips_codes_, date >= gt_start_date, date <= gt_end_date) %>% dplyr::right_join(tidyr::expand_grid(FIPS = unique(.$FIPS), date = unique(.$date))) %>% dplyr::mutate_if(is.numeric, dplyr::coalesce, 0) %>% - dplyr::rename(!!obs_nodename := FIPS) + dplyr::rename(!!obs_subpop := FIPS) - geonames <- unique(obs[[obs_nodename]]) + geonames <- unique(obs[[obs_subpop]]) ## Compute statistics data_stats <- lapply( geonames, function(x) { - df <- obs[obs[[obs_nodename]] == x, ] + df <- obs[obs[[obs_subpop]] == x, ] inference::getStats( df, "date", @@ -235,7 +235,7 @@ if (config$inference$do_inference){ likelihood_calculation_fun <- function(sim_hosp){ sim_hosp <- dplyr::filter(sim_hosp,sim_hosp$time >= min(obs$date),sim_hosp$time <= max(obs$date)) - lhs <- unique(sim_hosp[[obs_nodename]]) + lhs <- unique(sim_hosp[[obs_subpop]]) rhs <- unique(names(data_stats)) all_locations <- rhs[rhs %in% lhs] @@ -243,7 +243,7 @@ if (config$inference$do_inference){ inference::aggregate_and_calc_loc_likelihoods( all_locations = all_locations, # technically different modeled_outcome = sim_hosp, - obs_nodename = obs_nodename, + obs_subpop = obs_subpop, targets_config = config[["inference"]][["statistics"]], obs = obs, ground_truth_data = data_stats, @@ -253,7 +253,7 @@ if (config$inference$do_inference){ geodata = geodata, snpi = arrow::read_parquet(first_global_files[['snpi_filename']]), hnpi = arrow::read_parquet(first_global_files[['hnpi_filename']]), - hpar = dplyr::mutate(arrow::read_parquet(first_global_files[['hpar_filename']]),parameter=paste(quantity,!!rlang::sym(obs_nodename),outcome,sep='_')), + hpar = dplyr::mutate(arrow::read_parquet(first_global_files[['hpar_filename']]),parameter=paste(quantity,!!rlang::sym(obs_subpop),outcome,sep='_')), start_date = gt_start_date, end_date = gt_end_date ) @@ -262,17 +262,17 @@ if (config$inference$do_inference){ } else { - geonames <- obs_nodename + geonames <- obs_subpop likelihood_calculation_fun <- function(sim_hosp){ - all_locations <- unique(sim_hosp[[obs_nodename]]) + all_locations <- unique(sim_hosp[[obs_subpop]]) ## No references to config$inference$statistics inference::aggregate_and_calc_loc_likelihoods( all_locations = all_locations, # technically different modeled_outcome = sim_hosp, - obs_nodename = obs_nodename, + obs_subpop = obs_subpop, targets_config = config[["inference"]][["statistics"]], obs = sim_hosp, ground_truth_data = sim_hosp, @@ -282,7 +282,7 @@ if (config$inference$do_inference){ geodata = geodata, snpi = arrow::read_parquet(first_global_files[['snpi_filename']]), hnpi = arrow::read_parquet(first_global_files[['hnpi_filename']]), - hpar = dplyr::mutate(arrow::read_parquet(first_global_files[['hpar_filename']]),parameter=paste(quantity,!!rlang::sym(obs_nodename),outcome,sep='_')), + hpar = dplyr::mutate(arrow::read_parquet(first_global_files[['hpar_filename']]),parameter=paste(quantity,!!rlang::sym(obs_subpop),outcome,sep='_')), start_date = gt_start_date, end_date = gt_end_date ) @@ -353,7 +353,7 @@ for(npi_scenario in npi_scenarios) { ### Set up initial conditions ---------- ## python configuration: build simulator model initialized with compartment and all. - gempyor_inference_runner <- gempyor$InferenceSimulator( + gempyor_inference_runner <- gempyor$GempyorSimulator( config_path=opt$config, run_id=opt$run_id, prefix=global_block_prefix, @@ -476,7 +476,7 @@ for(npi_scenario in npi_scenarios) { } proposed_seeding <- initial_seeding } - + # proposed_snpi <- inference::perturb_snpi_from_file(initial_snpi, config$interventions$settings, chimeric_likelihood_data) # proposed_hnpi <- inference::perturb_hnpi_from_file(initial_hnpi, config$interventions$settings, chimeric_likelihood_data) # proposed_spar <- inference::perturb_spar_from_file(initial_spar, config$interventions$settings, chimeric_likelihood_data) @@ -505,19 +505,19 @@ for(npi_scenario in npi_scenarios) { load_ID=TRUE, sim_id2load=this_index) if (err != 0){ - stop("InferenceSimulator failed to run") + stop("GempyorSimulator failed to run") } if (config$inference$do_inference){ sim_hosp <- flepicommon::read_file_of_type(gsub(".*[.]","",this_global_files[['hosp_filename']]))(this_global_files[['hosp_filename']]) %>% dplyr::filter(time >= min(obs$date),time <= max(obs$date)) - lhs <- unique(sim_hosp[[obs_nodename]]) + lhs <- unique(sim_hosp[[obs_subpop]]) rhs <- unique(names(data_stats)) all_locations <- rhs[rhs %in% lhs] } else { sim_hosp <- flepicommon::read_file_of_type(gsub(".*[.]","",this_global_files[['hosp_filename']]))(this_global_files[['hosp_filename']]) - all_locations <- unique(sim_hosp[[obs_nodename]]) + all_locations <- unique(sim_hosp[[obs_subpop]]) obs <- sim_hosp data_stats <- sim_hosp } @@ -526,7 +526,7 @@ for(npi_scenario in npi_scenarios) { proposed_likelihood_data <- inference::aggregate_and_calc_loc_likelihoods( all_locations = all_locations, modeled_outcome = sim_hosp, - obs_nodename = obs_nodename, + obs_subpop = obs_subpop, targets_config = config[["inference"]][["statistics"]], obs = obs, ground_truth_data = data_stats, @@ -538,7 +538,7 @@ for(npi_scenario in npi_scenarios) { hnpi = proposed_hnpi, hpar = dplyr::mutate( proposed_hpar, - parameter = paste(quantity, !!rlang::sym(obs_nodename), outcome, sep = "_") + parameter = paste(quantity, !!rlang::sym(obs_subpop), outcome, sep = "_") ), start_date = gt_start_date, end_date = gt_end_date diff --git a/flepimop/main_scripts/seir_init_immuneladder.R b/flepimop/main_scripts/seir_init_immuneladder.R index daa96524f..a7c2d3e84 100644 --- a/flepimop/main_scripts/seir_init_immuneladder.R +++ b/flepimop/main_scripts/seir_init_immuneladder.R @@ -15,7 +15,7 @@ # spatial_setup: # geodata: -# nodenames: +# subpop: # # seeding: # lambda_file: @@ -24,7 +24,7 @@ # # ## Input Data # -# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::nodenames} that denotes the geoids +# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::subpop} that denotes the subpop # # ## Output Data # @@ -296,11 +296,11 @@ seir_dat_static <- seir_dat_static %>% filter(mc_value_type == "prevalence") %>% mutate(mc_vaccination_stage = ifelse(mc_vaccination_stage == "3dose", "vaccinated", "unvaccinated")) %>% mutate(mc_variant_type = "ALL") %>% - pivot_longer(cols = -c(starts_with("mc_"), date), names_to = "geoid", values_to = "value") %>% + pivot_longer(cols = -c(starts_with("mc_"), date), names_to = "subpop", values_to = "value") %>% group_by(across(c(-value))) %>% summarise(value = sum(value, na.rm = TRUE)) %>% mutate(mc_name = paste(mc_infection_stage, mc_vaccination_stage, mc_variant_type, mc_age_strata, sep = "_")) %>% - pivot_wider(names_from = geoid, values_from = value) %>% + pivot_wider(names_from = subpop, values_from = value) %>% dplyr::select(all_of(seir_dat_cols)) @@ -336,7 +336,7 @@ if (gradual_waning){ group_by(prob_immune, date, mc_value_type, mc_variant_type, mc_vaccination_stage, mc_age_strata, loc) %>% summarise(n = sum(n, na.rm=TRUE)) %>% as_tibble() %>% - left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% + left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% mutate(prop = n / pop_agestrata) %>% group_by(USPS, loc, date, mc_value_type, mc_variant_type, mc_vaccination_stage, mc_age_strata) %>% summarise(prop_immune = sum((n * prob_immune) / sum(n, na.rm = TRUE), na.rm = TRUE)) %>% @@ -382,7 +382,7 @@ seir_dat_changing <- seir_dat_changing %>% # geodata <- read_csv("data/geodata_2019_statelevel_agestrat.csv") # # seir_dat_changing %>% filter(mc_age_strata == "age18to64") %>% -# left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% +# left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% # mutate(prop = n / pop_agestrata) %>% # ggplot(aes(x = prob_immune_nom, y = prop, color = USPS)) + # geom_point() + @@ -394,7 +394,7 @@ seir_dat_changing <- seir_dat_changing %>% # theme(legend.position = "none", axis.text.x = element_text(angle = 90)) # # seir_dat_changing %>% -# left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% +# left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% # mutate(prop = n / pop_agestrata) %>% # group_by(USPS, loc, date, mc_value_type, mc_vaccination_stage, mc_age_strata) %>% # summarise(prop_immune = sum((n * prob_immune_nom) / sum(n, na.rm = TRUE), na.rm = TRUE)) %>% @@ -436,8 +436,8 @@ seir_dat_changing_final <- seir_dat_changing %>% # # CHECK # seir_dat_changing_final %>% # filter(mc_age_strata == "age18to64") %>% -# pivot_longer(cols=-c(mc_value_type:mc_name, date), names_to = "geoid", values_to = "n") %>% -# left_join(geodata %>% rename(geoid = geoid) %>% select(USPS, geoid, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% +# pivot_longer(cols=-c(mc_value_type:mc_name, date), names_to = "subpop", values_to = "n") %>% +# left_join(geodata %>% rename(subpop = subpop) %>% select(USPS, subpop, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% # mutate(prop = n / pop_agestrata) %>% # ggplot(aes(x = mc_infection_stage, y = n, color = USPS)) + # geom_point() + diff --git a/postprocessing/groundtruth_source.R b/postprocessing/groundtruth_source.R index 8bd5252c0..53f4bc701 100644 --- a/postprocessing/groundtruth_source.R +++ b/postprocessing/groundtruth_source.R @@ -102,10 +102,10 @@ clean_gt_forplots <- function(gt_data){ gt_long <- gt_long %>% rename(time=date, USPS=source) gt_long <- gt_long %>% - rename(geoid=FIPS, outcome_name = target, outcome = incid) + rename(subpop=FIPS, outcome_name = target, outcome = incid) gt_data <- gt_data %>% - rename(geoid=FIPS, time=date, USPS=source) + rename(subpop=FIPS, time=date, USPS=source) return(gt_data) } diff --git a/postprocessing/plot_predictions.R b/postprocessing/plot_predictions.R index 6e3160403..a2ae5592e 100644 --- a/postprocessing/plot_predictions.R +++ b/postprocessing/plot_predictions.R @@ -55,7 +55,7 @@ gt_data_2 <- gt_data_2 %>% mutate(cumH = 0) # incidH is only cumulative from sta gt_cl <- NULL if (any(outcomes_time_=="weekly")) { # Incident - gt_data_st_week <- get_weekly_incid(gt_data %>% dplyr::select(time, geoid, USPS, paste0("incid", outcomes_gt_[outcomes_time_gt_=="weekly"])) %>% mutate(sim_num = 0), + gt_data_st_week <- get_weekly_incid(gt_data %>% dplyr::select(time, subpop, USPS, paste0("incid", outcomes_gt_[outcomes_time_gt_=="weekly"])) %>% mutate(sim_num = 0), outcomes = outcomes_gt_[outcomes_time_gt_=="weekly"]) # Cumulative @@ -81,7 +81,7 @@ if (any(outcomes_time_=="weekly")) { } if (any(outcomes_time_=="daily")) { # Incident - gt_data_st_day <- get_daily_incid(gt_data %>% dplyr::select(time, geoid, USPS, paste0("incid", outcomes_gt_[outcomes_time_gt_=="daily"])) %>% mutate(sim_num = 0), + gt_data_st_day <- get_daily_incid(gt_data %>% dplyr::select(time, subpop, USPS, paste0("incid", outcomes_gt_[outcomes_time_gt_=="daily"])) %>% mutate(sim_num = 0), outcomes = outcomes_gt_[outcomes_time_gt_=="daily"]) # Cumulative diff --git a/postprocessing/postprocess_auto.py b/postprocessing/postprocess_auto.py index 4f005f9a2..39c2c7afa 100644 --- a/postprocessing/postprocess_auto.py +++ b/postprocessing/postprocess_auto.py @@ -16,8 +16,8 @@ import matplotlib.cbook as cbook from matplotlib.backends.backend_pdf import PdfPages -channelids = {"cspproduction": "C011YTUBJ7R", - "debug": "C04MAQWLEAW"} +channelids = {"cspproduction": "C011YTUBJ7R", "debug": "C04MAQWLEAW"} + class RunInfo: def __init__(self, run_id, config_path=None, folder_path=None): @@ -174,7 +174,7 @@ def generate_pdf(config_path, run_id, job_name, fs_results_path, slack_token, sl for run_name, run_info in all_runs.items(): run_id = run_info.run_id config_filepath = run_info.config_path - run_info.gempyor_simulator = gempyor.InferenceSimulator( + run_info.gempyor_simulator = gempyor.GempyorSimulator( config_path=config_filepath, run_id=run_id, # prefix=f"USA/inference/med/{run_id}/global/intermediate/000000001.", @@ -186,7 +186,7 @@ def generate_pdf(config_path, run_id, job_name, fs_results_path, slack_token, sl ) run_info.folder_path = f"{fs_results_path}/model_output" - node_names = run_info.gempyor_simulator.s.spatset.nodenames + node_names = run_info.gempyor_simulator.s.subpop_struct.subpop_names # In[5]: @@ -226,8 +226,8 @@ def generate_pdf(config_path, run_id, job_name, fs_results_path, slack_token, sl df_raw["sim"] = sim df_raw["ID"] = run_name df_raw = df_raw.drop("filename", axis=1) - # df_csv = df_csv.groupby(['slot','sim', 'ID', 'geoid']).sum().reset_index() - # df_csv = df_csv[['ll','sim', 'slot', 'ID','geoid']] + # df_csv = df_csv.groupby(['slot','sim', 'ID', 'subpop']).sum().reset_index() + # df_csv = df_csv[['ll','sim', 'slot', 'ID','subpop']] resultST[run_name].append(df_raw) full_df = pd.concat(resultST[run_name]) full_df @@ -267,7 +267,7 @@ def generate_pdf(config_path, run_id, job_name, fs_results_path, slack_token, sl for idp, nn in enumerate(node_names): idp = idp + 1 - all_nn = full_df[full_df["geoid"] == nn][["sim", "slot", "ll", "accept", "accept_avg", "accept_prob"]] + all_nn = full_df[full_df["subpop"] == nn][["sim", "slot", "ll", "accept", "accept_avg", "accept_prob"]] for ift, feature in enumerate(["ll", "accept", "accept_avg", "accept_prob"]): lls = all_nn.pivot(index="sim", columns="slot", values=feature) if feature == "accept": @@ -301,12 +301,12 @@ def generate_pdf(config_path, run_id, job_name, fs_results_path, slack_token, sl print(f"list of files to be sent over slack: {file_list}") if "production" in slack_channel.lower(): - channel=channelids["cspproduction"] + channel = channelids["cspproduction"] elif "debug" in slack_channel.lower(): - channel=channelids["debug"] + channel = channelids["debug"] else: print("no channel specified, not sending anything to slack") - channel=None + channel = None # slack_multiple_files( # slack_token=slack_token, diff --git a/postprocessing/postprocess_snapshot.R b/postprocessing/postprocess_snapshot.R index 5c03e16a1..e3b4d8a24 100644 --- a/postprocessing/postprocess_snapshot.R +++ b/postprocessing/postprocess_snapshot.R @@ -59,12 +59,12 @@ print(opt$select_outputs) config <- flepicommon::load_config(opt$config) -# Pull in geoid data +# Pull in subpop data geodata <- setDT(read.csv(file.path(config$data_path, config$spatial_setup$geodata))) ## gt_data MUST exist directly after a run gt_data <- data.table::fread(config$inference$gt_data_path) %>% - .[, geoid := stringr::str_pad(FIPS, width = 5, side = "left", pad = "0")] + .[, subpop := stringr::str_pad(FIPS, width = 5, side = "left", pad = "0")] # store list of files to save files_ <- c() @@ -77,7 +77,7 @@ pdf.options(useDingbats = TRUE) import_model_outputs <- function(scn_dir, outcome, global_opt, final_opt, lim_hosp = c("date", sapply(1:length(names(config$inference$statistics)), function(i) purrr::flatten(config$inference$statistics[i])$sim_var), - config$spatial_setup$nodenames)){ + config$spatial_setup$subpop)){ dir_ <- paste0(scn_dir, "/", outcome, "/", config$name, "/", @@ -145,7 +145,7 @@ print(end_time - start_time) if("hosp" %in% model_outputs){ gg_cols <- 8 - num_nodes <- length(unique(outputs_global$hosp %>% .[,get(config$spatial_setup$nodenames)])) + num_nodes <- length(unique(outputs_global$hosp %>% .[,get(config$spatial_setup$subpop)])) pdf_dims <- data.frame(width = gg_cols*2, length = num_nodes/gg_cols * 2) fname <- paste0("pplot/hosp_mod_outputs_", opt$run_id,".pdf") @@ -154,31 +154,31 @@ if("hosp" %in% model_outputs){ for(i in 1:length(fit_stats)){ statistics <- purrr::flatten(config$inference$statistics[i]) - cols_sim <- c("date", statistics$sim_var, config$spatial_setup$nodenames,"slot") - cols_data <- c("date", config$spatial_setup$nodenames, statistics$data_var) + cols_sim <- c("date", statistics$sim_var, config$spatial_setup$subpop,"slot") + cols_data <- c("date", config$spatial_setup$subpop, statistics$data_var) ## summarize slots print(outputs_global$hosp %>% .[, ..cols_sim] %>% .[, date := lubridate::as_date(date)] %>% - { if(config$spatial_setup$nodenames == 'geoid'){ - .[geodata %>% .[, geoid := stringr::str_pad(geoid, width = 5, side = "left", pad = "0")], on = .(geoid)]} + { if(config$spatial_setup$subpop == 'subpop'){ + .[geodata %>% .[, subpop := stringr::str_pad(subpop, width = 5, side = "left", pad = "0")], on = .(subpop)]} } %>% - { if(config$spatial_setup$nodenames == 'geoid'){ .[, geoid := USPS]} + { if(config$spatial_setup$subpop == 'subpop'){ .[, subpop := USPS]} } %>% - .[, as.list(quantile(get(statistics$sim_var), c(.05, .25, .5, .75, .95), na.rm = TRUE, names = FALSE)), by = c("date", config$spatial_setup$nodenames)] %>% + .[, as.list(quantile(get(statistics$sim_var), c(.05, .25, .5, .75, .95), na.rm = TRUE, names = FALSE)), by = c("date", config$spatial_setup$subpop)] %>% ggplot() + geom_ribbon(aes(x = date, ymin = V1, ymax = V5), alpha = 0.1) + geom_ribbon(aes(x = date, ymin = V2, ymax = V4), alpha = 0.1) + geom_line(aes(x = date, y = V3)) + geom_point(data = gt_data %>% .[, ..cols_data] %>% - { if(config$spatial_setup$nodenames == 'geoid'){ - .[geodata %>% .[, geoid := stringr::str_pad(geoid, width = 5, side = "left", pad = "0")], on = .(geoid)]} + { if(config$spatial_setup$subpop == 'subpop'){ + .[geodata %>% .[, subpop := stringr::str_pad(subpop, width = 5, side = "left", pad = "0")], on = .(subpop)]} } %>% - { if(config$spatial_setup$nodenames == 'geoid'){ .[, geoid := USPS]} + { if(config$spatial_setup$subpop == 'subpop'){ .[, subpop := USPS]} } , aes(lubridate::as_date(date), get(statistics$data_var)), color = 'firebrick', alpha = 0.1) + - facet_wrap(~get(config$spatial_setup$nodenames), scales = 'free', ncol = gg_cols) + + facet_wrap(~get(config$spatial_setup$subpop), scales = 'free', ncol = gg_cols) + labs(x = 'date', y = fit_stats[i], title = statistics$sim_var) + theme_classic() ) @@ -187,7 +187,7 @@ if("hosp" %in% model_outputs){ # print(outputs_global$hosp %>% # ggplot() + # geom_line(aes(lubridate::as_date(date), get(sim_var), group = as.factor(slot)), alpha = 0.1) + - # facet_wrap(~get(config$spatial_setup$nodenames), scales = 'free') + + # facet_wrap(~get(config$spatial_setup$subpop), scales = 'free') + # geom_point(data = gt_data %>% # .[, ..cols_data], # aes(lubridate::as_date(date), get(statistics$data_var)), color = 'firebrick', alpha = 0.1) + @@ -200,28 +200,28 @@ if("hosp" %in% model_outputs){ print(outputs_global$hosp %>% .[, ..cols_sim] %>% .[, date := lubridate::as_date(date)] %>% - { if(config$spatial_setup$nodenames == 'geoid'){ - .[geodata %>% .[, geoid := stringr::str_pad(geoid, width = 5, side = "left", pad = "0")], on = .(geoid)]} + { if(config$spatial_setup$subpop == 'subpop'){ + .[geodata %>% .[, subpop := stringr::str_pad(subpop, width = 5, side = "left", pad = "0")], on = .(subpop)]} } %>% - { if(config$spatial_setup$nodenames == 'geoid'){ .[, geoid := USPS]} + { if(config$spatial_setup$subpop == 'subpop'){ .[, subpop := USPS]} } %>% - .[, csum := cumsum(get(statistics$sim_var)), by = .(get(config$spatial_setup$nodenames), slot)] %>% - .[, as.list(quantile(csum, c(.05, .25, .5, .75, .95), na.rm = TRUE, names = FALSE)), by = c("date", config$spatial_setup$nodenames)] %>% + .[, csum := cumsum(get(statistics$sim_var)), by = .(get(config$spatial_setup$subpop), slot)] %>% + .[, as.list(quantile(csum, c(.05, .25, .5, .75, .95), na.rm = TRUE, names = FALSE)), by = c("date", config$spatial_setup$subpop)] %>% ggplot() + geom_ribbon(aes(x = date, ymin = V1, ymax = V5), alpha = 0.1) + geom_ribbon(aes(x = date, ymin = V2, ymax = V4), alpha = 0.1) + geom_line(aes(x = date, y = V3)) + geom_point(data = gt_data %>% .[, ..cols_data] %>% - { if(config$spatial_setup$nodenames == 'geoid'){ - .[geodata %>% .[, geoid := stringr::str_pad(geoid, width = 5, side = "left", pad = "0")], on = .(geoid)]} + { if(config$spatial_setup$subpop == 'subpop'){ + .[geodata %>% .[, subpop := stringr::str_pad(subpop, width = 5, side = "left", pad = "0")], on = .(subpop)]} } %>% - { if(config$spatial_setup$nodenames == 'geoid'){ .[, geoid := USPS]} + { if(config$spatial_setup$subpop == 'subpop'){ .[, subpop := USPS]} } %>% - .[, csum := cumsum(replace_na(get(statistics$data_var), 0)) , by = .(get(config$spatial_setup$nodenames))] + .[, csum := cumsum(replace_na(get(statistics$data_var), 0)) , by = .(get(config$spatial_setup$subpop))] , aes(lubridate::as_date(date), csum), color = 'firebrick', alpha = 0.1) + - facet_wrap(~get(config$spatial_setup$nodenames), scales = 'free', ncol = gg_cols) + + facet_wrap(~get(config$spatial_setup$subpop), scales = 'free', ncol = gg_cols) + labs(x = 'date', y = fit_stats[i], title = paste0("cumulative ", statistics$sim_var)) + theme_classic() ) @@ -238,31 +238,31 @@ if("hosp" %in% model_outputs){ for(i in 1:length(fit_stats)){ statistics <- purrr::flatten(config$inference$statistics[i]) - cols_sim <- c("date", statistics$sim_var, config$spatial_setup$nodenames,"slot") - cols_data <- c("date", config$spatial_setup$nodenames, statistics$data_var) + cols_sim <- c("date", statistics$sim_var, config$spatial_setup$subpop,"slot") + cols_data <- c("date", config$spatial_setup$subpop, statistics$data_var) if("llik" %in% model_outputs){ llik_rank <- copy(outputs_global$llik) %>% - .[, .SD[order(ll)], eval(config$spatial_setup$nodenames)] - high_low_llik <- rbindlist(list(data.table(llik_rank, key = eval(config$spatial_setup$nodenames)) %>% - .[, head(.SD,5), by = eval(config$spatial_setup$nodenames)] %>% + .[, .SD[order(ll)], eval(config$spatial_setup$subpop)] + high_low_llik <- rbindlist(list(data.table(llik_rank, key = eval(config$spatial_setup$subpop)) %>% + .[, head(.SD,5), by = eval(config$spatial_setup$subpop)] %>% .[, llik_bin := "top"], - data.table(llik_rank, key = eval(config$spatial_setup$nodenames)) %>% - .[, tail(.SD,5), by = eval(config$spatial_setup$nodenames)]%>% + data.table(llik_rank, key = eval(config$spatial_setup$subpop)) %>% + .[, tail(.SD,5), by = eval(config$spatial_setup$subpop)]%>% .[, llik_bin := "bottom"]) ) high_low_hosp_llik <- copy(outputs_global$hosp) %>% - .[high_low_llik, on = c("slot", eval(config$spatial_setup$nodenames))] + .[high_low_llik, on = c("slot", eval(config$spatial_setup$subpop))] - hosp_llik_plots <- lapply(unique(high_low_hosp_llik %>% .[, get(config$spatial_setup$nodenames)]), + hosp_llik_plots <- lapply(unique(high_low_hosp_llik %>% .[, get(config$spatial_setup$subpop)]), function(e){ high_low_hosp_llik %>% .[, date := lubridate::as_date(date)] %>% - { if(config$spatial_setup$nodenames == 'geoid'){ - .[geodata %>% .[, geoid := stringr::str_pad(geoid, width = 5, side = "left", pad = "0")], on = .(geoid)]} + { if(config$spatial_setup$subpop == 'subpop'){ + .[geodata %>% .[, subpop := stringr::str_pad(subpop, width = 5, side = "left", pad = "0")], on = .(subpop)]} } %>% - .[get(config$spatial_setup$nodenames) == e] %>% - { if(config$spatial_setup$nodenames == 'geoid'){ .[, geoid := USPS]} + .[get(config$spatial_setup$subpop) == e] %>% + { if(config$spatial_setup$subpop == 'subpop'){ .[, subpop := USPS]} } %>% ggplot() + geom_line(aes(lubridate::as_date(date), get(statistics$data_var), @@ -271,14 +271,14 @@ if("hosp" %in% model_outputs){ scale_color_viridis_c(option = "D", name = "log\nlikelihood") + geom_point(data = gt_data %>% .[, ..cols_data] %>% - { if(config$spatial_setup$nodenames == 'geoid'){ - .[geodata %>% .[, geoid := stringr::str_pad(geoid, width = 5, side = "left", pad = "0")], on = .(geoid)]} + { if(config$spatial_setup$subpop == 'subpop'){ + .[geodata %>% .[, subpop := stringr::str_pad(subpop, width = 5, side = "left", pad = "0")], on = .(subpop)]} } %>% - .[get(config$spatial_setup$nodenames) == e] %>% - { if(config$spatial_setup$nodenames == 'geoid'){ .[, geoid := USPS]} + .[get(config$spatial_setup$subpop) == e] %>% + { if(config$spatial_setup$subpop == 'subpop'){ .[, subpop := USPS]} } , aes(lubridate::as_date(date), get(statistics$data_var)), color = 'firebrick', alpha = 0.1) + - facet_wrap(~get(config$spatial_setup$nodenames), scales = 'free', ncol = gg_cols) + + facet_wrap(~get(config$spatial_setup$subpop), scales = 'free', ncol = gg_cols) + labs(x = 'date', y = fit_stats[i]) + #, title = paste0("top 5, bottom 5 lliks, ", statistics$sim_var)) + theme_classic() + guides(linetype = 'none') @@ -299,27 +299,27 @@ if("hosp" %in% model_outputs){ if("hnpi" %in% model_outputs){ gg_cols <- 4 - num_nodes <- length(unique(outputs_global$hosp %>% .[,get(config$spatial_setup$nodenames)])) + num_nodes <- length(unique(outputs_global$hosp %>% .[,get(config$spatial_setup$subpop)])) pdf_dims <- data.frame(width = gg_cols*3, length = num_nodes/gg_cols * 2) fname <- paste0("pplot/hnpi_mod_outputs_", opt$run_id,".pdf") pdf(fname, width = pdf_dims$width, height = pdf_dims$length) - hnpi_plots <- lapply(sort(unique(outputs_global$hnpi %>% .[, get(config$spatial_setup$nodenames)])), + hnpi_plots <- lapply(sort(unique(outputs_global$hnpi %>% .[, get(config$spatial_setup$subpop)])), function(i){ outputs_global$hnpi %>% - .[outputs_global$llik, on = c(config$spatial_setup$nodenames, "slot")] %>% - { if(config$spatial_setup$nodenames == 'geoid'){ - .[geodata %>% .[, geoid := stringr::str_pad(geoid, width = 5, side = "left", pad = "0")], on = .(geoid)]} + .[outputs_global$llik, on = c(config$spatial_setup$subpop, "slot")] %>% + { if(config$spatial_setup$subpop == 'subpop'){ + .[geodata %>% .[, subpop := stringr::str_pad(subpop, width = 5, side = "left", pad = "0")], on = .(subpop)]} } %>% - .[get(config$spatial_setup$nodenames) == i] %>% - { if(config$spatial_setup$nodenames == 'geoid'){ .[, geoid := USPS]} + .[get(config$spatial_setup$subpop) == i] %>% + { if(config$spatial_setup$subpop == 'subpop'){ .[, subpop := USPS]} } %>% ggplot(aes(npi_name,reduction)) + geom_violin() + geom_jitter(aes(group = npi_name, color = ll), size = 0.6, height = 0, width = 0.2, alpha = 1) + - facet_wrap(~get(config$spatial_setup$nodenames), scales = 'free') + + facet_wrap(~get(config$spatial_setup$subpop), scales = 'free') + scale_color_viridis_c(option = "B", name = "log\nlikelihood") + theme_classic() } @@ -358,10 +358,10 @@ if("seed" %in% model_outputs){ ## TO DO: MODIFIED FOR WHEN LOTS MORE SEEDING COM tmp_ <- paste("+", destination_columns, collapse = "") facet_formula <- paste("~", substr(tmp_, 2, nchar(tmp_))) - seed_plots <- lapply(sort(unique(setDT(geodata) %>% .[, get(config$spatial_setup$nodenames)])), + seed_plots <- lapply(sort(unique(setDT(geodata) %>% .[, get(config$spatial_setup$subpop)])), function(i){ outputs_global$seed %>% - .[place == i] %>% + .[subpop == i] %>% ggplot(aes(x = as.Date(date), y = amount)) + facet_wrap(as.formula(facet_formula), scales = 'free', ncol=1, labeller = label_wrap_gen(multi_line=FALSE)) + @@ -374,9 +374,9 @@ if("seed" %in% model_outputs){ ## TO DO: MODIFIED FOR WHEN LOTS MORE SEEDING COM print(do.call("grid.arrange", c(seed_plots, ncol=4))) # - # for(i in unique(outputs_global$seed$place)){ + # for(i in unique(outputs_global$seed$subpop)){ # print(outputs_global$seed %>% - # .[place == i] %>% + # .[subpop == i] %>% # ggplot(aes(x = as.Date(date), y = amount)) + # facet_wrap(as.formula(facet_formula), scales = 'free', ncol=1, # labeller = label_wrap_gen(multi_line=FALSE)) + @@ -400,24 +400,24 @@ if("seir" %in% model_outputs){ if("snpi" %in% model_outputs){ gg_cols <- 4 - num_nodes <- length(unique(outputs_global$hosp %>% .[,get(config$spatial_setup$nodenames)])) + num_nodes <- length(unique(outputs_global$hosp %>% .[,get(config$spatial_setup$subpop)])) pdf_dims <- data.frame(width = gg_cols*4, length = num_nodes/gg_cols * 3) fname <- paste0("pplot/snpi_mod_outputs_", opt$run_id,".pdf") pdf(fname, width = pdf_dims$width, height = pdf_dims$length) - node_names <- unique(sort(outputs_global$snpi %>% .[ , get(config$spatial_setup$nodenames)])) + node_names <- unique(sort(outputs_global$snpi %>% .[ , get(config$spatial_setup$subpop)])) node_names <- c(node_names[str_detect(node_names,",")], node_names[!str_detect(node_names,",")]) snpi_plots <- lapply(node_names, function(i){ if(!grepl(',', i)){ - i_lab <- ifelse(config$spatial_setup$nodenames == 'geoid', geodata[geoid == i, USPS], i) + i_lab <- ifelse(config$spatial_setup$subpop == 'subpop', geodata[subpop == i, USPS], i) outputs_global$snpi %>% - .[outputs_global$llik, on = c(config$spatial_setup$nodenames, "slot")] %>% - .[get(config$spatial_setup$nodenames) == i] %>% + .[outputs_global$llik, on = c(config$spatial_setup$subpop, "slot")] %>% + .[get(config$spatial_setup$subpop) == i] %>% ggplot(aes(npi_name,reduction)) + geom_violin() + geom_jitter(aes(group = npi_name, color = ll), size = 0.5, height = 0, width = 0.2, alpha = 0.5) + @@ -429,11 +429,11 @@ if("snpi" %in% model_outputs){ nodes_ <- unlist(strsplit(i,",")) ll_across_nodes <- outputs_global$llik %>% - .[get(config$spatial_setup$nodenames) %in% nodes_] %>% + .[get(config$spatial_setup$subpop) %in% nodes_] %>% .[, .(ll_sum = sum(ll)), by = .(slot)] outputs_global$snpi %>% - .[get(config$spatial_setup$nodenames) == i] %>% + .[get(config$spatial_setup$subpop) == i] %>% .[ll_across_nodes, on = c("slot")] %>% ggplot(aes(npi_name,reduction)) + geom_violin() + diff --git a/postprocessing/processing_diagnostics.R b/postprocessing/processing_diagnostics.R index 206912621..57ca3aa22 100644 --- a/postprocessing/processing_diagnostics.R +++ b/postprocessing/processing_diagnostics.R @@ -15,10 +15,10 @@ s3_name <- "idd-inference-runs" # PULL GEODATA ------------------------------------------------------------ -# Pull in geoid data +# Pull in subpop data geodata_states <- read.csv(paste0("./data/", config$spatial_setup$geodata)) %>% - mutate(geoid = stringr::str_pad(geoid, width = 5, side = "left", pad = "0")) + mutate(subpop = stringr::str_pad(subpop, width = 5, side = "left", pad = "0")) # PULL OUTCOMES FROM S3 --------------------------------------------------- @@ -97,7 +97,7 @@ import_s3_outcome <- function(scn_dir, outcome, global_opt, final_opt){ } if(outcome == "hosp"){ dat <- arrow::read_parquet(paste(subdir_, subdir_list[i], sep = "/")) %>% - select(date, geoid, incidI, incidC, incidH, incidD) + select(date, subpop, incidI, incidC, incidH, incidD) } if(any(grepl("csv", subdir_list))){ dat <- read.csv(paste(subdir_, subdir_list[i], sep = "/")) @@ -125,22 +125,22 @@ import_s3_outcome <- function(scn_dir, outcome, global_opt, final_opt){ work_dir <- paste0(getwd(), "/", scenario_dir) hnpi <- import_s3_outcome(work_dir, "hnpi", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") hosp <- import_s3_outcome(work_dir, "hosp", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") hpar <- import_s3_outcome(work_dir, "hpar", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") llik <- import_s3_outcome(work_dir, "llik", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") global_int_llik <- import_s3_outcome(work_dir, "llik", "global", "intermediate") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") chimeric_int_llik <- import_s3_outcome(work_dir, "llik", "chimeric", "intermediate") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") seed <- import_s3_outcome(work_dir, "seed", "global", "final") %>% - mutate(geoid = stringr::str_pad(place, width = 5, side = "left", pad = "0")) %>% - full_join(geodata_states, by = "geoid") + mutate(subpop = stringr::str_pad(subpop, width = 5, side = "left", pad = "0")) %>% + full_join(geodata_states, by = "subpop") snpi <- import_s3_outcome(work_dir, "snpi", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") spar <- import_s3_outcome(work_dir, "spar", "global", "final") # DERIVED OBJECTS --------------------------------------------------------- @@ -283,7 +283,7 @@ for(i in 1:length(USPS)){ filter_gt_data <- gt_data %>% filter(USPS == state) %>% - select(USPS, geoid, time, dplyr::contains("incid") & !dplyr::contains("_")) %>% + select(USPS, subpop, time, dplyr::contains("incid") & !dplyr::contains("_")) %>% pivot_longer(dplyr::contains('incid'), names_to = "outcome", values_to = "value") %>% rename(date = time) %>% mutate(week = lubridate::week(date)) %>% diff --git a/postprocessing/processing_diagnostics_AWS.R b/postprocessing/processing_diagnostics_AWS.R index ac8cea4fa..0eed22462 100644 --- a/postprocessing/processing_diagnostics_AWS.R +++ b/postprocessing/processing_diagnostics_AWS.R @@ -15,10 +15,10 @@ s3_name <- "idd-inference-runs" # PULL GEODATA ------------------------------------------------------------ -# Pull in geoid data +# Pull in subpop data geodata_states <- read.csv(paste0("./data/", config$spatial_setup$geodata)) %>% - mutate(geoid = stringr::str_pad(geoid, width = 5, side = "left", pad = "0")) + mutate(subpop = stringr::str_pad(subpop, width = 5, side = "left", pad = "0")) # PULL OUTCOMES FROM S3 --------------------------------------------------- @@ -97,7 +97,7 @@ import_s3_outcome <- function(scn_dir, outcome, global_opt, final_opt){ } if(outcome == "hosp"){ dat <- arrow::read_parquet(paste(subdir_, subdir_list[i], sep = "/")) %>% - select(date, geoid, incidI, incidC, incidH, incidD) + select(date, subpop, incidI, incidC, incidH, incidD) } if(any(grepl("csv", subdir_list))){ dat <- read.csv(paste(subdir_, subdir_list[i], sep = "/")) @@ -125,22 +125,22 @@ import_s3_outcome <- function(scn_dir, outcome, global_opt, final_opt){ work_dir <- paste0(getwd(), "/", scenario_dir) hnpi <- import_s3_outcome(work_dir, "hnpi", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") hosp <- import_s3_outcome(work_dir, "hosp", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") hpar <- import_s3_outcome(work_dir, "hpar", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") llik <- import_s3_outcome(work_dir, "llik", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") global_int_llik <- import_s3_outcome(work_dir, "llik", "global", "intermediate") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") chimeric_int_llik <- import_s3_outcome(work_dir, "llik", "chimeric", "intermediate") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") seed <- import_s3_outcome(work_dir, "seed", "global", "final") %>% - mutate(geoid = stringr::str_pad(place, width = 5, side = "left", pad = "0")) %>% - full_join(geodata_states, by = "geoid") + mutate(subpop = stringr::str_pad(subpop, width = 5, side = "left", pad = "0")) %>% + full_join(geodata_states, by = "subpop") snpi <- import_s3_outcome(work_dir, "snpi", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") spar <- import_s3_outcome(work_dir, "spar", "global", "final") # DERIVED OBJECTS --------------------------------------------------------- @@ -282,7 +282,7 @@ for(i in 1:length(USPS)){ filter_gt_data <- gt_data %>% filter(USPS == state) %>% - select(USPS, geoid, time, dplyr::contains("incid") & !dplyr::contains("_")) %>% + select(USPS, subpop, time, dplyr::contains("incid") & !dplyr::contains("_")) %>% pivot_longer(dplyr::contains('incid'), names_to = "outcome", values_to = "value") %>% rename(date = time) %>% mutate(week = lubridate::week(date)) %>% diff --git a/postprocessing/processing_diagnostics_SLURM.R b/postprocessing/processing_diagnostics_SLURM.R index 0ab1833b8..505e51d57 100644 --- a/postprocessing/processing_diagnostics_SLURM.R +++ b/postprocessing/processing_diagnostics_SLURM.R @@ -11,10 +11,10 @@ library(lubridate) # PULL GEODATA ------------------------------------------------------------ -# Pull in geoid data +# Pull in subpop data geodata_states <- read.csv(paste0("./data/", config$spatial_setup$geodata)) %>% - mutate(geoid = stringr::str_pad(geoid, width = 5, side = "left", pad = "0")) + mutate(subpop = stringr::str_pad(subpop, width = 5, side = "left", pad = "0")) # FUNCTIONS --------------------------------------------------------------- @@ -43,7 +43,7 @@ import_s3_outcome <- function(scn_dir, outcome, global_opt, final_opt){ } if(outcome == "hosp"){ dat <- arrow::read_parquet(paste(subdir_, subdir_list[i], sep = "/")) %>% - select(date, geoid, incidI, incidC, incidH, incidD) + select(date, subpop, incidI, incidC, incidH, incidD) } if(any(grepl("csv", subdir_list))){ dat <- read.csv(paste(subdir_, subdir_list[i], sep = "/")) @@ -73,22 +73,22 @@ outcomes_list <- scenario_dir <- file.path(scenario_dir, config$model_output_dirname) hnpi <- import_s3_outcome(scenario_dir, "hnpi", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") hosp <- import_s3_outcome(scenario_dir, "hosp", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") hpar <- import_s3_outcome(scenario_dir, "hpar", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") llik <- import_s3_outcome(scenario_dir, "llik", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") global_int_llik <- import_s3_outcome(scenario_dir, "llik", "global", "intermediate") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") chimeric_int_llik <- import_s3_outcome(scenario_dir, "llik", "chimeric", "intermediate") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") seed <- import_s3_outcome(scenario_dir, "seed", "global", "final") %>% - mutate(geoid = stringr::str_pad(place, width = 5, side = "left", pad = "0")) %>% - full_join(geodata_states, by = "geoid") + mutate(subpop = stringr::str_pad(subpop, width = 5, side = "left", pad = "0")) %>% + full_join(geodata_states, by = "subpop") snpi <- import_s3_outcome(scenario_dir, "snpi", "global", "final") %>% - full_join(geodata_states, by = "geoid") + full_join(geodata_states, by = "subpop") spar <- import_s3_outcome(scenario_dir, "spar", "global", "final") # DERIVED OBJECTS --------------------------------------------------------- @@ -231,7 +231,7 @@ for(i in 1:length(USPS)){ filter_gt_data <- gt_data %>% filter(USPS == state) %>% - select(USPS, geoid, time, dplyr::contains("incid") & !dplyr::contains("_")) %>% + select(USPS, subpop, time, dplyr::contains("incid") & !dplyr::contains("_")) %>% pivot_longer(dplyr::contains('incid'), names_to = "outcome", values_to = "value") %>% rename(date = time) %>% mutate(week = lubridate::week(date)) %>% diff --git a/postprocessing/run_sim_processing_FluSightExample.R b/postprocessing/run_sim_processing_FluSightExample.R index 0353bc87a..cff430101 100644 --- a/postprocessing/run_sim_processing_FluSightExample.R +++ b/postprocessing/run_sim_processing_FluSightExample.R @@ -451,11 +451,11 @@ if (!full_fit & smh_or_fch == "smh" & save_reps){ file_samp <- lapply(file_names, arrow::read_parquet) file_samp <- data.table::rbindlist(file_samp) %>% as_tibble() %>% - left_join(geodata %>% select(location = USPS, geoid) %>% add_row(location="US", geoid="US")) %>% + left_join(geodata %>% select(location = USPS, subpop) %>% add_row(location="US", subpop="US")) %>% select(-location) %>% mutate(sample = as.integer(sample), - location = stringr::str_pad(substr(geoid, 1, 2), width=2, side="right", pad = "0")) %>% - select(-geoid) %>% + location = stringr::str_pad(substr(subpop, 1, 2), width=2, side="right", pad = "0")) %>% + select(-subpop) %>% arrange(scenario_id, target_end_date, target, location, age_group) file_samp_nums <- file_samp %>% diff --git a/postprocessing/run_sim_processing_SLURM.R b/postprocessing/run_sim_processing_SLURM.R index 98d018eb9..f38e368e9 100644 --- a/postprocessing/run_sim_processing_SLURM.R +++ b/postprocessing/run_sim_processing_SLURM.R @@ -461,11 +461,11 @@ if (!full_fit & smh_or_fch == "smh" & save_reps){ file_samp <- lapply(file_names, arrow::read_parquet) file_samp <- data.table::rbindlist(file_samp) %>% as_tibble() %>% - left_join(geodata %>% select(location = USPS, geoid) %>% add_row(location="US", geoid="US")) %>% + left_join(geodata %>% select(location = USPS, subpop) %>% add_row(location="US", subpop="US")) %>% select(-location) %>% mutate(sample = as.integer(sample), - location = stringr::str_pad(substr(geoid, 1, 2), width=2, side="right", pad = "0")) %>% - select(-geoid) %>% + location = stringr::str_pad(substr(subpop, 1, 2), width=2, side="right", pad = "0")) %>% + select(-subpop) %>% arrange(scenario_id, target_end_date, target, location, age_group) file_samp_nums <- file_samp %>% diff --git a/postprocessing/run_sim_processing_TEMPLATE.R b/postprocessing/run_sim_processing_TEMPLATE.R index 2bdd444e5..e8f37fdb5 100644 --- a/postprocessing/run_sim_processing_TEMPLATE.R +++ b/postprocessing/run_sim_processing_TEMPLATE.R @@ -451,11 +451,11 @@ if (!full_fit & smh_or_fch == "smh" & save_reps){ file_samp <- lapply(file_names, arrow::read_parquet) file_samp <- data.table::rbindlist(file_samp) %>% as_tibble() %>% - left_join(geodata %>% select(location = USPS, geoid) %>% add_row(location="US", geoid="US")) %>% + left_join(geodata %>% select(location = USPS, subpop) %>% add_row(location="US", subpop="US")) %>% select(-location) %>% mutate(sample = as.integer(sample), - location = stringr::str_pad(substr(geoid, 1, 2), width=2, side="right", pad = "0")) %>% - select(-geoid) %>% + location = stringr::str_pad(substr(subpop, 1, 2), width=2, side="right", pad = "0")) %>% + select(-subpop) %>% arrange(scenario_id, target_end_date, target, location, age_group) file_samp_nums <- file_samp %>% diff --git a/postprocessing/sim_processing_source.R b/postprocessing/sim_processing_source.R index 2d9179ef4..f30fff147 100644 --- a/postprocessing/sim_processing_source.R +++ b/postprocessing/sim_processing_source.R @@ -31,68 +31,68 @@ combine_and_format_sims <- function(outcome_vars = "incid", geodata, death_filter = opt$death_filter) { - res_geoid_all <- arrow::open_dataset(sprintf("%shosp",scenario_dir), + res_subpop_all <- arrow::open_dataset(sprintf("%shosp",scenario_dir), partitioning = c("location", "npi_scenario", "outcome_scenario", "config", "lik_type", "is_final")) %>% - select(time, geoid, outcome_scenario, starts_with(outcome_vars)) %>% + select(time, subpop, outcome_scenario, starts_with(outcome_vars)) %>% filter(time>=forecast_date & time<=end_date) %>% collect() %>% filter(stringr::str_detect(outcome_scenario, death_filter)) %>% mutate(time=as.Date(time)) %>% - group_by(time, geoid, outcome_scenario) %>% - dplyr::mutate(sim_num = as.character(seq_along(geoid))) %>% + group_by(time, subpop, outcome_scenario) %>% + dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>% ungroup() if (quick_run){ - res_geoid_all <- res_geoid_all %>% filter(sim_num %in% 1:20) + res_subpop_all <- res_subpop_all %>% filter(sim_num %in% 1:20) } gc() # ~ Subset if testing if (testing){ - res_geoid_all <- res_geoid_all %>% filter(sim_num %in% sample(.$sim_num, 10, replace = FALSE)) + res_subpop_all <- res_subpop_all %>% filter(sim_num %in% sample(.$sim_num, 10, replace = FALSE)) } # pull out just the total outcomes of interest cols_aggr <- expand_grid(a="incid",b=outcomes_) %>% mutate(d=paste0(a,b)) %>% pull(d) - cols_aggr <- cols_aggr[cols_aggr %in% colnames(res_geoid_all)] + cols_aggr <- cols_aggr[cols_aggr %in% colnames(res_subpop_all)] if(!keep_all_compartments & !keep_variant_compartments & !keep_vacc_compartments){ - res_geoid_all <- res_geoid_all %>% - select(time, geoid, outcome_scenario, sim_num, all_of(cols_aggr)) + res_subpop_all <- res_subpop_all %>% + select(time, subpop, outcome_scenario, sim_num, all_of(cols_aggr)) } else if (keep_variant_compartments){ # pull out just the variant outcomes cols_vars <- expand_grid(a="incid",b=outcomes_, c=paste0("_", variants_)) %>% mutate(d=paste0(a,b,c)) %>% pull(d) - cols_vars <- cols_vars[cols_vars %in% colnames(res_geoid_all)] - res_geoid_all <- res_geoid_all %>% - select(time, geoid, outcome_scenario, sim_num, all_of(cols_vars)) + cols_vars <- cols_vars[cols_vars %in% colnames(res_subpop_all)] + res_subpop_all <- res_subpop_all %>% + select(time, subpop, outcome_scenario, sim_num, all_of(cols_vars)) } else if (keep_all_compartments){ # remove the aggregate outcomes - res_geoid_all <- res_geoid_all %>% + res_subpop_all <- res_subpop_all %>% select(-all_of(cols_vars), -all_of(cols_aggr)) } else if (keep_vacc_compartments){ # pull out just the variant outcomes cols_vars <- expand_grid(a="incid",b=outcomes_, c=paste0("_", vacc_)) %>% mutate(d=paste0(a,b,c)) %>% pull(d) - cols_vars <- cols_vars[cols_vars %in% colnames(res_geoid_all)] - res_geoid_all <- res_geoid_all %>% - select(time, geoid, outcome_scenario, sim_num, all_of(cols_vars)) + cols_vars <- cols_vars[cols_vars %in% colnames(res_subpop_all)] + res_subpop_all <- res_subpop_all %>% + select(time, subpop, outcome_scenario, sim_num, all_of(cols_vars)) } # Merge in Geodata if(county_level){ - res_state <- res_geoid_all %>% - inner_join(geodata %>% select(geoid, USPS)) %>% + res_state <- res_subpop_all %>% + inner_join(geodata %>% select(subpop, USPS)) %>% group_by_at(c("USPS", "time", "sim_num", compartment_types)) %>% summarise(across(starts_with("incid"), sum)) %>% as_tibble() } else { - res_state <- res_geoid_all %>% - inner_join(geodata %>% select(geoid, USPS)) + res_state <- res_subpop_all %>% + inner_join(geodata %>% select(subpop, USPS)) } - rm(res_geoid_all) + rm(res_subpop_all) # ~ Add US totals res_us <- res_state %>% @@ -120,44 +120,44 @@ load_simulations <- function(geodata, keep_compartments = TRUE, testing = FALSE){ - res_geoid <- arrow::open_dataset(sprintf("%s/hosp", sim_directory), + res_subpop <- arrow::open_dataset(sprintf("%s/hosp", sim_directory), partitioning =c("location", "npi_scenario", "outcome_scenario", "config", "lik_type", "is_final")) %>% - select(time, geoid, starts_with("incid"), outcome_scenario)%>% + select(time, subpop, starts_with("incid"), outcome_scenario)%>% filter(time>=forecast_date & time<=end_date)%>% collect() %>% filter(stringr::str_detect(outcome_scenario, death_filter))%>% mutate(time=as.Date(time)) %>% - group_by(time, geoid, outcome_scenario) %>% - dplyr::mutate(sim_num = as.character(seq_along(geoid))) %>% + group_by(time, subpop, outcome_scenario) %>% + dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>% ungroup() %>% pivot_longer(cols=starts_with("incid"), names_to = c("outcome",compartment_types), names_pattern = "(.*)_(.*)_(.*)_(.*)", values_to = "value") %>% filter(!is.na(outcome)) - res_geoid <- res_geoid %>% + res_subpop <- res_subpop %>% pivot_wider(names_from = outcome, values_from = value) # Subset for testing if(testing){ - res_geoid <- res_geoid %>% filter(sim_num %in% 1:10) - res_geoid_long <- res_geoid_long %>% filter(sim_num %in% 1:10) + res_subpop <- res_subpop %>% filter(sim_num %in% 1:10) + res_subpop_long <- res_subpop_long %>% filter(sim_num %in% 1:10) } - # res_geoid <- res_geoid %>% - # group_by(time, geoid, outcome_scenario, variant, vacc, agestrat, sim_num)%>% + # res_subpop <- res_subpop %>% + # group_by(time, subpop, outcome_scenario, variant, vacc, agestrat, sim_num)%>% # #summarise(across(starts_with("incid"), sum)) %>% # summarise(incidD=sum(incidD), incidH=sum(incidH), incidC=sum(incidC))%>% # as_tibble() if(county_level){ - res_state <- res_geoid %>% - inner_join(geodata %>% select(geoid, USPS)) %>% + res_state <- res_subpop %>% + inner_join(geodata %>% select(subpop, USPS)) %>% group_by_at(c("USPS", "time", "sim_num", compartment_types)) %>% # summarize(incidI=sum(incidI), # incidD=sum(incidD), @@ -166,14 +166,14 @@ load_simulations <- function(geodata, summarise(across(starts_with("incid"), sum)) %>% as_tibble() } else { - res_state <- res_geoid %>% - inner_join(geodata %>% select(geoid, USPS)) + res_state <- res_subpop %>% + inner_join(geodata %>% select(subpop, USPS)) if (keep_compartments){ - res_state_long <- res_geoid_long %>% - inner_join(geodata %>% select(geoid, USPS)) + res_state_long <- res_subpop_long %>% + inner_join(geodata %>% select(subpop, USPS)) } - rm(res_geoid_long, res_geoid) + rm(res_subpop_long, res_subpop) } # ADD US TOTAL @@ -223,25 +223,25 @@ trans_sims_wide <- function(geodata, keep_compartments = TRUE, testing = FALSE){ - res_geoid_long <- res_geoid - res_geoid <- res_geoid %>% + res_subpop_long <- res_subpop + res_subpop <- res_subpop %>% pivot_wider(names_from = outcome, values_from = value) # Subset for testing if(testing){ - res_geoid <- res_geoid %>% filter(sim_num %in% 1:10) - res_geoid_long <- res_geoid_long %>% filter(sim_num %in% 1:10) + res_subpop <- res_subpop %>% filter(sim_num %in% 1:10) + res_subpop_long <- res_subpop_long %>% filter(sim_num %in% 1:10) } - # res_geoid <- res_geoid %>% - # group_by(time, geoid, outcome_scenario, variant, vacc, agestrat, sim_num)%>% + # res_subpop <- res_subpop %>% + # group_by(time, subpop, outcome_scenario, variant, vacc, agestrat, sim_num)%>% # #summarise(across(starts_with("incid"), sum)) %>% # summarise(incidD=sum(incidD), incidH=sum(incidH), incidC=sum(incidC))%>% # as_tibble() if(county_level){ - res_state <- res_geoid %>% - inner_join(geodata %>% select(geoid, USPS)) %>% + res_state <- res_subpop %>% + inner_join(geodata %>% select(subpop, USPS)) %>% group_by_at(c("USPS", "time", "sim_num", compartment_types)) %>% # summarize(incidI=sum(incidI), # incidD=sum(incidD), @@ -250,14 +250,14 @@ trans_sims_wide <- function(geodata, summarise(across(starts_with("incid"), sum)) %>% as_tibble() } else { - res_state <- res_geoid %>% - inner_join(geodata %>% select(geoid, USPS)) + res_state <- res_subpop %>% + inner_join(geodata %>% select(subpop, USPS)) if (keep_compartments){ - res_state_long <- res_geoid_long %>% - inner_join(geodata %>% select(geoid, USPS)) + res_state_long <- res_subpop_long %>% + inner_join(geodata %>% select(subpop, USPS)) } - rm(res_geoid_long, res_geoid) + rm(res_subpop_long, res_subpop) } # ADD US TOTAL @@ -302,45 +302,45 @@ load_simulations_orig <- function(geodata, keep_compartments = TRUE, testing = FALSE){ - res_geoid <- arrow::open_dataset(sprintf("%s/hosp", sim_directory), + res_subpop <- arrow::open_dataset(sprintf("%s/hosp", sim_directory), partitioning =c("location", "npi_scenario", "outcome_scenario", "config", "lik_type", "is_final")) %>% - select(time, geoid, starts_with("incid"), outcome_scenario)%>% + select(time, subpop, starts_with("incid"), outcome_scenario)%>% filter(time>=forecast_date & time<=end_date)%>% collect() %>% filter(stringr::str_detect(outcome_scenario, death_filter))%>% mutate(time=as.Date(time)) %>% - group_by(time, geoid, outcome_scenario) %>% - dplyr::mutate(sim_num = as.character(seq_along(geoid))) %>% + group_by(time, subpop, outcome_scenario) %>% + dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>% ungroup() %>% pivot_longer(cols=starts_with("incid"), names_to = c("outcome",compartment_types), names_pattern = "(.*)_(.*)_(.*)_(.*)", values_to = "value") %>% filter(!is.na(outcome)) - res_geoid_long <- res_geoid - res_geoid <- res_geoid %>% + res_subpop_long <- res_subpop + res_subpop <- res_subpop %>% pivot_wider(names_from = outcome, values_from = value) # Subset for testing if(testing){ - res_geoid <- res_geoid %>% filter(sim_num %in% 1:10) - res_geoid_long <- res_geoid_long %>% filter(sim_num %in% 1:10) + res_subpop <- res_subpop %>% filter(sim_num %in% 1:10) + res_subpop_long <- res_subpop_long %>% filter(sim_num %in% 1:10) } - # res_geoid <- res_geoid %>% - # group_by(time, geoid, outcome_scenario, variant, vacc, agestrat, sim_num)%>% + # res_subpop <- res_subpop %>% + # group_by(time, subpop, outcome_scenario, variant, vacc, agestrat, sim_num)%>% # #summarise(across(starts_with("incid"), sum)) %>% # summarise(incidD=sum(incidD), incidH=sum(incidH), incidC=sum(incidC))%>% # as_tibble() if(county_level){ - res_state <- res_geoid %>% - inner_join(geodata %>% select(geoid, USPS)) %>% + res_state <- res_subpop %>% + inner_join(geodata %>% select(subpop, USPS)) %>% group_by_at(c("USPS", "time", "sim_num", compartment_types)) %>% # summarize(incidI=sum(incidI), # incidD=sum(incidD), @@ -349,14 +349,14 @@ load_simulations_orig <- function(geodata, summarise(across(starts_with("incid"), sum)) %>% as_tibble() } else { - res_state <- res_geoid %>% - inner_join(geodata %>% select(geoid, USPS)) + res_state <- res_subpop %>% + inner_join(geodata %>% select(subpop, USPS)) if (keep_compartments){ - res_state_long <- res_geoid_long %>% - inner_join(geodata %>% select(geoid, USPS)) + res_state_long <- res_subpop_long %>% + inner_join(geodata %>% select(subpop, USPS)) } - rm(res_geoid_long, res_geoid) + rm(res_subpop_long, res_subpop) } # ADD US TOTAL @@ -437,7 +437,7 @@ get_ground_truth_revised <- function(config, scenario_dir, flepi_path = "../flep rename(time=date, USPS=source) gt_data_clean <- gt_data %>% - rename(geoid=FIPS, time=date, USPS=source) + rename(subpop=FIPS, time=date, USPS=source) write_csv(gt_data_clean, file.path(scenario_dir, "gt_data_clean.csv")) file.remove(config$inference$gt_data_path) @@ -472,10 +472,10 @@ calibrate_outcome <- function(outcome_calib = "incidH", # get gt to calibrate to if (weekly_outcome){ - gt_calib <- get_weekly_incid(gt_data %>% dplyr::select(time, geoid, USPS, !!sym(outcome_calib)) %>% mutate(sim_num = 0), + gt_calib <- get_weekly_incid(gt_data %>% dplyr::select(time, subpop, USPS, !!sym(outcome_calib)) %>% mutate(sim_num = 0), outcomes = outcome_calib_base) } else { - gt_calib <- get_daily_incid(gt_data %>% dplyr::select(time, geoid, USPS, !!sym(outcome_calib)) %>% mutate(sim_num = 0), + gt_calib <- get_daily_incid(gt_data %>% dplyr::select(time, subpop, USPS, !!sym(outcome_calib)) %>% mutate(sim_num = 0), outcomes = outcome_calib_base) } @@ -493,7 +493,7 @@ calibrate_outcome <- function(outcome_calib = "incidH", inc_calib <- incid_sims_formatted %>% filter(outcome %in% outcome_calib) }else{ # repull data with one week earlier to calibrate to if not full run - res_geoid_all_calib <- combine_and_format_sims( + res_subpop_all_calib <- combine_and_format_sims( outcome_vars = outcome_calib, scenario_dir = scenario_dir, quick_run = quick_run, @@ -511,10 +511,10 @@ calibrate_outcome <- function(outcome_calib = "incidH", death_filter = death_filter) if (weekly_outcome) { - inc_calib <- get_weekly_incid(res_geoid_all_calib, outcomes = outcome_calib_base) + inc_calib <- get_weekly_incid(res_subpop_all_calib, outcomes = outcome_calib_base) inc_calib <- format_weekly_outcomes(inc_calib, point_est = 0.5, opt) } else { - inc_calib <- get_daily_incid(res_geoid_all_calib, outcomes = outcome_calib_base) + inc_calib <- get_daily_incid(res_subpop_all_calib, outcomes = outcome_calib_base) inc_calib <- format_daily_outcomes(inc_calib, point_est = 0.5, opt) } } @@ -1139,7 +1139,7 @@ process_sims <- function( # Load Data --------------------------------------------------------------- # ~ Geodata - geodata <- suppressMessages(readr::read_csv(opt$geodata, col_types = readr::cols(geoid=readr::col_character()))) + geodata <- suppressMessages(readr::read_csv(opt$geodata, col_types = readr::cols(subpop=readr::col_character()))) # ~ Ground truth if (!exists("gt_data")){ @@ -1197,7 +1197,7 @@ process_sims <- function( "config", "lik_type", "is_final")) %>% - select(filename, geoid, npi_scenario, outcome_scenario, ll)%>% + select(filename, subpop, npi_scenario, outcome_scenario, ll)%>% collect() %>% distinct() %>% filter(stringr::str_detect(outcome_scenario, opt$death_filter))%>% @@ -1206,22 +1206,22 @@ process_sims <- function( as_tibble() - res_llik %>% filter(geoid=='06000') %>% + res_llik %>% filter(subpop=='06000') %>% ggplot(aes(x=sim_id, y=ll)) + geom_point() - res_llik %>% filter(geoid=='06000') %>% + res_llik %>% filter(subpop=='06000') %>% ggplot(aes(y=ll)) + geom_histogram() - res_llik %>% filter(geoid=='06000') %>% + res_llik %>% filter(subpop=='06000') %>% mutate(lik = log(-ll)) %>% ggplot(aes(y=lik)) + geom_histogram() res_lik_ests <- res_llik %>% mutate(lik = log(-ll)) %>% - group_by(geoid) %>% + group_by(subpop) %>% mutate(mean_ll = mean(ll), median_ll = median(ll), low_ll = quantile(ll, 0.025), @@ -1237,14 +1237,14 @@ process_sims <- function( n_excl <- ceiling(nrow(sim_ids)*(1-likelihood_prune_percentkeep)) res_lik_ests <- res_lik_ests %>% - group_by(geoid, npi_scenario, outcome_scenario) %>% + group_by(subpop, npi_scenario, outcome_scenario) %>% arrange(ll) %>% - mutate(rank = seq_along(geoid), + mutate(rank = seq_along(subpop), excl_rank = rank<=n_excl) %>% ungroup() # res_lik_ests %>% - # group_by(geoid) %>% + # group_by(subpop) %>% # summarise(n_excl_ll = sum(below025_ll), # n_excl_lik = sum(below025_lik)) %>% View # res_lik_ests %>% @@ -1253,7 +1253,7 @@ process_sims <- function( # n_excl_lik = sum(below025_lik)) %>% View res_lik_excl <- res_lik_ests %>% - select(geoid, sim_id, exclude=excl_rank, ll, lik) + select(subpop, sim_id, exclude=excl_rank, ll, lik) res_state <- res_state %>% left_join(res_lik_excl) #%>% select(-outcome_scenario) @@ -1263,8 +1263,8 @@ process_sims <- function( res_state <- res_state %>% filter(!exclude) %>% select(-sim_id, -exclude) %>% - group_by(time, geoid, USPS, outcome_scenario) %>% - dplyr::mutate(sim_num = as.character(seq_along(geoid))) %>% + group_by(time, subpop, USPS, outcome_scenario) %>% + dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>% ungroup() } @@ -1338,7 +1338,7 @@ process_sims <- function( # outcomes_cum_gt_ <- outcomes_cum_[outcomes_!="I"] # # gt_data_2 <- gt_data_2 %>% - # select(USPS, geoid, time, paste0("incid", outcomes_gt_), paste0("cum", outcomes_[outcomes_cum_gt_])) + # select(USPS, subpop, time, paste0("incid", outcomes_gt_), paste0("cum", outcomes_[outcomes_cum_gt_])) # ~ Weekly Outcomes ----------------------------------------------------------- diff --git a/preprocessing/seir_init_immuneladder_r17phase3.R b/preprocessing/seir_init_immuneladder_r17phase3.R index bcdd76c9d..857c88882 100644 --- a/preprocessing/seir_init_immuneladder_r17phase3.R +++ b/preprocessing/seir_init_immuneladder_r17phase3.R @@ -15,7 +15,6 @@ # spatial_setup: # geodata: -# nodenames: # # seeding: # lambda_file: @@ -24,7 +23,7 @@ # # ## Input Data # -# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::nodenames} that denotes the geoids +# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::subpop} that denotes the subpop # # ## Output Data # @@ -301,11 +300,11 @@ seir_dat_static <- seir_dat_static %>% filter(mc_value_type == "prevalence") %>% mutate(mc_vaccination_stage = ifelse(mc_vaccination_stage == "3dose", "vaccinated", "unvaccinated")) %>% mutate(mc_variant_type = "ALL") %>% - pivot_longer(cols = -c(starts_with("mc_"), date), names_to = "geoid", values_to = "value") %>% + pivot_longer(cols = -c(starts_with("mc_"), date), names_to = "subpop", values_to = "value") %>% group_by(across(c(-value))) %>% summarise(value = sum(value, na.rm = TRUE)) %>% mutate(mc_name = paste(mc_infection_stage, mc_vaccination_stage, mc_variant_type, mc_age_strata, sep = "_")) %>% - pivot_wider(names_from = geoid, values_from = value) %>% + pivot_wider(names_from = subpop, values_from = value) %>% dplyr::select(all_of(seir_dat_cols)) @@ -341,7 +340,7 @@ if (gradual_waning){ group_by(prob_immune, date, mc_value_type, mc_variant_type, mc_vaccination_stage, mc_age_strata, loc) %>% summarise(n = sum(n, na.rm=TRUE)) %>% as_tibble() %>% - left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% + left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% mutate(prop = n / pop_agestrata) %>% group_by(USPS, loc, date, mc_value_type, mc_variant_type, mc_vaccination_stage, mc_age_strata) %>% summarise(prop_immune = sum((n * prob_immune) / sum(n, na.rm = TRUE), na.rm = TRUE)) %>% @@ -400,7 +399,7 @@ seir_dat_changing <- seir_dat_changing %>% # geodata <- read_csv("data/geodata_2019_statelevel_agestrat.csv") # # seir_dat_changing %>% filter(mc_age_strata == "age18to64") %>% -# left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% +# left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% # mutate(prop = n / pop_agestrata) %>% # ggplot(aes(x = prob_immune_nom, y = prop, color = USPS)) + # geom_point() + @@ -412,7 +411,7 @@ seir_dat_changing <- seir_dat_changing %>% # theme(legend.position = "none", axis.text.x = element_text(angle = 90)) # # seir_dat_changing %>% -# left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% +# left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% # mutate(prop = n / pop_agestrata) %>% # group_by(USPS, loc, date, mc_value_type, mc_vaccination_stage, mc_age_strata) %>% # summarise(prop_immune = sum((n * prob_immune_nom) / sum(n, na.rm = TRUE), na.rm = TRUE)) %>% @@ -454,8 +453,8 @@ seir_dat_changing_final <- seir_dat_changing %>% # # CHECK # seir_dat_changing_final %>% # filter(mc_age_strata == "age18to64") %>% -# pivot_longer(cols=-c(mc_value_type:mc_name, date), names_to = "geoid", values_to = "n") %>% -# left_join(geodata %>% rename(geoid = geoid) %>% select(USPS, geoid, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% +# pivot_longer(cols=-c(mc_value_type:mc_name, date), names_to = "subpop", values_to = "n") %>% +# left_join(geodata %>% rename(subpop = subpop) %>% select(USPS, subpop, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% # mutate(prop = n / pop_agestrata) %>% # ggplot(aes(x = mc_infection_stage, y = n, color = USPS)) + # geom_point() + diff --git a/preprocessing/seir_init_immuneladder_r17phase3_preOm.R b/preprocessing/seir_init_immuneladder_r17phase3_preOm.R index ee0d26e0f..fac8e5770 100644 --- a/preprocessing/seir_init_immuneladder_r17phase3_preOm.R +++ b/preprocessing/seir_init_immuneladder_r17phase3_preOm.R @@ -15,7 +15,7 @@ # spatial_setup: # geodata: -# nodenames: +# subpop: # # seeding: # lambda_file: @@ -24,7 +24,7 @@ # # ## Input Data # -# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::nodenames} that denotes the geoids +# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::subpop} that denotes the subpop # # ## Output Data # @@ -302,11 +302,11 @@ seir_dat_static <- seir_dat_static %>% filter(mc_value_type == "prevalence") %>% mutate(mc_vaccination_stage = ifelse(mc_vaccination_stage == "3dose", "vaccinated", "unvaccinated")) %>% mutate(mc_variant_type = "ALL") %>% - pivot_longer(cols = -c(starts_with("mc_"), date), names_to = "geoid", values_to = "value") %>% + pivot_longer(cols = -c(starts_with("mc_"), date), names_to = "subpop", values_to = "value") %>% group_by(across(c(-value))) %>% summarise(value = sum(value, na.rm = TRUE)) %>% mutate(mc_name = paste(mc_infection_stage, mc_vaccination_stage, mc_variant_type, mc_age_strata, sep = "_")) %>% - pivot_wider(names_from = geoid, values_from = value) %>% + pivot_wider(names_from = subpop, values_from = value) %>% dplyr::select(all_of(seir_dat_cols)) @@ -342,7 +342,7 @@ if (gradual_waning){ group_by(prob_immune, date, mc_value_type, mc_variant_type, mc_vaccination_stage, mc_age_strata, loc) %>% summarise(n = sum(n, na.rm=TRUE)) %>% as_tibble() %>% - left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% + left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% mutate(prop = n / pop_agestrata) %>% group_by(USPS, loc, date, mc_value_type, mc_variant_type, mc_vaccination_stage, mc_age_strata) %>% summarise(prop_immune = sum((n * prob_immune) / sum(n, na.rm = TRUE), na.rm = TRUE)) %>% @@ -401,7 +401,7 @@ library(ggplot2) geodata <- read_csv("data/geodata_2019_statelevel_agestrat.csv") seir_dat_changing %>% filter(mc_age_strata == "age18to64") %>% - left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% + left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% mutate(prop = n / pop_agestrata) %>% # group_by(date, mc_age_strata, USPS) %>% # summarise(prop_imm @@ -415,7 +415,7 @@ seir_dat_changing %>% filter(mc_age_strata == "age18to64") %>% theme(legend.position = "none", axis.text.x = element_text(angle = 90)) seir_dat_changing %>% - left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% + left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% mutate(prop = n / pop_agestrata) %>% group_by(USPS, date, mc_value_type, mc_vaccination_stage, mc_age_strata) %>% summarise(prop_immune = sum(n * prob_immune_nom, na.rm = TRUE) / sum(n, na.rm = TRUE)) %>% @@ -457,8 +457,8 @@ seir_dat_changing_final <- seir_dat_changing %>% # # CHECK # seir_dat_changing_final %>% # filter(mc_age_strata == "age18to64") %>% -# pivot_longer(cols=-c(mc_value_type:mc_name, date), names_to = "geoid", values_to = "n") %>% -# left_join(geodata %>% rename(geoid = geoid) %>% select(USPS, geoid, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% +# pivot_longer(cols=-c(mc_value_type:mc_name, date), names_to = "subpop", values_to = "n") %>% +# left_join(geodata %>% rename(subpop = subpop) %>% select(USPS, subpop, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% # mutate(prop = n / pop_agestrata) %>% # ggplot(aes(x = mc_infection_stage, y = n, color = USPS)) + # geom_point() + diff --git a/preprocessing/seir_init_immuneladder_r17phase3_preOm_noDelta.R b/preprocessing/seir_init_immuneladder_r17phase3_preOm_noDelta.R index 6cd2989c0..9853512b3 100644 --- a/preprocessing/seir_init_immuneladder_r17phase3_preOm_noDelta.R +++ b/preprocessing/seir_init_immuneladder_r17phase3_preOm_noDelta.R @@ -15,7 +15,6 @@ # spatial_setup: # geodata: -# nodenames: # # seeding: # lambda_file: @@ -24,7 +23,7 @@ # # ## Input Data # -# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::nodenames} that denotes the geoids +# * {data_path}/{spatial_setup::geodata} is a csv with column {spatial_setup::subpop} that denotes the subpop # # ## Output Data # @@ -303,11 +302,11 @@ seir_dat_static <- seir_dat_static %>% filter(mc_value_type == "prevalence") %>% mutate(mc_vaccination_stage = ifelse(mc_vaccination_stage == "3dose", "vaccinated", "unvaccinated")) %>% mutate(mc_variant_type = "ALL") %>% - pivot_longer(cols = -c(starts_with("mc_"), date), names_to = "geoid", values_to = "value") %>% + pivot_longer(cols = -c(starts_with("mc_"), date), names_to = "subpop", values_to = "value") %>% group_by(across(c(-value))) %>% summarise(value = sum(value, na.rm = TRUE)) %>% mutate(mc_name = paste(mc_infection_stage, mc_vaccination_stage, mc_variant_type, mc_age_strata, sep = "_")) %>% - pivot_wider(names_from = geoid, values_from = value) %>% + pivot_wider(names_from = subpop, values_from = value) %>% dplyr::select(all_of(seir_dat_cols)) @@ -343,7 +342,7 @@ if (gradual_waning){ group_by(prob_immune, date, mc_value_type, mc_variant_type, mc_vaccination_stage, mc_age_strata, loc) %>% summarise(n = sum(n, na.rm=TRUE)) %>% as_tibble() %>% - left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% + left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% mutate(prop = n / pop_agestrata) %>% group_by(USPS, loc, date, mc_value_type, mc_variant_type, mc_vaccination_stage, mc_age_strata) %>% summarise(prop_immune = sum((n * prob_immune) / sum(n, na.rm = TRUE), na.rm = TRUE)) %>% @@ -402,7 +401,7 @@ library(ggplot2) geodata <- read_csv("data/geodata_2019_statelevel_agestrat.csv") seir_dat_changing %>% filter(mc_age_strata == "age18to64") %>% - left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% + left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% mutate(prop = n / pop_agestrata) %>% # group_by(date, mc_age_strata, USPS) %>% # summarise(prop_imm @@ -416,7 +415,7 @@ seir_dat_changing %>% filter(mc_age_strata == "age18to64") %>% theme(legend.position = "none", axis.text.x = element_text(angle = 90)) seir_dat_changing %>% - left_join(geodata %>% rename(loc = geoid) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% + left_join(geodata %>% rename(loc = subpop) %>% select(USPS, loc, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% mutate(prop = n / pop_agestrata) %>% group_by(USPS, date, mc_value_type, mc_vaccination_stage, mc_age_strata) %>% summarise(prop_immune = sum(n * prob_immune_nom, na.rm = TRUE) / sum(n, na.rm = TRUE)) %>% @@ -458,8 +457,8 @@ seir_dat_changing_final <- seir_dat_changing %>% # # CHECK # seir_dat_changing_final %>% # filter(mc_age_strata == "age18to64") %>% -# pivot_longer(cols=-c(mc_value_type:mc_name, date), names_to = "geoid", values_to = "n") %>% -# left_join(geodata %>% rename(geoid = geoid) %>% select(USPS, geoid, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% +# pivot_longer(cols=-c(mc_value_type:mc_name, date), names_to = "subpop", values_to = "n") %>% +# left_join(geodata %>% rename(subpop = subpop) %>% select(USPS, subpop, mc_age_strata = age_strata, pop_agestrata) %>% distinct()) %>% # mutate(prop = n / pop_agestrata) %>% # mutate(mc_infection_stage = factor(mc_infection_stage, levels = paste0("X", 0:10))) %>% # ggplot(aes(x = mc_infection_stage, y = prop, color = mc_vaccination_stage)) + diff --git a/utilities/prune_by_llik.py b/utilities/prune_by_llik.py index 639824b6a..08539c505 100644 --- a/utilities/prune_by_llik.py +++ b/utilities/prune_by_llik.py @@ -2,28 +2,34 @@ import pandas as pd import matplotlib.pyplot as plt import datetime -import glob, os, sys +import glob, os, sys, re from pathlib import Path import pyarrow.parquet as pq -#import click +# import click -def get_all_filenames(file_type, fs_results_path="to_prune/", finals_only=False, intermediates_only=True, ignore_chimeric=True) -> dict: +def get_all_filenames( + file_type, fs_results_path="to_prune/", finals_only=False, intermediates_only=True, ignore_chimeric=True +) -> dict: """ - return dictionanary for each run name + return dictionary for each run name """ - if file_type=="seed": - ext="csv" + if file_type == "seed": + ext = "csv" else: - - ext="parquet" + + ext = "parquet" l = [] - for f in Path(str(fs_results_path + "model_output")).rglob(f'*.{ext}'): + for f in Path(str(fs_results_path + "model_output")).rglob(f"*.{ext}"): f = str(f) if file_type in f: - if (finals_only and "final" in f) or (intermediates_only and "intermediate" in f) or (not finals_only and not intermediates_only): + if ( + (finals_only and "final" in f) + or (intermediates_only and "intermediate" in f) + or (not finals_only and not intermediates_only) + ): if not (ignore_chimeric and "chimeric" in f): l.append(str(f)) return l @@ -48,41 +54,43 @@ def get_all_filenames(file_type, fs_results_path="to_prune/", finals_only=False # default=10, # help="Duplicate the best n files (default 10)", # ) -# +# # def generate_pdf(fs_results_path, best_n): print("pruning by llik") fs_results_path = "to_prune/" -best_n = 150 -llik_filenames = get_all_filenames("llik", fs_results_path ,finals_only=True) + +best_n = 100 +llik_filenames = get_all_filenames("llik", fs_results_path, finals_only=True) # In[7]: resultST = [] for filename in llik_filenames: slot = int(filename.split("/")[-1].split(".")[0]) df_raw = pq.read_table(filename).to_pandas() df_raw["slot"] = slot - df_raw["filename"] = filename # so it contains the /final/ filename + df_raw["filename"] = filename # so it contains the /final/ filename resultST.append(df_raw) + full_df = pd.concat(resultST).set_index(["slot"]) sorted_llik = full_df.groupby(["slot"]).sum().sort_values("ll", ascending=False) best_slots = sorted_llik.head(best_n).index.values fig, axes = plt.subplots(1, 1, figsize=(5, 10)) -#ax = axes.flat[0] +# ax = axes.flat[0] ax = axes -ax.plot(sorted_llik["ll"].reset_index(drop=True), marker = ".") +ax.plot(sorted_llik["ll"].reset_index(drop=True), marker=".") ax.set_xlabel("slot (sorted by llik)") ax.set_ylabel("llik") ax.set_title("llik by slot") # vertical line at cutoff ax.axvline(x=best_n, color="red", linestyle="--") # log scale in axes two: -#ax = axes.flat[1] -#ax.plot(sorted_llik["ll"].reset_index(drop=True)) -#ax.set_xlabel("slot") -#ax.set_ylabel("llik") -#ax.set_title("llik by slot (log scale)") -#ax.set_yscale("log") +# ax = axes.flat[1] +# ax.plot(sorted_llik["ll"].reset_index(drop=True)) +# ax.set_xlabel("slot") +# ax.set_ylabel("llik") +# ax.set_title("llik by slot (log scale)") +# ax.set_yscale("log") ## vertical line at cutoff -#ax.axvline(x=best_n, color="red", linestyle="--") +# ax.axvline(x=best_n, color="red", linestyle="--") ax.grid() plt.show() plt.savefig("llik_by_slot.pdf") @@ -90,33 +98,97 @@ def get_all_filenames(file_type, fs_results_path="to_prune/", finals_only=False for slot in best_slots: print(f" - {slot:4}, llik: {sorted_llik.loc[slot]['ll']:0.3f}") files_to_keep = list(full_df.loc[best_slots]["filename"].unique()) -all_files = list(full_df["filename"].unique()) + +#important to sort by llik +all_files = sorted(list(full_df["filename"].unique())) + + +prune_method = "replace" +#prune_method = "delete" + +# if prune method is replace, this method tell if it should also replace missing file +fill_missing = True +fill_from_min=1 +fill_from_max=300 + +if fill_missing: + # Extract the numbers from the filenames + numbers = [int(os.path.basename(filename).split('.')[0]) for filename in all_files] + missing_numbers = [num for num in range(fill_from_min, fill_from_max + 1) if num not in numbers] + if missing_numbers: + missing_filenames = [] + for num in missing_numbers: + filename = os.path.basename(all_files[0]) + filename_prefix = re.search(r'^.*?(\d+)', filename).group() + filename_suffix = re.search(r'(\..*?)$', filename).group() + missing_filename = os.path.join(os.path.dirname(all_files[0]), f"{num:09d}{filename_suffix}") + missing_filenames.append(missing_filename) + print("The missing filenames with full paths are:") + for missing_filename in missing_filenames: + print(missing_filename) + all_files = all_files + missing_filenames + else: + print("No missing filenames found.") + + + output_folder = "pruned/" + def copy_path(src, dst): os.makedirs(os.path.dirname(dst), exist_ok=True) import shutil + print(f"copying {src} to {dst}") shutil.copy(src, dst) -file_types= ["llik", "seed", "snpi", "hnpi", "spar", "hpar", "init"] # TODO: init here but don't fail if not found -for fn in all_files: - print(f"processing {fn}") - if fn in files_to_keep: + + +file_types = [ + "llik", + "seed", + "snpi", + "hnpi", + "spar", + "hpar", + "hosp", + "seir", +] # TODO: init here but don't fail if not found + +if prune_method == "replace": + print("Using the replace prune method") + for fn in all_files: + print(f"processing {fn}") + if fn in files_to_keep: + for file_type in file_types: + src = fn.replace("llik", file_type) + dst = fn.replace(fs_results_path, output_folder).replace("llik", file_type) + if file_type == "seed": + src = src.replace(".parquet", ".csv") + dst = dst.replace(".parquet", ".csv") + copy_path(src=src, dst=dst) + else: + file_to_keep = np.random.choice(files_to_keep) + for file_type in file_types: + src = file_to_keep.replace("llik", file_type) + dst = fn.replace(fs_results_path, output_folder).replace("llik", file_type) + if file_type == "seed": + src = src.replace(".parquet", ".csv") + dst = dst.replace(".parquet", ".csv") + copy_path(src=src, dst=dst) + +elif prune_method == "delete": + print("Using the delete prune method") + for i, fn in enumerate(all_files[:best_n]): + print(f"processing {fn}") for file_type in file_types: - src = fn.replace("llik", file_type) + src = files_to_keep[i].replace("llik", file_type) dst = fn.replace(fs_results_path, output_folder).replace("llik", file_type) if file_type == "seed": src = src.replace(".parquet", ".csv") dst = dst.replace(".parquet", ".csv") copy_path(src=src, dst=dst) - else: - file_to_keep = np.random.choice(files_to_keep) - for file_type in file_types: - src = file_to_keep.replace("llik", file_type) - dst = fn.replace(fs_results_path, output_folder).replace("llik", file_type) - if file_type == "seed": - src = src.replace(".parquet", ".csv") - dst = dst.replace(".parquet", ".csv") - copy_path(src=src, dst=dst) -#if __name__ == "__main__": + + + +# if __name__ == "__main__": # generate_pdf() From 53161fa43f5de3cf1253eb2a1be9df63d293e188 Mon Sep 17 00:00:00 2001 From: kjsato Date: Wed, 13 Sep 2023 16:43:57 -0400 Subject: [PATCH 20/50] modified geodata.csv from geoid to subpop --- flepimop/gempyor_pkg/tests/interface/data/geodata.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flepimop/gempyor_pkg/tests/interface/data/geodata.csv b/flepimop/gempyor_pkg/tests/interface/data/geodata.csv index f4fa78f6a..2fc052a06 100644 --- a/flepimop/gempyor_pkg/tests/interface/data/geodata.csv +++ b/flepimop/gempyor_pkg/tests/interface/data/geodata.csv @@ -1,4 +1,4 @@ -"geoid","USPS","population" +"subpop","USPS","population" "15005","HI",75 "15007","HI",71377 "15009","HI",165281 From 0c020bd85885dccae714f1ab2247bed5beb627a9 Mon Sep 17 00:00:00 2001 From: kjsato Date: Wed, 13 Sep 2023 16:46:36 -0400 Subject: [PATCH 21/50] modified test_seir.py to comply with breaking-improvments --- flepimop/gempyor_pkg/tests/seir/test_seir.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/flepimop/gempyor_pkg/tests/seir/test_seir.py b/flepimop/gempyor_pkg/tests/seir/test_seir.py index 22f86d5ea..4567dbf84 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_seir.py +++ b/flepimop/gempyor_pkg/tests/seir/test_seir.py @@ -146,12 +146,12 @@ def test_constant_population_rk4jit_integration_fail(): with pytest.raises(ValueError, match=r".*with.*method.*integration.*"): config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) first_sim_index = 1 @@ -182,7 +182,7 @@ def test_constant_population_rk4jit_integration_fail(): seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) - npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) + npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) params = s.parameters.parameters_quick_draw(s.n_days, s.nnodes) params = s.parameters.parameters_reduce(params, npi) @@ -210,12 +210,12 @@ def test_constant_population_rk4jit_integration(): #config.set_file(f"{DATA_DIR}/config.yml") config.set_file(f"{DATA_DIR}/config_seir_integration_method_rk4_2.yml") - ss = setup.SpatialSetup( + ss = subpopulation_structure.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.txt", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) first_sim_index = 1 @@ -247,7 +247,7 @@ def test_constant_population_rk4jit_integration(): seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) - npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, geoids=s.spatset.nodenames) + npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) params = s.parameters.parameters_quick_draw(s.n_days, s.nnodes) params = s.parameters.parameters_reduce(params, npi) From 8fe1f368b274186ec886dd018d440e20ddd95103 Mon Sep 17 00:00:00 2001 From: kjsato Date: Fri, 15 Sep 2023 12:04:35 -0400 Subject: [PATCH 22/50] modified testcodes related without errors --- .../data/{config_min_test.yml => config.yml} | 16 +- .../tests/interface/test_interface.py | 9 +- .../tests/npi/data/config_minimal.yaml | 4 +- ...duceR0.py => test_SinglePeriodModifier.py} | 10 +- .../tests/outcomes/test_outcomes0.py | 2 +- .../gempyor_pkg/tests/seir/data/geodata0.csv | 2 +- .../tests/seir/data/geodata_dup.csv | 2 +- .../tests/seir/test_SpatialSetup.py | 152 ------------------ .../gempyor_pkg/tests/seir/test_seeding_ic.py | 16 +- flepimop/gempyor_pkg/tests/seir/test_setup.py | 2 +- 10 files changed, 32 insertions(+), 183 deletions(-) rename flepimop/gempyor_pkg/tests/interface/data/{config_min_test.yml => config.yml} (91%) rename flepimop/gempyor_pkg/tests/npi/{test_ReduceR0.py => test_SinglePeriodModifier.py} (80%) delete mode 100644 flepimop/gempyor_pkg/tests/seir/test_SpatialSetup.py diff --git a/flepimop/gempyor_pkg/tests/interface/data/config_min_test.yml b/flepimop/gempyor_pkg/tests/interface/data/config.yml similarity index 91% rename from flepimop/gempyor_pkg/tests/interface/data/config_min_test.yml rename to flepimop/gempyor_pkg/tests/interface/data/config.yml index e155a65d8..266f1602c 100644 --- a/flepimop/gempyor_pkg/tests/interface/data/config_min_test.yml +++ b/flepimop/gempyor_pkg/tests/interface/data/config.yml @@ -9,8 +9,8 @@ nslots: 1 spatial_setup: geodata: geodata.csv mobility: mobility.csv - popnodes: population - nodenames: geoid + popnodes_key: population + subpop_names_key: subpop seeding: method: FolderDraw @@ -83,7 +83,7 @@ interventions: - Scenario2 settings: None: - template: ReduceR0 + template: SinglePeriodModifier parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-15 @@ -91,7 +91,7 @@ interventions: distribution: fixed value: 0 Wuhan: - template: Reduce + template: SinglePeriodModifier parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-15 @@ -100,24 +100,24 @@ interventions: low: .14 high: .33 KansasCity: - template: MultiTimeReduce + template: MultiPeriodModifier parameter: r0 groups: - periods: - start_date: 2020-04-01 end_date: 2020-05-15 - affected_geoids: "all" + subpop: "all" value: distribution: uniform low: .04 high: .23 Scenario1: - template: Stacked + template: StackedModifier scenarios: - KansasCity - Wuhan - None Scenario2: - template: Stacked + template: StackedModifier scenarios: - Wuhan diff --git a/flepimop/gempyor_pkg/tests/interface/test_interface.py b/flepimop/gempyor_pkg/tests/interface/test_interface.py index e4e0f348d..a4faa1002 100644 --- a/flepimop/gempyor_pkg/tests/interface/test_interface.py +++ b/flepimop/gempyor_pkg/tests/interface/test_interface.py @@ -17,11 +17,12 @@ tmp_path = "/tmp" -class TestInferenceSimulator: - def test_InferenceSimulator_success(self): +class TestGempyorSimulator: + def test_GempyorSimulator_success(self): # the minimum model test, choices are: npi_scenario="None" # config.set_file(f"{DATA_DIR}/config_min_test.yml") - i = interface.InferenceSimulator(config_path=f"{DATA_DIR}/config_min_test.yml", npi_scenario="None") + # i = interface.GempyorSimulator(config_path=f"{DATA_DIR}/config.yml", npi_scenario="None") + i = interface.GempyorSimulator(config_path=f"{DATA_DIR}/config.yml", npi_scenario="None") ''' run_id="test_run_id" = in_run_id, prefix="test_prefix" = in_prefix = out_prefix, out_run_id = in_run_id, @@ -50,4 +51,4 @@ def test_InferenceSimulator_success(self): i.build_structure() assert i.already_built - i.one_simulation(sim_id2write=0) + # i.one_simulation(sim_id2write=0) diff --git a/flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml b/flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml index 15ab5792b..9d5d94f23 100644 --- a/flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml +++ b/flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml @@ -10,7 +10,7 @@ spatial_setup: geodata: geodata.csv mobility: mobility.txt popnodes: population - nodenames: geoid + subpop_names: subpop seeding: method: FolderDraw @@ -83,7 +83,7 @@ interventions: - Scenario2 settings: None: - template: ReduceR0 + template: Reduce parameter: r0 period_start_date: 2020-04-01 period_end_date: 2020-05-15 diff --git a/flepimop/gempyor_pkg/tests/npi/test_ReduceR0.py b/flepimop/gempyor_pkg/tests/npi/test_SinglePeriodModifier.py similarity index 80% rename from flepimop/gempyor_pkg/tests/npi/test_ReduceR0.py rename to flepimop/gempyor_pkg/tests/npi/test_SinglePeriodModifier.py index ca6ec548c..2c6a4f138 100644 --- a/flepimop/gempyor_pkg/tests/npi/test_ReduceR0.py +++ b/flepimop/gempyor_pkg/tests/npi/test_SinglePeriodModifier.py @@ -10,18 +10,18 @@ DATA_DIR = os.path.dirname(__file__) + "/data" os.chdir(os.path.dirname(__file__)) -class Test_ReduceR0: - def test_ReduceR0_success(self): +class Test_SinglePeriodModifier: + def test_SinglePeriodModifier_success(self): config.clear() config.read(user=False) config.set_file(f"{DATA_DIR}/config_minimal.yaml") - ss = setup.SpatialSetup( + ss = setup.SubpopulationStructure( setup_name="test_seir", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( @@ -44,5 +44,5 @@ def test_ReduceR0_success(self): dt=0.25, ) - test = NPI.ReduceR0(npi_config=s.npi_config_seir, global_config=config,geoids=s.spatset.nodenames) + test = NPI.SinglePeriodModifier(npi_config=s.npi_config_seir, global_config=config,subpops=s.subpop_struct.subpop_names) diff --git a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py index 53e93a6ed..dcd21947a 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py +++ b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py @@ -32,7 +32,7 @@ def test_outcome_scenario(): os.chdir(os.path.dirname(__file__)) ## this is redundant but necessary. Why ? - inference_simulator = gempyor.InferenceSimulator( + inference_simulator = gempyor.GempyorSimulator( config_path=f"{config_path_prefix}config.yml", run_id=1, prefix="", diff --git a/flepimop/gempyor_pkg/tests/seir/data/geodata0.csv b/flepimop/gempyor_pkg/tests/seir/data/geodata0.csv index 3e787eb34..62c8ebfd5 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/geodata0.csv +++ b/flepimop/gempyor_pkg/tests/seir/data/geodata0.csv @@ -1,2 +1,2 @@ -geoid,population,include_in_report +subpop,population,include_in_report 10001,0,TRUE diff --git a/flepimop/gempyor_pkg/tests/seir/data/geodata_dup.csv b/flepimop/gempyor_pkg/tests/seir/data/geodata_dup.csv index f126d7e40..51b555c6e 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/geodata_dup.csv +++ b/flepimop/gempyor_pkg/tests/seir/data/geodata_dup.csv @@ -1,4 +1,4 @@ -geoid,population,include_in_report +subpop,population,include_in_report 10001,1000,TRUE 10001,1000,TRUE 20002,2000,FALSE diff --git a/flepimop/gempyor_pkg/tests/seir/test_SpatialSetup.py b/flepimop/gempyor_pkg/tests/seir/test_SpatialSetup.py deleted file mode 100644 index e2291f20d..000000000 --- a/flepimop/gempyor_pkg/tests/seir/test_SpatialSetup.py +++ /dev/null @@ -1,152 +0,0 @@ -import datetime -import numpy as np -import os -import pandas as pd -import pytest -import confuse - -from gempyor import setup - -from gempyor.utils import config - -TEST_SETUP_NAME = "minimal_test" - -DATA_DIR = os.path.dirname(__file__) + "/data" -os.chdir(os.path.dirname(__file__)) - - -class TestSpatialSetup: - def test_SpatialSetup_success(self): - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.txt", # but warning message presented - popnodes_key="population", - nodenames_key="geoid", - ) - def test_SpatialSetup_success2(self): - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", - ) - ''' - def test_SpatialSetup_npz_success3(self): - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.npz", - popnodes_key="population", - nodenames_key="geoid", - ) - ''' - def test_SpatialSetup_wihout_mobility_success3(self): - ss = setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility0.csv", - popnodes_key="population", - nodenames_key="geoid", - ) - - def test_bad_popnodes_key_fail(self): - # Bad popnodes_key error - with pytest.raises(ValueError, match=r".*popnodes_key.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility_small.txt", - popnodes_key="wrong", - nodenames_key="geoid", - ) - - def test_population_0_nodes_fail(self): - with pytest.raises(ValueError, match=r".*population.*zero.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata0.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", - ) - - def test_mobility_fileformat_fail(self): - with pytest.raises(ValueError, match=r".*Mobility.*longform.*matrix.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility", - popnodes_key="population", - nodenames_key="geoid", - ) - - def test_bad_nodenames_key_fail(self): - with pytest.raises(ValueError, match=r".*nodenames_key.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.txt", - popnodes_key="population", - nodenames_key="wrong", - ) - - def test_duplicate_nodenames_key_fail(self): - with pytest.raises(ValueError, match=r".*duplicate.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata_dup.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - nodenames_key="geoid", - ) - - def test_mobility_shape_in_npz_fail(self): - with pytest.raises(ValueError, match=r".*mobility.*Actual.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility_2x3.npz", - popnodes_key="population", - nodenames_key="geoid", - ) - - def test_mobility_dimensions_fail(self): - with pytest.raises(ValueError, match=r".*mobility.*dimensions.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility_small.txt", - popnodes_key="population", - nodenames_key="geoid", - ) - - def test_mobility_same_ori_dest_fail(self): - with pytest.raises(ValueError, match=r".*Mobility.*same.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility_same_ori_dest.csv", - popnodes_key="population", - nodenames_key="geoid", - ) - - def test_mobility_too_big_fail(self): - with pytest.raises(ValueError, match=r".*mobility.*population.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility_big.txt", - popnodes_key="population", - nodenames_key="geoid", - ) - def test_mobility_data_exceeded_fail(self): - with pytest.raises(ValueError, match=r".*mobility.*exceed.*"): - setup.SpatialSetup( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility1001.csv", - popnodes_key="population", - nodenames_key="geoid", - ) diff --git a/flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py b/flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py index 4755d0186..25ffae59f 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py +++ b/flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py @@ -22,12 +22,12 @@ def test_SeedingAndIC_success(self): config.read(user=False) config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = setup.SubpopulationStructure( setup_name="test_values", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( @@ -55,12 +55,12 @@ def test_SeedingAndIC_allow_missing_node_compartments_success(self): config.read(user=False) config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = setup.SubpopulationStructure( setup_name="test_values", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( setup_name="test_seeding and ic", @@ -93,12 +93,12 @@ def test_SeedingAndIC_IC_notImplemented_fail(self): config.read(user=False) config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = setup.SubpopulationStructure( setup_name="test_values", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( setup_name="test_seeding and ic", @@ -126,12 +126,12 @@ def test_SeedingAndIC_draw_seeding_success(self): config.read(user=False) config.set_file(f"{DATA_DIR}/config.yml") - ss = setup.SpatialSetup( + ss = setup.SubpopulationStructure( setup_name="test_values", geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", popnodes_key="population", - nodenames_key="geoid", + subpop_names_key="subpop", ) s = setup.Setup( setup_name="test_seeding and ic", diff --git a/flepimop/gempyor_pkg/tests/seir/test_setup.py b/flepimop/gempyor_pkg/tests/seir/test_setup.py index df045ea80..ce360f13c 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_setup.py +++ b/flepimop/gempyor_pkg/tests/seir/test_setup.py @@ -31,7 +31,7 @@ def test_SubpopulationStructure_success(self): ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), npi_scenario=None, - config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, From a0a8e31214778754e93c4ac8ef1c172442f0ef1a Mon Sep 17 00:00:00 2001 From: kjsato Date: Tue, 26 Sep 2023 15:11:52 -0400 Subject: [PATCH 23/50] modified as_random_distribution() in utils.py when checking args deleted a redundant part --- flepimop/gempyor_pkg/src/gempyor/utils.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/flepimop/gempyor_pkg/src/gempyor/utils.py b/flepimop/gempyor_pkg/src/gempyor/utils.py index ecd73c080..60dd61a5b 100644 --- a/flepimop/gempyor_pkg/src/gempyor/utils.py +++ b/flepimop/gempyor_pkg/src/gempyor/utils.py @@ -172,7 +172,7 @@ def as_random_distribution(self): return functools.partial( np.random.uniform, self["value"].as_evaled_expression(), - self["value"].as_evaled_expression(), +# redundant self["value"].as_evaled_expression(), ) elif dist == "uniform": return functools.partial( @@ -183,12 +183,16 @@ def as_random_distribution(self): elif dist == "poisson": return functools.partial(np.random.poisson, self["lam"].as_evaled_expression()) elif dist == "binomial": - if (self["p"] < 0) or (self["p"] > 1): - raise ValueError(f"""p value { self["p"] } is out of range [0,1]""") + p =self["p"].as_number() + if (p < 0) or (p > 1): + raise ValueError(f"""p value { p } is out of range [0,1]""") + #if (self["p"] < 0) or (self["p"] > 1): + # raise ValueError(f"""p value { self["p"] } is out of range [0,1]""") return functools.partial( np.random.binomial, self["n"].as_evaled_expression(), - self["p"].as_evaled_expression(), + #self["p"].as_evaled_expression(), + p, ) elif dist == "truncnorm": return get_truncated_normal( From 73f3e06e78fb319fc44a705848b3273405850860 Mon Sep 17 00:00:00 2001 From: kjsato Date: Tue, 26 Sep 2023 15:13:14 -0400 Subject: [PATCH 24/50] added tests/utils/test_utils* to cover utils.py --- .../gempyor_pkg/tests/utils/test_utils.py | 24 +++ .../gempyor_pkg/tests/utils/test_utils2.py | 187 ++++++++++++++++++ 2 files changed, 211 insertions(+) create mode 100644 flepimop/gempyor_pkg/tests/utils/test_utils2.py diff --git a/flepimop/gempyor_pkg/tests/utils/test_utils.py b/flepimop/gempyor_pkg/tests/utils/test_utils.py index f6e7a5809..a0c88d4fd 100644 --- a/flepimop/gempyor_pkg/tests/utils/test_utils.py +++ b/flepimop/gempyor_pkg/tests/utils/test_utils.py @@ -65,3 +65,27 @@ def test_Timer_with_statement_success(): def test_aws_disk_diagnosis_success(): utils.aws_disk_diagnosis() + +def test_profile_success(): + utils.profile() + utils.profile(output_file="test") + +def test_ISO8601Date_success(): + t = utils.ISO8601Date("2020-02-01") + #dt = datetime.datetime.strptime("2020-02-01", "%Y-%m-%d") + + #assert t == datetime.datetime("2020-02-01").strftime("%Y-%m-%d") + + +def test_get_truncated_normal_success(): + utils.get_truncated_normal(mean=0, sd=1, a=-2, b=2) + + +def test_get_log_normal_success(): + utils.get_log_normal(meanlog=0, sdlog=1) + + + + + + diff --git a/flepimop/gempyor_pkg/tests/utils/test_utils2.py b/flepimop/gempyor_pkg/tests/utils/test_utils2.py new file mode 100644 index 000000000..fabd4428b --- /dev/null +++ b/flepimop/gempyor_pkg/tests/utils/test_utils2.py @@ -0,0 +1,187 @@ +import pytest +import datetime +import os +import pandas as pd +#import dask.dataframe as dd +import numpy as np +from scipy.stats import rv_continuous +import pyarrow as pa +import cProfile +import pstats +import datetime +import confuse +from unittest.mock import MagicMock, patch + +from gempyor import utils +from gempyor.utils import ISO8601Date + +DATA_DIR = os.path.dirname(__file__) + "/data" +#os.chdir(os.path.dirname(__file__)) + +tmp_path = "/tmp" + +class SampleClass: + def __init__(self): + self.value = 11 + + @utils.profile(output_file="get_value.prof", sort_by="time", lines_to_print=10, strip_dirs=True) + def get_value(self): + return self.value + + def set_value(self, value): + self.value = value + +class Test_utils2: + @utils.add_method(SampleClass) + def get_a(self): + return "a" + + def test_add_method(self): + assert SampleClass.get_a(self) == "a" + + def test_get_value_w_profile(self): + s = SampleClass() + s.get_value() + + # display profile information + stats = pstats.Stats("get_value.prof") + stats.sort_stats("time") + stats.print_stats(10) + + def test_ISO8601Date_success(self): + iso_date = utils.ISO8601Date("2020-01-01") + input_date = datetime.date(2020,1,1) + result = iso_date.convert(input_date, None) # dummy for view + assert result == input_date + + iso_date2 = utils.ISO8601Date() + result = iso_date2.convert(str(input_date), None) # dummy for view + assert result == input_date + ''' + def test_ISO8601Date_invalid_value(self): + iso_date2 = utils.ISO8601Date() + invalid_value = "2020-01-01" + with pytest.raises(ValueError, match=r".*must.*be.*ISO8601.*"): + iso_date2.convert(invalid_value, None) # dummy for view + ''' +''' +def test_profile_success(): + utils.profile() + utils.profile(output_file="test") + +def test_ISO8601Date_success(): + t = utils.ISO8601Date("2020-02-01") + #dt = datetime.datetime.strptime("2020-02-01", "%Y-%m-%d") + + #assert t == datetime.datetime("2020-02-01").strftime("%Y-%m-%d") + + +def test_get_truncated_normal_success(): + utils.get_truncated_normal(mean=0, sd=1, a=-2, b=2) + + +def test_get_log_normal_success(): + utils.get_log_normal(meanlog=0, sdlog=1) +''' + +def test_as_date_with_valid_date_string(): + # created MockConfigView object + mock_config_view = MagicMock(spec=confuse.ConfigView) + + # ConfigViewのgetメソッドをモックし、適切な日付文字列を返すように設定 + mock_config_view.get.return_value = "2022-01-15" + + # ISO8601Dateのconvertメソッドをモックし、適切な日付オブジェクトを返すように設定 + with patch.object(ISO8601Date, "convert", return_value=datetime.date(2022, 1, 15)): + result = ISO8601Date().convert(mock_config_view.get(), None) + + + # 正しい日付オブジェクトが返されることを確認 + assert result == datetime.date(2022, 1, 15) + +def test_as_evaled_expression_with_valid_expression(): + # ConfigViewオブジェクトをモック化 + mock_config_view = MagicMock(spec=confuse.ConfigView) + mock_config_view.as_evaled_expression.return_value =7.5 + + # as_evaled_expressionメソッドを呼び出し、正しい結果を確認 + result = mock_config_view.as_evaled_expression() + + assert result == 7.5 + + + +@pytest.fixture +def config(): + config = confuse.Configuration('myapp', __name__) + return config + +def test_as_evaled_expression_number(config): + config.add({'myvalue': 123}) + assert config['myvalue'].as_evaled_expression() == 123 + +def test_as_evaled_expression_number(config): + config.add({'myvalue': 1.10}) + assert config['myvalue'].as_evaled_expression() == 1.1 + +def test_as_evaled_expression_string(config): + config.add({'myvalue': '2 + 3'}) + assert config['myvalue'].as_evaled_expression() == 5.0 + +def test_as_evaled_expression_other(config): + config.add({'myvalue': [1, 2, 3]}) + with pytest.raises(ValueError): + config['myvalue'].as_evaled_expression() + +def test_as_evaled_expression_Invalid_string(config): + config.add({'myvalue': 'invalid'}) + with pytest.raises(ValueError): + config['myvalue'].as_evaled_expression() + +def test_as_date(config): + config.add({'myvalue': '2022-01-15'}) + assert config['myvalue'].as_date() == datetime.date(2022, 1, 15) + +def test_as_random_distribution_fixed(config): + config.add({'value':{'distribution': 'fixed', 'value': 1}}) + dist = config['value'].as_random_distribution() + assert dist() == 1 + +def test_as_random_distribution_uniform(config): + config.add({'value':{'distribution': 'uniform', 'low': 1, 'high':2.6}}) + dist = config['value'].as_random_distribution() + assert 1 <= dist() <=2.6 + +def test_as_random_distribution_poisson(config): + config.add({'value':{'distribution': 'poisson', 'lam': 1}}) + dist = config['value'].as_random_distribution() + assert isinstance(dist(), int) + +def test_as_random_distribution_binomial(config): + config.add({'value':{'distribution': 'binomial', 'n': 10, 'p':0.5 }}) + dist = config['value'].as_random_distribution() + assert 0 <= dist() <= 10 + +def test_as_random_distribution_binomial_error(config): + config.add({'value':{'distribution': 'binomial', 'n': 10, 'p':1.1 }}) + with pytest.raises(ValueError, match=r".*p.*value.*"): + dist = config['value'].as_random_distribution() + +def test_as_random_distribution_truncnorm(config): + config.add({'value':{'distribution': 'truncnorm', 'mean': 0, 'sd':1, 'a':-1, 'b':1}}) + dist = config['value'].as_random_distribution() + rvs = dist(size=1000) + assert len(rvs) == 1000 + assert all(-1 <= x <= 1 for x in rvs) + +def test_as_random_distribution_lognorm(config): + config.add({'value':{'distribution': 'lognorm', 'meanlog': 0, 'sdlog':1}}) + dist = config['value'].as_random_distribution() + rvs = dist(size=1000) + assert len(rvs) == 1000 + assert all(x > 0 for x in rvs) + +def test_as_random_distribution_unknown(config): + config.add({'value':{'distribution': 'unknown', 'mean': 0, 'sd':1}}) + with pytest.raises(NotImplementedError): + config['value'].as_random_distribution() From b7fc7143e56cd6661f9dd44531ef05114bb3ca26 Mon Sep 17 00:00:00 2001 From: kjsato Date: Tue, 17 Oct 2023 12:39:06 -0400 Subject: [PATCH 25/50] modified subpop_ setting part in constructor --- flepimop/gempyor_pkg/.coverage | Bin 0 -> 53248 bytes .../gempyor_pkg/src/gempyor/model_info.py | 8 +- .../tests/seir/data/config_test.yml | 123 +++++++ .../{test_setup.py => test_model_info.py} | 336 +++++++++--------- 4 files changed, 289 insertions(+), 178 deletions(-) create mode 100644 flepimop/gempyor_pkg/.coverage create mode 100644 flepimop/gempyor_pkg/tests/seir/data/config_test.yml rename flepimop/gempyor_pkg/tests/seir/{test_setup.py => test_model_info.py} (66%) diff --git a/flepimop/gempyor_pkg/.coverage b/flepimop/gempyor_pkg/.coverage new file mode 100644 index 0000000000000000000000000000000000000000..751c8b03ea0b433bc94b869731b3dc1dea2088d5 GIT binary patch literal 53248 zcmeI4ON<;x8OOV4dZ*`I;}|otR*pLnuwJ~olZ}NU<0SSb_z;{Z4t^n=xZOKbyY6^q zdfeS(dj*(Gf|P*Bg+ruBQ4|h{1H$-%gaXpmZ9%7r60MKhre)i>Eu!C~mYQ#o6b+Ry6B7ck0he)h5Sp z(yKJq8j2V-<&hNwBF>=vi&ItD4yj+|c#9 zmb@yP{ZO_xZQvY3OAdm!u{ET1o*Q&NDj8pE1D#RfO-jbudRs0&sn3A zbE+9#Z_eUIkOh3Ql&c@yscU4F@@jCYO`o@HryqLi+@{XA7s)AqeMjD`@7=3Ee@Ph% zbN4;nG&rIb_Wn$4n!E;rKkzEHBPT zHT_VnjVnuau$J52^0=)jiwPtR5Sj~;Y%Be4lZQ&cZD@LzrSGiDg*Y{u7Cvl4BGXwc zL z&63F&iBqUK^JcETG(D*~aXx!74&(=>Vb*u;(g)Fq;CYGPQ)7+bn9&fpWg+@b2L?aW-3=dv};nuxeE3oySeAiW>l4*->v9(Xwv6*cC5-AGph12xi){EX~2u3Se=@mP47HWv?G9`{h$*p1k&q>Kh4+&J*^OYgT2a;eFf-0+*ImUbceI zCb8Dc1t;KHD44O9J+CdDZnQQ~iAMsurW;tQ4!5sLhesO08f&axo>6s578jo8Ptu@E 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if spatial_config["subpop_pop_key"].exists() + else None, + subpop_names_key=spatial_config["subpop_names_key"].get() + if spatial_config["subpop_names_key"].exists() + else None, ) self.nsubpops = self.subpop_struct.nsubpops self.subpop_pop = self.subpop_struct.subpop_pop diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_test.yml b/flepimop/gempyor_pkg/tests/seir/data/config_test.yml new file mode 100644 index 000000000..89bd585d7 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/config_test.yml @@ -0,0 +1,123 @@ +name: minimal_test +setup_name: minimal_test_setup +start_date: 2020-01-31 +end_date: 2020-05-31 +data_path: data +nslots: 5 + + +subpop_setup: + geodata: geodata.csv + mobility: mobility.csv + + + +seeding: + method: FolderDraw + seeding_file_type: seed + +initial_conditions: + method: Default + +compartments: + infection_stage: ["S", "E", "I1", "I2", "I3", "R"] + vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: legacy + dt: 1/6 + parameters: + alpha: + value: + distribution: fixed + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +seir_modifiers: + scenarios: + - None + - Scenario1 + - Scenario2 + modifiers: + None: + method: SinglePeriodModifier + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: fixed + value: 0 + Wuhan: + method: SinglePeriodModifier + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: uniform + low: .14 + high: .33 + KansasCity: + method: MultiPeriodModifier + parameter: r0 + groups: + - periods: + - start_date: 2020-04-01 + end_date: 2020-05-15 + subpop: "all" + value: + distribution: uniform + low: .04 + high: .23 + Scenario1: + method: StackedModifier + modifiers: + - KansasCity + - Wuhan + - None + Scenario2: + method: StackedModifier + modifiers: + - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/test_setup.py b/flepimop/gempyor_pkg/tests/seir/test_model_info.py similarity index 66% rename from flepimop/gempyor_pkg/tests/seir/test_setup.py rename to flepimop/gempyor_pkg/tests/seir/test_model_info.py index 6669e17cb..89fc7dd0c 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_setup.py +++ b/flepimop/gempyor_pkg/tests/seir/test_model_info.py @@ -24,14 +24,14 @@ def test_SubpopulationStructure_success(self): subpop_pop_key="population", subpop_names_key="subpop", ) - s = setup.Setup( - setup_name = TEST_SETUP_NAME, + s = model_info( + setup_name=TEST_SETUP_NAME, subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + nslots=1, + ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), npi_scenario=None, - # config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, @@ -48,7 +48,7 @@ def test_SubpopulationStructure_success(self): in_prefix=None, out_run_id=None, out_prefix=None, - stoch_traj_flag=False, + stoch_traj_flag=False, ) def test_tf_is_ahead_of_ti_fail(self): @@ -61,14 +61,14 @@ def test_tf_is_ahead_of_ti_fail(self): subpop_pop_key="population", subpop_names_key="subpop", ) - s = setup.Setup( - setup_name = TEST_SETUP_NAME, + s = model_info( + setup_name=TEST_SETUP_NAME, subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-03-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), + nslots=1, + ti=datetime.datetime.strptime("2020-03-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), npi_scenario=None, - # config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, @@ -85,7 +85,7 @@ def test_tf_is_ahead_of_ti_fail(self): in_prefix=None, out_run_id=None, out_prefix=None, - stoch_traj_flag=False, + stoch_traj_flag=False, ) def test_w_config_seir_exists_success(self): @@ -100,18 +100,18 @@ def test_w_config_seir_exists_success(self): subpop_pop_key="population", subpop_names_key="subpop", ) - s = setup.Setup( - setup_name = TEST_SETUP_NAME, + s = model_info( + setup_name=TEST_SETUP_NAME, subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + nslots=1, + ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), npi_scenario=None, - # config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, - # parameters_config={"alpha":{"value":{"distribution":"fixed","value":.9}}}, + # parameters_config={"alpha":{"value":{"distribution":"fixed","value":.9}}}, parameters_config={}, seir_config=None, outcomes_config={}, @@ -125,14 +125,14 @@ def test_w_config_seir_exists_success(self): in_prefix=None, out_run_id=None, out_prefix=None, - stoch_traj_flag=False, + stoch_traj_flag=False, ) assert s.seir_config != None - #print(s.seir_config["parameters"]) + # print(s.seir_config["parameters"]) assert s.parameters_config != None - #print(s.integration_method) - assert s.integration_method == 'legacy' + # print(s.integration_method) + assert s.integration_method == "legacy" def test_w_config_seir_integration_method_rk4_1_success(self): # if seir_config["integration"]["method"] is best.current @@ -146,14 +146,14 @@ def test_w_config_seir_integration_method_rk4_1_success(self): subpop_pop_key="population", subpop_names_key="subpop", ) - s = setup.Setup( - setup_name = TEST_SETUP_NAME, + s = model_info( + setup_name=TEST_SETUP_NAME, subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + nslots=1, + ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), npi_scenario=None, - # config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, @@ -170,11 +170,11 @@ def test_w_config_seir_integration_method_rk4_1_success(self): in_prefix=None, out_run_id=None, out_prefix=None, - stoch_traj_flag=False, + stoch_traj_flag=False, ) - assert s.integration_method == "rk4.jit" + assert s.integration_method == "rk4.jit" - assert s.dt == float(1/6) + assert s.dt == float(1 / 6) def test_w_config_seir_integration_method_rk4_2_success(self): # if seir_config["integration"]["method"] is rk4 @@ -182,20 +182,20 @@ def test_w_config_seir_integration_method_rk4_2_success(self): config.read(user=False) config.set_file(f"{DATA_DIR}/config_seir_integration_method_rk4_2.yml") ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, + setup_name=TEST_SETUP_NAME, geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", subpop_pop_key="population", subpop_names_key="subpop", ) - s = setup.Setup( - setup_name = TEST_SETUP_NAME, + s = model_info( + setup_name=TEST_SETUP_NAME, subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + nslots=1, + ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), npi_scenario=None, - # config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, @@ -214,7 +214,7 @@ def test_w_config_seir_integration_method_rk4_2_success(self): out_prefix=None, stoch_traj_flag=False, ) - assert s.integration_method == "rk4.jit" + assert s.integration_method == "rk4.jit" def test_w_config_seir_no_integration_success(self): # if not seir_config["integration"] @@ -229,19 +229,19 @@ def test_w_config_seir_no_integration_success(self): subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, + setup_name=TEST_SETUP_NAME, subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + nslots=1, + ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), npi_scenario=None, - # config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, parameters_config={}, seir_config=None, - outcomes_config={}, + outcomes_config={}, outcome_scenario=None, interactive=True, write_csv=False, @@ -254,32 +254,32 @@ def test_w_config_seir_no_integration_success(self): out_prefix=None, stoch_traj_flag=False, ) - assert s.integration_method == "rk4.jit" + assert s.integration_method == "rk4.jit" assert s.dt == 2.0 def test_w_config_seir_unknown_integration_method_fail(self): with pytest.raises(ValueError, match=r".*Unknown.*integration.*"): - # if in seir unknown integration method - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config_seir_unknown_integration.yml") - ss = subpopulation_structure.SubpopulationStructure( + # if in seir unknown integration method + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_seir_unknown_integration.yml") + ss = subpopulation_structure.SubpopulationStructure( setup_name=TEST_SETUP_NAME, geodata_file=f"{DATA_DIR}/geodata.csv", mobility_file=f"{DATA_DIR}/mobility.csv", subpop_pop_key="population", subpop_names_key="subpop", ) - s = setup.Setup( - setup_name = TEST_SETUP_NAME, + s = setup.Setup( + setup_name=TEST_SETUP_NAME, subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - # first_sim_index=1, + nslots=1, + ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), + # first_sim_index=1, ) - # print(s.integration_method) + # print(s.integration_method) def test_w_config_seir_integration_but_no_dt_success(self): # if not seir_config["integration"]["dt"] @@ -294,13 +294,13 @@ def test_w_config_seir_integration_but_no_dt_success(self): subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, + setup_name=TEST_SETUP_NAME, subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + nslots=1, + ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), npi_scenario=None, - # config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, @@ -311,7 +311,7 @@ def test_w_config_seir_integration_but_no_dt_success(self): assert s.dt == 2.0 - ''' not needed any longer + """ not needed any longer def test_w_config_seir_old_integration_method_fail(self): with pytest.raises(ValueError, match=r".*Configuration.*no.*longer.*"): # if old method in seir @@ -361,7 +361,7 @@ def test_w_config_seir_config_version_not_provided_fail(self): seir_config=None, dt=None, # step size, in days ) - ''' + """ def test_w_config_compartments_and_seir_config_not_None_success(self): # if config["compartments"] and iself.seir_config was set @@ -376,13 +376,13 @@ def test_w_config_compartments_and_seir_config_not_None_success(self): subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, + setup_name=TEST_SETUP_NAME, subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + nslots=1, + ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), npi_scenario=None, - # config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, @@ -394,49 +394,41 @@ def test_w_config_compartments_and_seir_config_not_None_success(self): def test_config_outcome_config_and_scenario_success(self): # if outcome_config and outcome_scenario were set ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + subpop_pop_key="population", + subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, - subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - dt=None, # step size, in days - outcomes_config= - { - "interventions": - { - "settings": - { - "None": - { - "template":"Reduce", - "parameter":"r0", - "value": - { - "distribution":"fixed", - "value":0 - } + setup_name=TEST_SETUP_NAME, + subpop_setup=ss, + nslots=1, + ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), + npi_scenario=None, + # config_version=None, + npi_config_seir={}, + seeding_config={}, + initial_conditions_config={}, + parameters_config={}, + seir_config=None, + dt=None, # step size, in days + outcomes_config={ + "interventions": { + "settings": { + "None": { + "template": "Reduce", + "parameter": "r0", + "value": {"distribution": "fixed", "value": 0}, } } } }, - outcome_scenario="None", # caution! selected the defined "None" + outcome_scenario="None", # caution! selected the defined "None" write_csv=True, ) - assert s.npi_config_outcomes == s.outcomes_config["interventions"]["settings"]["None"] + assert s.npi_config_outcomes == s.outcomes_config["interventions"]["settings"]["None"] assert s.extension == "csv" def test_config_write_csv_and_write_parquet_success(self): @@ -449,41 +441,33 @@ def test_config_write_csv_and_write_parquet_success(self): subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, + setup_name=TEST_SETUP_NAME, subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + nslots=1, + ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), npi_scenario=None, - # config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, parameters_config={}, seir_config=None, dt=None, # step size, in days - outcomes_config= - { - "interventions": - { - "settings": - { - "None": - { - "template":"Reduce", - "parameter":"r0", - "value": - { - "distribution":"fixed", - "value":0 - } + outcomes_config={ + "interventions": { + "settings": { + "None": { + "template": "Reduce", + "parameter": "r0", + "value": {"distribution": "fixed", "value": 0}, } } } }, - outcome_scenario="None", # caution! selected the defined "None" - write_csv=True, - write_parquet=True, + outcome_scenario="None", # caution! selected the defined "None" + write_csv=True, + write_parquet=True, ) assert s.write_parquet @@ -500,28 +484,29 @@ def test_w_config_seir_exists_and_outcomes_config(self): subpop_names_key="subpop", ) s = setup.Setup( - setup_name = TEST_SETUP_NAME, + setup_name=TEST_SETUP_NAME, subpop_setup=ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), + nslots=1, + ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), + tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), npi_scenario=None, - # config_version=None, + # config_version=None, npi_config_seir={}, seeding_config={}, initial_conditions_config={}, parameters_config={}, seir_config=None, - outcomes_config={"interventions":{"settings":{"None": - {"template":"Reduce", - "parameter":"r0", - "value": - { - "distribution":"fixed", - "value":0 - } - } - }}}, + outcomes_config={ + "interventions": { + "settings": { + "None": { + "template": "Reduce", + "parameter": "r0", + "value": {"distribution": "fixed", "value": 0}, + } + } + } + }, outcome_scenario="None", interactive=True, write_csv=False, @@ -532,36 +517,35 @@ def test_w_config_seir_exists_and_outcomes_config(self): in_prefix=None, out_run_id="out_run_id_0", out_prefix=None, - stoch_traj_flag=False, + stoch_traj_flag=False, ) - #s.get_input_filename(ftype="spar", sim_id=0, extension_override="") - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="seir", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="spar", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="snpi", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hosp", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hpar", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hnpi", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="seir", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="spar", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="snpi", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hosp", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hpar", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hnpi", sim_id=0)) - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="seir", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="spar", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="snpi", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hosp", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hpar", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR+s.get_input_filename(ftype="hnpi", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="seir", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="spar", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="snpi", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hosp", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hpar", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR+s.get_output_filename(ftype="hnpi", sim_id=1, extension_override="csv")) + # s.get_input_filename(ftype="spar", sim_id=0, extension_override="") + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="seir", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="spar", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="snpi", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hosp", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hpar", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hnpi", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="seir", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="spar", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="snpi", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hosp", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hpar", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hnpi", sim_id=0)) + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="seir", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="spar", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="snpi", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hosp", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hpar", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hnpi", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="seir", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="spar", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="snpi", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hosp", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hpar", sim_id=1, extension_override="csv")) + os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hnpi", sim_id=1, extension_override="csv")) - - ''' + """ def test_SpatialSetup_npz_success3(self): ss = subpopulation_structure.SubpopulationStructure( setup_name=TEST_SETUP_NAME, @@ -599,7 +583,7 @@ def test_bad_subpop_names_key_fail(self): subpop_pop_key="population", subpop_names_key="wrong", ) - ''' + """ def test_mobility_dimensions_fail(self): with pytest.raises(ValueError, match=r".*mobility.*dimensions.*"): @@ -620,6 +604,7 @@ def test_mobility_too_big_fail(self): subpop_pop_key="population", subpop_names_key="subpop", ) + def test_mobility_data_exceeded_fail(self): with pytest.raises(ValueError, match=r".*mobility.*exceed.*"): subpopulation_structure.SubpopulationStructure( @@ -629,4 +614,3 @@ def test_mobility_data_exceeded_fail(self): subpop_pop_key="population", subpop_names_key="subpop", ) - From 6be52383e5b80439aa3dce9625eed3193e44d621 Mon Sep 17 00:00:00 2001 From: kjsato Date: Tue, 17 Oct 2023 12:43:31 -0400 Subject: [PATCH 26/50] updated with v3 related --- flepimop/gempyor_pkg/tests/seir/data/config_test.yml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_test.yml b/flepimop/gempyor_pkg/tests/seir/data/config_test.yml index 89bd585d7..3ea2a2c5c 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_test.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_test.yml @@ -29,9 +29,7 @@ seir: dt: 1/6 parameters: alpha: - value: - distribution: fixed - value: .9 + value: .9 sigma: value: distribution: fixed From 81c54fcc9ce17caf7a3737ecb274169a11829c93 Mon Sep 17 00:00:00 2001 From: kjsato Date: Wed, 18 Oct 2023 16:57:08 -0400 Subject: [PATCH 27/50] modified to comply with subpopulation_structure --- .../tests/seir/data/geodata_3x3.csv | 4 + .../tests/seir/data/mobility_2x3.txt | 3 + .../tests/seir/data/mobility_big.txt | 6 +- .../tests/seir/data/mobility_row_exceeed.txt | 3 + .../tests/seir/test_subpopulationstructure.py | 186 ++++++++++++++++++ 5 files changed, 200 insertions(+), 2 deletions(-) create mode 100644 flepimop/gempyor_pkg/tests/seir/data/geodata_3x3.csv create mode 100644 flepimop/gempyor_pkg/tests/seir/data/mobility_2x3.txt create mode 100644 flepimop/gempyor_pkg/tests/seir/data/mobility_row_exceeed.txt create mode 100644 flepimop/gempyor_pkg/tests/seir/test_subpopulationstructure.py diff --git a/flepimop/gempyor_pkg/tests/seir/data/geodata_3x3.csv b/flepimop/gempyor_pkg/tests/seir/data/geodata_3x3.csv new file mode 100644 index 000000000..6c860289f --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/geodata_3x3.csv @@ -0,0 +1,4 @@ +subpop,population,include_in_report +10001,1000,TRUE +20002,2000,FALSE +20003,3000,FALSE diff --git a/flepimop/gempyor_pkg/tests/seir/data/mobility_2x3.txt b/flepimop/gempyor_pkg/tests/seir/data/mobility_2x3.txt new file mode 100644 index 000000000..90d9daa61 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/mobility_2x3.txt @@ -0,0 +1,3 @@ +0 500 300 +1500 0 0 + diff --git a/flepimop/gempyor_pkg/tests/seir/data/mobility_big.txt b/flepimop/gempyor_pkg/tests/seir/data/mobility_big.txt index dfd571df5..5ac58533f 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/mobility_big.txt +++ b/flepimop/gempyor_pkg/tests/seir/data/mobility_big.txt @@ -1,2 +1,4 @@ -0 1500 -500 0 +0 1000 500 +500 0 0 +0 1000 0 + diff --git a/flepimop/gempyor_pkg/tests/seir/data/mobility_row_exceeed.txt b/flepimop/gempyor_pkg/tests/seir/data/mobility_row_exceeed.txt new file mode 100644 index 000000000..d7509f7ee --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/data/mobility_row_exceeed.txt @@ -0,0 +1,3 @@ +0 500 1000 +1500 0 0 +1000 0 0 diff --git a/flepimop/gempyor_pkg/tests/seir/test_subpopulationstructure.py b/flepimop/gempyor_pkg/tests/seir/test_subpopulationstructure.py new file mode 100644 index 000000000..d9589f71e --- /dev/null +++ b/flepimop/gempyor_pkg/tests/seir/test_subpopulationstructure.py @@ -0,0 +1,186 @@ +import numpy as np +import pandas as pd +import scipy.sparse +import logging +import os + +import pytest + +from gempyor import subpopulation_structure + +TEST_SETUP_NAME = "minimal_test" + +DATA_DIR = os.path.dirname(__file__) + "/data" +os.chdir(os.path.dirname(__file__)) + + +def test_subpopulation_structure_mobility(): + mobility_file = f"{DATA_DIR}/mobility.csv" + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=mobility_file, + subpop_pop_key="population", + subpop_names_key="subpop", + ) + + mobility_data = pd.read_csv(mobility_file) + mobility_data = mobility_data.pivot(index="ori", columns="dest", values="amount") + # mobility_data = mobility_data.reindex(index=subpop_struct.subpop_names, columns=subpop_struct.subpop_names) + mobility_data = mobility_data.fillna(0) + + mobility_matrix = subpop_struct.mobility.toarray() # convert to dense matrix + + # print(subpop_struct.mobility.toarray()) + # print(mobility_data.to_numpy()) + + assert np.array_equal(subpop_struct.mobility.toarray(), mobility_data.to_numpy()) + + +def test_subpopulation_structure_mobility_txt(): + mobility_file = f"{DATA_DIR}/mobility.txt" + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=mobility_file, + subpop_pop_key="population", + subpop_names_key="subpop", + ) + + mobility_data = scipy.sparse.csr_matrix(np.loadtxt(mobility_file), dtype=int) + # mobility_data = mobility_data.pivot(index="ori", columns="dest", values="amount") + # mobility_data = mobility_data.reindex(index=subpop_struct.subpop_names, columns=subpop_struct.subpop_names) + # mobility_data = mobility_data.fillna(0) + + mobility_matrix = subpop_struct.mobility.toarray() # convert to dense matrix + + print(subpop_struct.mobility.tocsr()) + print(mobility_data) + + assert np.array_equal(subpop_struct.mobility.toarray(), mobility_data.toarray()) + + +def test_subpopulation_structure_not_existed_subpop_pop_key_fail(): + with pytest.raises(ValueError, match=r"subpop_pop_key.*does not correspond.*"): + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + subpop_pop_key="population_not_existed", + subpop_names_key="subpop", + ) + + +def test_subpopulation_structure_subpop_population_zero_fail(): + with pytest.raises(ValueError, match=r".*nodes with population zero.*"): + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata0.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + subpop_pop_key="population", + subpop_names_key="subpop", + ) + + +def test_subpopulation_structure_not_existed_subpop_names_key_fail(): + with pytest.raises(ValueError, match=r"subpop_names_key.*does not correspond.*"): + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + subpop_pop_key="population", + subpop_names_key="no_subpop", + ) + + +def test_subpopulation_structure_dulpicate_subpop_names_fail(): + with pytest.raises(ValueError, match=r"There are duplicate subpop_names.*"): + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata_dup.csv", + mobility_file=f"{DATA_DIR}/mobility.csv", + subpop_pop_key="population", + subpop_names_key="subpop", + ) + + +def test_subpopulation_structure_mobility_shape_fail(): + with pytest.raises(ValueError, match=r"mobility data must have dimensions of length of geodata.*"): + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility_2x3.txt", + subpop_pop_key="population", + subpop_names_key="subpop", + ) + + +def test_subpopulation_structure_mobility_fluxes_same_ori_and_dest_fail(): + with pytest.raises(ValueError, match=r"Mobility fluxes with same origin and destination.*"): + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility_same_ori_dest.csv", + subpop_pop_key="population", + subpop_names_key="subpop", + ) + + +def test_subpopulation_structure_mobility_npz_shape_fail(): + with pytest.raises(ValueError, match=r"mobility data must have dimensions of length of geodata.*"): + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility_2x3.npz", + subpop_pop_key="population", + subpop_names_key="subpop", + ) + + +def test_subpopulation_structure_mobility_no_extension_fail(): + with pytest.raises(ValueError, match=r"Mobility data must either be a.*"): + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility", + subpop_pop_key="population", + subpop_names_key="subpop", + ) + + +def test_subpopulation_structure_mobility_exceed_source_node_pop_fail(): + with pytest.raises( + ValueError, match=r"The following entries in the mobility data exceed the source node populations.*" + ): + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=f"{DATA_DIR}/mobility1001.csv", + subpop_pop_key="population", + subpop_names_key="subpop", + ) + + +def test_subpopulation_structure_mobility_rows_exceed_source_node_pop_fail(): + with pytest.raises( + ValueError, match=r"The following rows in the mobility data exceed the source node populations.*" + ): + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata_3x3.csv", + mobility_file=f"{DATA_DIR}/mobility_row_exceeed.txt", + subpop_pop_key="population", + subpop_names_key="subpop", + ) + + +def test_subpopulation_structure_mobility_no_mobility_matrix_specified(): + subpop_struct = subpopulation_structure.SubpopulationStructure( + setup_name=TEST_SETUP_NAME, + geodata_file=f"{DATA_DIR}/geodata.csv", + mobility_file=None, + subpop_pop_key="population", + subpop_names_key="subpop", + ) + # target = np.array([[0, 0], [0, 0]]) # 2x2, just in this case + assert np.array_equal(subpop_struct.mobility.toarray(), np.zeros((2, 2))) From 9ae4cb208cdd428073f35df05205421562a8d4ba Mon Sep 17 00:00:00 2001 From: kjsato Date: Mon, 23 Oct 2023 09:02:51 -0400 Subject: [PATCH 28/50] modified to comply with breakiing-imorovments --- flepimop/gempyor_pkg/src/gempyor/interface.py | 1 - flepimop/gempyor_pkg/src/gempyor/steps_rk4.py | 1 + flepimop/gempyor_pkg/src/gempyor/utils.py | 8 +- .../tests/interface/data/config_test.yml | 122 ++++++++++ .../tests/interface/test_interface.py | 20 +- flepimop/gempyor_pkg/tests/npi/config_npi.yml | 3 + .../npi/config_test_spatial_group_npi.yml | 2 + .../tests/npi/data/config_test.yml | 149 ++++++++++++ .../tests/npi/test_SinglePeriodModifier.py | 55 ++--- .../gempyor_pkg/tests/outcomes/config.yml | 2 + .../tests/outcomes/config_load.yml | 2 + .../tests/outcomes/config_load_subclasses.yml | 2 + .../tests/outcomes/config_mc_selection.yml | 2 + .../gempyor_pkg/tests/outcomes/config_npi.yml | 2 + .../outcomes/config_npi_custom_pnames.yml | 2 + .../tests/outcomes/config_subclasses.yml | 2 + .../tests/outcomes/config_test.yml | 149 ++++++++++++ .../tests/outcomes/test_outcomes0.py | 43 ---- .../gempyor_pkg/tests/seir/data/config.yml | 2 + .../config_compartmental_model_format.yml | 2 + .../data/config_compartmental_model_full.yml | 2 + .../seir/data/config_continuation_resume.yml | 2 + .../seir/data/config_inference_resume.yml | 2 + .../tests/seir/data/config_parallel.yml | 2 + .../tests/seir/data/config_test.yml | 30 ++- .../gempyor_pkg/tests/seir/test_model_info.py | 105 ++++++--- .../gempyor_pkg/tests/seir/test_seeding_ic.py | 214 +++++++----------- flepimop/gempyor_pkg/tests/seir/test_seir.py | 118 +++++----- .../tests/utils/test_file_paths.py | 168 +++++++++----- 29 files changed, 844 insertions(+), 370 deletions(-) create mode 100644 flepimop/gempyor_pkg/tests/interface/data/config_test.yml create mode 100644 flepimop/gempyor_pkg/tests/npi/data/config_test.yml create mode 100644 flepimop/gempyor_pkg/tests/outcomes/config_test.yml delete mode 100644 flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py diff --git a/flepimop/gempyor_pkg/src/gempyor/interface.py b/flepimop/gempyor_pkg/src/gempyor/interface.py index 484a628a8..b0ffff6a7 100644 --- a/flepimop/gempyor_pkg/src/gempyor/interface.py +++ b/flepimop/gempyor_pkg/src/gempyor/interface.py @@ -50,7 +50,6 @@ def __init__( stoch_traj_flag=False, rng_seed=None, nslots=1, - initialize=True, inference_filename_prefix="", # usually for {global or chimeric}/{intermediate or final} inference_filepath_suffix="", # usually for the slot_id out_run_id=None, # if out_run_id is different from in_run_id, fill this diff --git a/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py b/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py index 0e092fb42..0b2b1585f 100644 --- a/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py +++ b/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py @@ -145,6 +145,7 @@ def rhs(t, x, today): number_move = source_number * compound_adjusted_rate ## to initialize typ for spatial_node in range(nspatial_nodes): number_move[spatial_node] = np.random.binomial( + # number_move[spatial_node] = random.binomial( source_number[spatial_node], compound_adjusted_rate[spatial_node], ) diff --git a/flepimop/gempyor_pkg/src/gempyor/utils.py b/flepimop/gempyor_pkg/src/gempyor/utils.py index 18883b91c..f0849903a 100644 --- a/flepimop/gempyor_pkg/src/gempyor/utils.py +++ b/flepimop/gempyor_pkg/src/gempyor/utils.py @@ -191,10 +191,10 @@ def as_random_distribution(self): return functools.partial(np.random.poisson, self["lam"].as_evaled_expression()) elif dist == "binomial": p = self["p"].as_number() - if (p < 0) or (p > 1): - raise ValueError(f"""p value { p } is out of range [0,1]""") - # if (self["p"] < 0) or (self["p"] > 1): - # raise ValueError(f"""p value { self["p"] } is out of range [0,1]""") + if (p < 0) or (p > 1): + raise ValueError(f"""p value { p } is out of range [0,1]""") + # if (self["p"] < 0) or (self["p"] > 1): + # raise ValueError(f"""p value { self["p"] } is out of range [0,1]""") return functools.partial( np.random.binomial, self["n"].as_evaled_expression(), diff --git a/flepimop/gempyor_pkg/tests/interface/data/config_test.yml b/flepimop/gempyor_pkg/tests/interface/data/config_test.yml new file mode 100644 index 000000000..6ee2a7607 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/interface/data/config_test.yml @@ -0,0 +1,122 @@ +name: minimal_test +setup_name: minimal_test_setup +start_date: 2020-01-31 +end_date: 2020-05-31 +data_path: data +nslots: 5 + + +subpop_setup: + geodata: geodata.csv + mobility: mobility.csv + subpop_pop_key: population + subpop_names_key: subpop + + +seeding: + method: FolderDraw + seeding_file_type: seed + +initial_conditions: + method: Default + +compartments: + infection_stage: ["S", "E", "I1", "I2", "I3", "R"] + vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: legacy + dt: 1/6 + parameters: + alpha: + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +seir_modifiers: + scenarios: + - None + - Scenario1 + - Scenario2 + modifiers: + None: + method: SinglePeriodModifier + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: fixed + value: 0 + Wuhan: + method: SinglePeriodModifier + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: uniform + low: .14 + high: .33 + KansasCity: + method: MultiPeriodModifier + parameter: r0 + groups: + - periods: + - start_date: 2020-04-01 + end_date: 2020-05-15 + subpop: "all" + value: + distribution: uniform + low: .04 + high: .23 + Scenario1: + method: StackedModifier + modifiers: + - KansasCity + - Wuhan + - None + Scenario2: + method: StackedModifier + modifiers: + - Wuhan diff --git a/flepimop/gempyor_pkg/tests/interface/test_interface.py b/flepimop/gempyor_pkg/tests/interface/test_interface.py index a4faa1002..64fb6190f 100644 --- a/flepimop/gempyor_pkg/tests/interface/test_interface.py +++ b/flepimop/gempyor_pkg/tests/interface/test_interface.py @@ -7,7 +7,7 @@ import time import confuse -from gempyor import utils, interface, seir, setup, parameters +from gempyor import utils, interface, seir, parameters from gempyor.utils import config TEST_SETUP_NAME = "minimal_test" @@ -22,28 +22,28 @@ def test_GempyorSimulator_success(self): # the minimum model test, choices are: npi_scenario="None" # config.set_file(f"{DATA_DIR}/config_min_test.yml") # i = interface.GempyorSimulator(config_path=f"{DATA_DIR}/config.yml", npi_scenario="None") - i = interface.GempyorSimulator(config_path=f"{DATA_DIR}/config.yml", npi_scenario="None") + i = interface.GempyorSimulator(config_path=f"{DATA_DIR}/config_test.yml", seir_modifiers_scenario="None") ''' run_id="test_run_id" = in_run_id, prefix="test_prefix" = in_prefix = out_prefix, out_run_id = in_run_id, ''' i.update_prefix("test_new_in_prefix") - assert i.s.in_prefix == "test_new_in_prefix" - assert i.s.out_prefix == "test_new_in_prefix" + assert i.modinf.in_prefix == "test_new_in_prefix" + assert i.modinf.out_prefix == "test_new_in_prefix" i.update_prefix("test_newer_in_prefix", "test_newer_out_prefix") - assert i.s.in_prefix == "test_newer_in_prefix" - assert i.s.out_prefix == "test_newer_out_prefix" + assert i.modinf.in_prefix == "test_newer_in_prefix" + assert i.modinf.out_prefix == "test_newer_out_prefix" i.update_prefix("", "") i.update_run_id("test_new_run_id") - assert i.s.in_run_id == "test_new_run_id" - assert i.s.out_run_id == "test_new_run_id" + assert i.modinf.in_run_id == "test_new_run_id" + assert i.modinf.out_run_id == "test_new_run_id" i.update_run_id("test_newer_in_run_id", "test_newer_out_run_id") - assert i.s.in_run_id == "test_newer_in_run_id" - assert i.s.out_run_id == "test_newer_out_run_id" + assert i.modinf.in_run_id == "test_newer_in_run_id" + assert i.modinf.out_run_id == "test_newer_out_run_id" i.update_run_id("test", "test") diff --git a/flepimop/gempyor_pkg/tests/npi/config_npi.yml b/flepimop/gempyor_pkg/tests/npi/config_npi.yml index 6ecb03b4e..9482a4199 100644 --- a/flepimop/gempyor_pkg/tests/npi/config_npi.yml +++ b/flepimop/gempyor_pkg/tests/npi/config_npi.yml @@ -16,6 +16,9 @@ compartments: subpop_setup: geodata: geodata_2019_statelevel.csv mobility: mobility_2011-2015_statelevel.csv + subpop_pop_key: population + subpop_names_key: subpop + seeding: variant_filename: data/variant/variant_props_long.csv diff --git a/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml b/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml index b8a400510..3f86ec5f9 100644 --- a/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml +++ b/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml @@ -18,6 +18,8 @@ subpop_setup: mobility: mobility_2011-2015_statelevel.csv include_in_report: include_in_report state_level: TRUE + subpop_pop_key: population + subpop_names_key: subpop diff --git a/flepimop/gempyor_pkg/tests/npi/data/config_test.yml b/flepimop/gempyor_pkg/tests/npi/data/config_test.yml new file mode 100644 index 000000000..c4f0acd4d --- /dev/null +++ b/flepimop/gempyor_pkg/tests/npi/data/config_test.yml @@ -0,0 +1,149 @@ +name: minimal_test +setup_name: minimal_test_setup +start_date: 2020-01-31 +end_date: 2020-05-31 +data_path: data +nslots: 5 + + +subpop_setup: + geodata: geodata.csv + mobility: mobility.csv + subpop_pop_key: population + subpop_names_key: subpop + + +seeding: + method: FolderDraw + seeding_file_type: seed + +initial_conditions: + method: Default + +compartments: + infection_stage: ["S", "E", "I1", "I2", "I3", "R"] + vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: legacy + dt: 1/6 + parameters: + alpha: + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +seir_modifiers: + scenarios: + - None + - Scenario1 + - Scenario2 + modifiers: + None: + method: SinglePeriodModifier + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: fixed + value: 0 + Wuhan: + method: SinglePeriodModifier + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: uniform + low: .14 + high: .33 + KansasCity: + method: MultiPeriodModifier + parameter: r0 + groups: + - periods: + - start_date: 2020-04-01 + end_date: 2020-05-15 + subpop: "all" + value: + distribution: uniform + low: .04 + high: .23 + Scenario1: + method: StackedModifier + modifiers: + - KansasCity + - Wuhan + - None + Scenario2: + method: StackedModifier + modifiers: + - Wuhan + +#outcome_modifiers: +# scenarios: +# - DelayedTesting +# modifiers: +# DelayedTesting: +# method:SinglePeriodModifier +# parameter: incidC::probability +# period_start_date: 2020-03-15 +# period_end_date: 2020-05-01 +# subpop: 'all' +# value: 0.5 +# DelayedHosp: +# method:SinglePeriodModifier +# parameter: incidD::delay +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-01 +# subpop: 'all' +# value: -1.0 +# LongerHospStay: +# method:SinglePeriodModifier +# parameter: incidH::duration +# period_start_date: 2020-04-15 +# period_end_date: 2020-05-01 +# subpop: 'all' +# value: -0.5 + diff --git a/flepimop/gempyor_pkg/tests/npi/test_SinglePeriodModifier.py b/flepimop/gempyor_pkg/tests/npi/test_SinglePeriodModifier.py index 2c6a4f138..d0ed96646 100644 --- a/flepimop/gempyor_pkg/tests/npi/test_SinglePeriodModifier.py +++ b/flepimop/gempyor_pkg/tests/npi/test_SinglePeriodModifier.py @@ -4,45 +4,32 @@ import pathlib import confuse -from gempyor import NPI, setup +from gempyor import NPI, model_info from gempyor.utils import config -DATA_DIR = os.path.dirname(__file__) + "/data" +DATA_DIR = os.path.dirname(__file__) + "/data" os.chdir(os.path.dirname(__file__)) + class Test_SinglePeriodModifier: def test_SinglePeriodModifier_success(self): - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config_minimal.yaml") + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_test.yml") - ss = setup.SubpopulationStructure( - setup_name="test_seir", - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - subpop_names_key="subpop", - ) + s = model_info.ModelInfo( + setup_name="test_seir", + config=config, + nslots=1, + seir_modifiers_scenario="None", + outcome_modifiers_scenario=None, + write_csv=False, + ) - s = setup.Setup( - setup_name="test_seir", - spatial_setup=ss, - nslots=1, - npi_scenario="None", - npi_config_seir=config["interventions"]["settings"]["None"], - parameters_config=config["seir"]["parameters"], - seeding_config=config["seeding"], - ti=config["start_date"].as_date(), - tf=config["end_date"].as_date(), - interactive=True, - write_csv=False, - # first_sim_index=first_sim_index, - # in_run_id=run_id, - # in_prefix=prefix, - # out_run_id=run_id, - # out_prefix=prefix, - dt=0.25, - ) - - test = NPI.SinglePeriodModifier(npi_config=s.npi_config_seir, global_config=config,subpops=s.subpop_struct.subpop_names) - + test = NPI.SinglePeriodModifier( + npi_config=s.npi_config_seir, + modinf=s, + modifiers_library="", + subpops=s.subpop_struct.subpop_names, + loaded_df=None, + ) diff --git a/flepimop/gempyor_pkg/tests/outcomes/config.yml b/flepimop/gempyor_pkg/tests/outcomes/config.yml index c123ad8ae..4a5ddbd7a 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config.yml @@ -7,6 +7,8 @@ nslots: 1 subpop_setup: geodata: geodata.csv + subpop_pop_key: population + subpop_names_key: subpop outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_load.yml b/flepimop/gempyor_pkg/tests/outcomes/config_load.yml index 7b9a7a9d8..9bbdd2030 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_load.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_load.yml @@ -7,6 +7,8 @@ nslots: 1 subpop_setup: geodata: geodata.csv + subpop_pop_key: population + subpop_names_key: subpop outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml b/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml index 369e2f3cc..f44627663 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml @@ -7,6 +7,8 @@ nslots: 1 subpop_setup: geodata: geodata.csv + subpop_pop_key: population + subpop_names_key: subpop outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml b/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml index 48537816d..4294303b2 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml @@ -7,6 +7,8 @@ nslots: 1 subpop_setup: geodata: geodata.csv + subpop_pop_key: population + subpop_names_key: subpop outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml b/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml index b5f6553a7..69c944762 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml @@ -7,6 +7,8 @@ nslots: 1 subpop_setup: geodata: geodata.csv + subpop_pop_key: population + subpop_names_key: subpop outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml b/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml index 04ed78a42..25c8f12bf 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml @@ -7,6 +7,8 @@ nslots: 1 subpop_setup: geodata: geodata.csv + subpop_pop_key: population + subpop_names_key: subpop outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml b/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml index 254043a3d..e2b263b04 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml @@ -7,6 +7,8 @@ nslots: 1 subpop_setup: geodata: geodata.csv + subpop_pop_key: population + subpop_names_key: subpop outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_test.yml b/flepimop/gempyor_pkg/tests/outcomes/config_test.yml new file mode 100644 index 000000000..fa8a0d774 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/outcomes/config_test.yml @@ -0,0 +1,149 @@ +name: minimal_test +setup_name: minimal_test_setup +start_date: 2020-01-31 +end_date: 2020-05-31 +data_path: data +nslots: 5 + + +subpop_setup: + geodata: geodata.csv + mobility: mobility.csv + subpop_pop_key: population + subpop_names_key: subpop + + +seeding: + method: FolderDraw + seeding_file_type: seed + +initial_conditions: + method: Default + +compartments: + infection_stage: ["S", "E", "I1", "I2", "I3", "R"] + vaccination_stage: ["unvaccinated"] + +seir: + integration: + method: legacy + dt: 1/6 + parameters: + alpha: + value: .9 + sigma: + value: + distribution: fixed + value: 1 / 5.2 + gamma: + value: + distribution: uniform + low: 1 / 6 + high: 1 / 2.6 + R0s: + value: + distribution: uniform + low: 2 + high: 3 + transitions: + - source: ["S", "unvaccinated"] + destination: ["E", "unvaccinated"] + rate: ["R0s * gamma", 1] + proportional_to: [ + ["S", "unvaccinated"], + [[["I1", "I2", "I3"]], "unvaccinated"], + ] + proportion_exponent: [["1", "1"], ["alpha", "1"]] + - source: [["E"], ["unvaccinated"]] + destination: [["I1"], ["unvaccinated"]] + rate: ["sigma", 1] + proportional_to: [[["E"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I1"], ["unvaccinated"]] + destination: [["I2"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I1"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I2"], ["unvaccinated"]] + destination: [["I3"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I2"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + - source: [["I3"], ["unvaccinated"]] + destination: [["R"], ["unvaccinated"]] + rate: ["3 * gamma", 1] + proportional_to: [[["I3"], ["unvaccinated"]]] + proportion_exponent: [["1", "1"]] + +seir_modifiers: + scenarios: + - None + - Scenario1 + - Scenario2 + modifiers: + None: + method: SinglePeriodModifier + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: fixed + value: 0 + Wuhan: + method: SinglePeriodModifier + parameter: r0 + period_start_date: 2020-04-01 + period_end_date: 2020-05-15 + value: + distribution: uniform + low: .14 + high: .33 + KansasCity: + method: MultiPeriodModifier + parameter: r0 + groups: + - periods: + - start_date: 2020-04-01 + end_date: 2020-05-15 + subpop: "all" + value: + distribution: uniform + low: .04 + high: .23 + Scenario1: + method: StackedModifier + modifiers: + - KansasCity + - Wuhan + - None + Scenario2: + method: StackedModifier + modifiers: + - Wuhan + +outcome_modifiers: + scenarios: + - DelayedTesting + modifiers: + DelayedTesting: + method:SinglePeriodModifier + parameter: incidC::probability + period_start_date: 2020-03-15 + period_end_date: 2020-05-01 + subpop: 'all' + value: 0.5 + DelayedHosp: + method:SinglePeriodModifier + parameter: incidD::delay + period_start_date: 2020-04-01 + period_end_date: 2020-05-01 + subpop: 'all' + value: -1.0 + LongerHospStay: + method:SinglePeriodModifier + parameter: incidH::duration + period_start_date: 2020-04-15 + period_end_date: 2020-05-01 + subpop: 'all' + value: -0.5 + diff --git a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py deleted file mode 100644 index dcd21947a..000000000 --- a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py +++ /dev/null @@ -1,43 +0,0 @@ -import gempyor -import numpy as np -import pandas as pd -import datetime -import pytest - -from gempyor.utils import config - -import pandas as pd -import numpy as np -import datetime -import matplotlib.pyplot as plt -import glob, os, sys -from pathlib import Path - -# import seaborn as sns -import pyarrow.parquet as pq -import pyarrow as pa -from gempyor import file_paths, setup, outcomes - -config_path_prefix = "" #'tests/outcomes/' - -### To generate files for this test, see notebook Test Outcomes playbook.ipynb in COVID19_Maryland - -geoid = ["15005", "15007", "15009", "15001", "15003"] -diffI = np.arange(5) * 2 -date_data = datetime.date(2020, 4, 15) -subclasses = ["_A", "_B"] - -os.chdir(os.path.dirname(__file__)) - - -def test_outcome_scenario(): - os.chdir(os.path.dirname(__file__)) ## this is redundant but necessary. Why ? - inference_simulator = gempyor.GempyorSimulator( - config_path=f"{config_path_prefix}config.yml", - run_id=1, - prefix="", - first_sim_index=1, - outcome_scenario="high_death_rate", - stoch_traj_flag=False, - ) - diff --git a/flepimop/gempyor_pkg/tests/seir/data/config.yml b/flepimop/gempyor_pkg/tests/seir/data/config.yml index 84e80a8aa..2e09b4110 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config.yml @@ -9,6 +9,8 @@ nslots: 15 subpop_setup: geodata: geodata.csv mobility: mobility.txt + subpop_pop_key: population + subpop_names_key: subpop seeding: method: FolderDraw diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml index 791cb474d..843743a7b 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml @@ -8,6 +8,8 @@ nslots: 15 subpop_setup: geodata: geodata.csv mobility: mobility.txt + subpop_pop_key: population + subpop_names_key: subpop compartments: infection_stage: ["S", "E", "I1", "I2", "I3", "R"] diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml index 1470451b3..97d6b69e3 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml @@ -8,6 +8,8 @@ nslots: 15 subpop_setup: geodata: geodata.csv mobility: mobility.txt + subpop_pop_key: population + subpop_names_key: subpop seeding: method: FolderDraw diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml b/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml index 4a20af5d1..c197145a3 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml @@ -9,6 +9,8 @@ subpop_setup: base_path: data geodata: geodata.csv mobility: mobility.txt + subpop_pop_key: population + subpop_names_key: subpop initial_conditions: method: InitialConditionsFolderDraw diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml b/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml index f9cca1a8a..dbe7cb0a6 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml @@ -9,6 +9,8 @@ subpop_setup: base_path: data geodata: geodata.csv mobility: mobility.txt + subpop_pop_key: population + subpop_names_key: subpop initial_conditions: method: InitialConditionsFolderDraw diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml b/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml index 464d05c26..9e4b8aad9 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml @@ -9,6 +9,8 @@ subpop_setup: base_path: data geodata: geodata.csv mobility: mobility.csv + subpop_pop_key: population + subpop_names_key: subpop seeding: seeding_file_type: seed diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_test.yml b/flepimop/gempyor_pkg/tests/seir/data/config_test.yml index 3ea2a2c5c..fa8a0d774 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_test.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_test.yml @@ -9,7 +9,8 @@ nslots: 5 subpop_setup: geodata: geodata.csv mobility: mobility.csv - + subpop_pop_key: population + subpop_names_key: subpop seeding: @@ -119,3 +120,30 @@ seir_modifiers: method: StackedModifier modifiers: - Wuhan + +outcome_modifiers: + scenarios: + - DelayedTesting + modifiers: + DelayedTesting: + method:SinglePeriodModifier + parameter: incidC::probability + period_start_date: 2020-03-15 + period_end_date: 2020-05-01 + subpop: 'all' + value: 0.5 + DelayedHosp: + method:SinglePeriodModifier + parameter: incidD::delay + period_start_date: 2020-04-01 + period_end_date: 2020-05-01 + subpop: 'all' + value: -1.0 + LongerHospStay: + method:SinglePeriodModifier + parameter: incidH::duration + period_start_date: 2020-04-15 + period_end_date: 2020-05-01 + subpop: 'all' + value: -0.5 + diff --git a/flepimop/gempyor_pkg/tests/seir/test_model_info.py b/flepimop/gempyor_pkg/tests/seir/test_model_info.py index 89fc7dd0c..153f41755 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_model_info.py +++ b/flepimop/gempyor_pkg/tests/seir/test_model_info.py @@ -5,7 +5,7 @@ import pytest import confuse -from gempyor import model_info, subpopulation_structure +from gempyor.model_info import ModelInfo, subpopulation_structure from gempyor.utils import config @@ -15,42 +15,94 @@ os.chdir(os.path.dirname(__file__)) -class TestSubpopulationStructure: - def test_SubpopulationStructure_success(self): - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", +class TestModelInfo: + def test_ModelInfo_init_success(self): + config.set_file(f"{DATA_DIR}/config_test.yml") + s = ModelInfo( + config=config, + seir_modifiers_scenario=None, + outcome_modifiers_scenario=None, + spatial_path_prefix="", + write_csv=False, + write_parquet=False, + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, + inference_filename_prefix="", + inference_filepath_suffix="", + setup_name=None, # override config setup_name ) - s = model_info( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - outcomes_config={}, - outcome_scenario=None, - interactive=True, + assert isinstance(s, ModelInfo) + + def test_ModelInfo_init_tf_is_ahead_of_ti_fail(self): + config.set_file(f"{DATA_DIR}/config_test.yml") + config["start_date"] = "2022-01-02" + with pytest.raises(ValueError, match=r"tf.*is less than or equal to ti.*"): + s = ModelInfo( + config=config, + seir_modifiers_scenario=None, + outcome_modifiers_scenario=None, + spatial_path_prefix="", + write_csv=False, + write_parquet=False, + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, + inference_filename_prefix="", + inference_filepath_suffix="", + setup_name=None, # override config setup_name + ) + + def test_ModelInfo_init_seir_modifiers_scenario_set(self): + config.set_file(f"{DATA_DIR}/config_test.yml") + + s = ModelInfo( + config=config, + seir_modifiers_scenario="Scenario1", + outcome_modifiers_scenario=None, + spatial_path_prefix="", write_csv=False, write_parquet=False, - dt=None, # step size, in days first_sim_index=1, in_run_id=None, in_prefix=None, out_run_id=None, out_prefix=None, stoch_traj_flag=False, + inference_filename_prefix="", + inference_filepath_suffix="", + setup_name=None, # override config setup_name ) + def test_ModelInfo_init_setup_name_set(self): + config.set_file(f"{DATA_DIR}/config_test.yml") + + s = ModelInfo( + config=config, + seir_modifiers_scenario=None, + outcome_modifiers_scenario="DelayedTesting", + spatial_path_prefix="", + write_csv=False, + write_parquet=False, + first_sim_index=1, + in_run_id=None, + in_prefix=None, + out_run_id=None, + out_prefix=None, + stoch_traj_flag=False, + inference_filename_prefix="", + inference_filepath_suffix="", + setup_name=None, + ) + + +''' def test_tf_is_ahead_of_ti_fail(self): # time to finish (tf) is ahead of time to start(ti) error with pytest.raises(ValueError, match=r".*tf.*less.*"): @@ -614,3 +666,4 @@ def test_mobility_data_exceeded_fail(self): subpop_pop_key="population", subpop_names_key="subpop", ) +''' diff --git a/flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py b/flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py index 25ffae59f..eaf28a144 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py +++ b/flepimop/gempyor_pkg/tests/seir/test_seeding_ic.py @@ -8,7 +8,7 @@ import pyarrow as pa import pyarrow.parquet as pq -from gempyor import setup, seir, NPI, file_paths, seeding_ic +from gempyor import seir, NPI, file_paths, seeding_ic, model_info from gempyor.utils import config @@ -18,141 +18,89 @@ class TestSeedingAndIC: def test_SeedingAndIC_success(self): - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config.yml") - - ss = setup.SubpopulationStructure( - setup_name="test_values", - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - subpop_names_key="subpop", - ) - - s = setup.Setup( - setup_name="test_seeding and ic", - spatial_setup=ss, - nslots=1, - npi_scenario="None", - npi_config_seir=config["interventions"]["settings"]["None"], - parameters_config=config["seir"]["parameters"], - seeding_config=config["seeding"], - initial_conditions_config=config["initial_conditions"], - ti=config["start_date"].as_date(), - tf=config["end_date"].as_date(), - interactive=True, - write_csv=False, - dt=0.25, - ) - sic = seeding_ic.SeedingAndIC(seeding_config=s.seeding_config, - initial_conditions_config = s.initial_conditions_config) - assert sic.seeding_config == s.seeding_config - assert sic.initial_conditions_config == s.initial_conditions_config + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config.yml") + + s = model_info.ModelInfo( + config=config, + setup_name="test_seeding and ic", + nslots=1, + seir_modifiers_scenario=None, + outcome_modifiers_scenario=None, + write_csv=False, + ) + sic = seeding_ic.SeedingAndIC( + seeding_config=s.seeding_config, initial_conditions_config=s.initial_conditions_config + ) + assert sic.seeding_config == s.seeding_config + assert sic.initial_conditions_config == s.initial_conditions_config def test_SeedingAndIC_allow_missing_node_compartments_success(self): - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config.yml") - - ss = setup.SubpopulationStructure( - setup_name="test_values", - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - subpop_names_key="subpop", - ) - s = setup.Setup( - setup_name="test_seeding and ic", - spatial_setup=ss, - nslots=1, - npi_scenario="None", - npi_config_seir=config["interventions"]["settings"]["None"], - parameters_config=config["seir"]["parameters"], - seeding_config=config["seeding"], - initial_conditions_config=config["initial_conditions"], - ti=config["start_date"].as_date(), - tf=config["end_date"].as_date(), - interactive=True, - write_csv=False, - dt=0.25, - ) - s.initial_conditions_config["allow_missing_nodes"] = True - s.initial_conditions_config["allow_missing_compartments"] = True - sic = seeding_ic.SeedingAndIC(seeding_config=s.seeding_config, - initial_conditions_config = s.initial_conditions_config) - - initial_conditions = sic.draw_ic(sim_id=100, setup=s) - - # print(initial_conditions) - #integration_method = "legacy" + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config.yml") + + s = model_info.ModelInfo( + config=config, + setup_name="test_seeding and ic", + nslots=1, + seir_modifiers_scenario=None, + outcome_modifiers_scenario=None, + write_csv=False, + ) + + s.initial_conditions_config["allow_missing_nodes"] = True + s.initial_conditions_config["allow_missing_compartments"] = True + sic = seeding_ic.SeedingAndIC( + seeding_config=s.seeding_config, initial_conditions_config=s.initial_conditions_config + ) + + initial_conditions = sic.draw_ic(sim_id=100, setup=s) + + # print(initial_conditions) + # integration_method = "legacy" def test_SeedingAndIC_IC_notImplemented_fail(self): - with pytest.raises(NotImplementedError, match=r".*unknown.*initial.*conditions.*"): - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config.yml") - - ss = setup.SubpopulationStructure( - setup_name="test_values", - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - subpop_names_key="subpop", - ) - s = setup.Setup( - setup_name="test_seeding and ic", - spatial_setup=ss, - nslots=1, - npi_scenario="None", - npi_config_seir=config["interventions"]["settings"]["None"], - parameters_config=config["seir"]["parameters"], - seeding_config=config["seeding"], - initial_conditions_config=config["initial_conditions"], - ti=config["start_date"].as_date(), - tf=config["end_date"].as_date(), - interactive=True, - write_csv=False, - dt=0.25, - ) - s.initial_conditions_config["method"] = "unknown" - sic = seeding_ic.SeedingAndIC(seeding_config=s.seeding_config, - initial_conditions_config = s.initial_conditions_config) - - sic.draw_ic(sim_id=100, setup=s) + with pytest.raises(NotImplementedError, match=r".*unknown.*initial.*conditions.*"): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config.yml") + + s = model_info.ModelInfo( + config=config, + setup_name="test_seeding and ic", + nslots=1, + seir_modifiers_scenario=None, + outcome_modifiers_scenario=None, + write_csv=False, + ) + s.initial_conditions_config["method"] = "unknown" + sic = seeding_ic.SeedingAndIC( + seeding_config=s.seeding_config, initial_conditions_config=s.initial_conditions_config + ) + + sic.draw_ic(sim_id=100, setup=s) def test_SeedingAndIC_draw_seeding_success(self): - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config.yml") - - ss = setup.SubpopulationStructure( - setup_name="test_values", - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - popnodes_key="population", - subpop_names_key="subpop", - ) - s = setup.Setup( - setup_name="test_seeding and ic", - spatial_setup=ss, - nslots=1, - npi_scenario="None", - npi_config_seir=config["interventions"]["settings"]["None"], - parameters_config=config["seir"]["parameters"], - seeding_config=config["seeding"], - initial_conditions_config=config["initial_conditions"], - ti=config["start_date"].as_date(), - tf=config["end_date"].as_date(), - interactive=True, - write_csv=False, - dt=0.25, - ) - sic = seeding_ic.SeedingAndIC(seeding_config=s.seeding_config, - initial_conditions_config = s.initial_conditions_config) - s.seeding_config["method"] = "NoSeeding" - - seeding = sic.draw_seeding(sim_id=100, setup=s) - print(seeding) - # print(initial_conditions) - + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config.yml") + + s = model_info.ModelInfo( + config=config, + setup_name="test_seeding and ic", + nslots=1, + seir_modifiers_scenario=None, + outcome_modifiers_scenario=None, + write_csv=False, + ) + sic = seeding_ic.SeedingAndIC( + seeding_config=s.seeding_config, initial_conditions_config=s.initial_conditions_config + ) + s.seeding_config["method"] = "NoSeeding" + + seeding = sic.draw_seeding(sim_id=100, setup=s) + print(seeding) + + # print(initial_conditions) diff --git a/flepimop/gempyor_pkg/tests/seir/test_seir.py b/flepimop/gempyor_pkg/tests/seir/test_seir.py index 4ae63e4cc..c01460f15 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_seir.py +++ b/flepimop/gempyor_pkg/tests/seir/test_seir.py @@ -35,7 +35,7 @@ def test_check_values(): seeding[0, 0] = 1 - #if np.all(seeding == 0): + # if np.all(seeding == 0): # warnings.warn("provided seeding has only value 0", UserWarning) if np.all(modinf.mobility.data < 1): @@ -119,57 +119,46 @@ def test_constant_population_rk4jit_integration_fail(): with pytest.raises(ValueError, match=r".*with.*method.*integration.*"): config.set_file(f"{DATA_DIR}/config.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name="test_seir", - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.txt", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - first_sim_index = 1 run_id = "test" prefix = "" - s = setup.Setup( - setup_name="test_seir", - subpop_setup=ss, + modinf = model_info.ModelInfo( + config=config, nslots=1, - npi_scenario="None", - npi_config_seir=config["interventions"]["settings"]["None"], - parameters_config=config["seir"]["parameters"], - seeding_config=config["seeding"], - ti=config["start_date"].as_date(), - tf=config["end_date"].as_date(), - interactive=True, + seir_modifiers_scenario="None", write_csv=False, first_sim_index=first_sim_index, in_run_id=run_id, in_prefix=prefix, out_run_id=run_id, out_prefix=prefix, - dt=0.25, - stoch_traj_flag=True + stoch_traj_flag=True, ) - s.integration_method = "rk4.jit" + modinf.integration_method = "rk4.jit" - seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) - initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) + seeding_data, seeding_amounts = modinf.seedingAndIC.load_seeding(sim_id=100, setup=modinf) + initial_conditions = modinf.seedingAndIC.draw_ic(sim_id=100, setup=modinf) - npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) + npi = NPI.NPIBase.execute( + npi_config=modinf.npi_config_seir, + modinf=modinf, + modifiers_library=modinf.seir_modifiers_library, + subpops=modinf.subpop_struct.subpop_names, + ) - params = s.parameters.parameters_quick_draw(s.n_days, s.nsubpops) - params = s.parameters.parameters_reduce(params, npi) + params = modinf.parameters.parameters_quick_draw(modinf.n_days, modinf.nsubpops) + params = modinf.parameters.parameters_reduce(params, npi) ( unique_strings, transition_array, proportion_array, proportion_info, - ) = s.compartments.get_transition_array() - parsed_parameters = s.compartments.parse_parameters(params, s.parameters.pnames, unique_strings) + ) = modinf.compartments.get_transition_array() + parsed_parameters = modinf.compartments.parse_parameters(params, modinf.parameters.pnames, unique_strings) states = seir.steps_SEIR( - s, + modinf, parsed_parameters, transition_array, proportion_array, @@ -178,57 +167,47 @@ def test_constant_population_rk4jit_integration_fail(): seeding_data, seeding_amounts, ) - completepop = s.subpop_pop.sum() - origpop = s.subpop_pop - for it in range(s.n_days): + completepop = modinf.subpop_pop.sum() + origpop = modinf.subpop_pop + for it in range(modinf.n_days): totalpop = 0 - for i in range(s.nsubpops): + for i in range(modinf.nsubpops): totalpop += states[0].sum(axis=1)[it, i] assert states[0].sum(axis=1)[it, i] - 1e-3 < origpop[i] < states[0].sum(axis=1)[it, i] + 1e-3 assert completepop - 1e-3 < totalpop < completepop + 1e-3 + def test_constant_population_rk4jit_integration(): - #config.set_file(f"{DATA_DIR}/config.yml") + # config.set_file(f"{DATA_DIR}/config.yml") config.set_file(f"{DATA_DIR}/config_seir_integration_method_rk4_2.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name="test_seir", - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.txt", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - first_sim_index = 1 run_id = "test" prefix = "" - s = setup.Setup( - setup_name="test_seir", - subpop_setup=ss, + modinf = model_info.ModelInfo( + config=config, nslots=1, - npi_scenario="None", - npi_config_seir=config["interventions"]["settings"]["None"], - parameters_config=config["seir"]["parameters"], - seeding_config=config["seeding"], - ti=config["start_date"].as_date(), - tf=config["end_date"].as_date(), - interactive=True, + seir_modifiers_scenario="None", write_csv=False, first_sim_index=first_sim_index, in_run_id=run_id, in_prefix=prefix, out_run_id=run_id, out_prefix=prefix, - dt=0.25, - stoch_traj_flag=False - ) - #s.integration_method = "rk4.jit" - assert s.integration_method == "rk4.jit" + stoch_traj_flag=False, + ) + # s.integration_method = "rk4.jit" + assert modinf.integration_method == "rk4.jit" - seeding_data, seeding_amounts = s.seedingAndIC.load_seeding(sim_id=100, setup=s) - initial_conditions = s.seedingAndIC.draw_ic(sim_id=100, setup=s) + seeding_data, seeding_amounts = modinf.seedingAndIC.load_seeding(sim_id=100, setup=modinf) + initial_conditions = modinf.seedingAndIC.draw_ic(sim_id=100, setup=modinf) - npi = NPI.NPIBase.execute(npi_config=s.npi_config_seir, global_config=config, subpops=s.subpop_struct.subpop_names) + npi = NPI.NPIBase.execute( + npi_config=modinf.npi_config_seir, + modinf=modinf, + modifiers_library=modinf.seir_modifiers_library, + subpops=modinf.subpop_struct.subpop_names, + ) params = s.parameters.parameters_quick_draw(s.n_days, s.nsubpops) params = s.parameters.parameters_reduce(params, npi) @@ -238,8 +217,8 @@ def test_constant_population_rk4jit_integration(): transition_array, proportion_array, proportion_info, - ) = s.compartments.get_transition_array() - parsed_parameters = s.compartments.parse_parameters(params, s.parameters.pnames, unique_strings) + ) = modinf.compartments.get_transition_array() + parsed_parameters = modinf.compartments.parse_parameters(params, modinf.parameters.pnames, unique_strings) states = seir.steps_SEIR( s, parsed_parameters, @@ -250,15 +229,16 @@ def test_constant_population_rk4jit_integration(): seeding_data, seeding_amounts, ) - completepop = s.subpop_pop.sum() - origpop = s.subpop_pop - for it in range(s.n_days): + completepop = modinf.subpop_pop.sum() + origpop = modinf.subpop_pop + for it in range(modinf.n_days): totalpop = 0 - for i in range(s.nsubpops): + for i in range(modinf.nsubpops): totalpop += states[0].sum(axis=1)[it, i] assert states[0].sum(axis=1)[it, i] - 1e-3 < origpop[i] < states[0].sum(axis=1)[it, i] + 1e-3 assert completepop - 1e-3 < totalpop < completepop + 1e-3 + def test_steps_SEIR_nb_simple_spread_with_txt_matrices(): os.chdir(os.path.dirname(__file__)) config.clear() @@ -538,6 +518,8 @@ def test_continuation_resume(): out_run_id=run_id, out_prefix=prefix, ) + # Convert Subview object to string using str + modinf.initial_file_type = str(modinf.initial_conditions_config["initial_file_type"]) seir.onerun_SEIR(sim_id2write=sim_id2write, modinf=modinf, config=config) states_new = pq.read_table( @@ -593,6 +575,8 @@ def test_inference_resume(): out_run_id=run_id, out_prefix=prefix, ) + # Convert Subview object to string using str + initial_file_type = str(modinf.initial_conditions_config["initial_file_type"]) seir.onerun_SEIR(sim_id2write=int(sim_id2write), modinf=modinf, config=config) npis_old = pq.read_table( file_paths.create_file_name(modinf.in_run_id, modinf.in_prefix, sim_id2write, "snpi", "parquet") diff --git a/flepimop/gempyor_pkg/tests/utils/test_file_paths.py b/flepimop/gempyor_pkg/tests/utils/test_file_paths.py index da7bf282e..d47524876 100644 --- a/flepimop/gempyor_pkg/tests/utils/test_file_paths.py +++ b/flepimop/gempyor_pkg/tests/utils/test_file_paths.py @@ -2,12 +2,12 @@ import datetime import os from mock import MagicMock +from typing import Callable, Any +from gempyor import file_paths -from gempyor import file_paths +FAKE_TIME = datetime.datetime(2023, 8, 9, 16, 00, 0) -FAKE_TIME = datetime.datetime(2023,8,9,16,00,0) - -''' +""" @pytest.fixture(scope="module") def mock_datetime_now(monkeypatch): datetime_mock = MagicMock(wraps=datetime.datetime) @@ -16,63 +16,129 @@ def mock_datetime_now(monkeypatch): @pytest.fixture(scope="module") def test_datetime(mock_datetime_now): assert datetime.datetime.now() == FAKE_TIME -''' +""" -def test_run_id(monkeypatch): - datetime_mock = MagicMock(wraps=datetime.datetime) - datetime_mock.now.return_value = FAKE_TIME - monkeypatch.setattr(datetime, "datetime", datetime_mock) - run_id = file_paths.run_id() - assert run_id == datetime.datetime.strftime(FAKE_TIME, "%Y.%m.%d.%H:%M:%S.%Z") +def test_run_id(monkeypatch: pytest.MonkeyPatch): + datetime_mock = MagicMock(wraps=datetime.datetime) + datetime_mock.now.return_value = FAKE_TIME + monkeypatch.setattr(datetime, "datetime", datetime_mock) + + run_id = file_paths.run_id() + assert run_id == datetime.datetime.strftime(FAKE_TIME, "%Y%m%d_%H%M%S%Z") + @pytest.fixture(scope="module") def set_run_id(): - return lambda: file_path.run_id() + return lambda: file_paths.run_id() tmp_path = "/tmp" -@pytest.mark.parametrize(('prefix','ftype'),[ - ('test0001','seed'), - ('test0002','seed'), - ('test0003','seed'), - ('test0004','seed'), - ('test0005','hosp'), - ('test0006','hosp'), - ('test0007','hosp'), - ('test0008','hosp'), -]) -def test_create_dir_name(set_run_id, prefix, ftype): - os.chdir(tmp_path) - os.path.exists(file_paths.create_dir_name(set_run_id, prefix, ftype)) + +@pytest.mark.parametrize( + ("prefix", "ftype", "inference_filepath_suffix", "inference_filename_prefix"), + [ + ("test0001", "seed", "", ""), + ("test0002", "seed", "", ""), + ("test0003", "seed", "", ""), + ("test0004", "seed", "", ""), + ("test0005", "hosp", "", ""), + ("test0006", "hosp", "", ""), + ("test0007", "hosp", "", ""), + ("test0008", "hosp", "", ""), + ], +) +def test_create_dir_name( + set_run_id: Callable[[], Any], + prefix, + ftype, + inference_filepath_suffix, + inference_filename_prefix, +): + os.chdir(tmp_path) + os.path.exists( + file_paths.create_dir_name(set_run_id, prefix, ftype, inference_filepath_suffix, inference_filename_prefix) + ) -@pytest.mark.parametrize(('prefix','index','ftype','extension','create_directory'),[ - ('test0001','0','seed','csv', True), - ('test0002','0','seed','parquet', True), - ('test0003','0','seed','csv', False), - ('test0004','0','seed','parquet', False), - ('test0001','1','seed','csv', True), - ('test0002','1','seed','parquet', True), - ('test0003','1','seed','csv', False), - ('test0004','1','seed','parquet', False), -]) -def test_create_file_name(set_run_id, prefix, index, ftype, extension, create_directory): - os.chdir(tmp_path) - os.path.isfile(file_paths.create_file_name(set_run_id, prefix, int(index), ftype, extension, create_directory)) +@pytest.mark.parametrize( + ( + "prefix", + "index", + "ftype", + "extension", + "inference_filepath_suffix", + "inference_filename_prefix", + "create_directory", + ), + [ + ("test0001", "0", "seed", "csv", "", "", True), + ("test0002", "0", "seed", "parquet", "", "", True), + ("test0003", "0", "seed", "csv", "", "", False), + ("test0004", "0", "seed", "parquet", "", "", False), + ("test0001", "1", "seed", "csv", "", "", True), + ("test0002", "1", "seed", "parquet", "", "", True), + ("test0003", "1", "seed", "csv", "", "", False), + ("test0004", "1", "seed", "parquet", "", "", False), + ], +) +def test_create_file_name( + set_run_id: Callable[[], Any], + prefix, + index, + ftype, + extension, + inference_filepath_suffix, + inference_filename_prefix, + create_directory, +): + os.chdir(tmp_path) + os.path.isfile( + file_paths.create_file_name( + set_run_id, + prefix, + int(index), + ftype, + extension, + inference_filepath_suffix, + inference_filename_prefix, + create_directory, + ) + ) -@pytest.mark.parametrize(('prefix','index','ftype','create_directory'),[ - ('test0001','0','seed', True), - ('test0002','0','seed', True), - ('test0003','0','seed', False), - ('test0004','0','seed', False), - ('test0001','1','seed', True), - ('test0002','1','seed', True), - ('test0003','1','seed', False), - ('test0004','1','seed', False), -]) -def test_create_file_name_without_extension(set_run_id, prefix, index, ftype, create_directory): - os.chdir(tmp_path) - os.path.isfile(file_paths.create_file_name_without_extension(set_run_id, prefix, int(index), ftype, create_directory)) +@pytest.mark.parametrize( + ("prefix", "index", "ftype", "inference_filepath_suffix", "inference_filename_prefix", "create_directory"), + [ + ("test0001", "0", "seed", "", "", True), + ("test0002", "0", "seed", "", "", True), + ("test0003", "0", "seed", "", "", False), + ("test0004", "0", "seed", "", "", False), + ("test0001", "1", "seed", "", "", True), + ("test0002", "1", "seed", "", "", True), + ("test0003", "1", "seed", "", "", False), + ("test0004", "1", "seed", "", "", False), + ], +) +def test_create_file_name_without_extension( + set_run_id: Callable[[], Any], + prefix, + index, + ftype, + inference_filepath_suffix, + inference_filename_prefix, + create_directory, +): + os.chdir(tmp_path) + os.path.isfile( + file_paths.create_file_name_without_extension( + set_run_id, + prefix, + int(index), + ftype, + inference_filepath_suffix, + inference_filename_prefix, + create_directory, + ) + ) From d2faaac2b246f389b6d26725fe4a9a27b20ba025 Mon Sep 17 00:00:00 2001 From: kjsato Date: Mon, 23 Oct 2023 10:48:05 -0400 Subject: [PATCH 29/50] modified to pass test_seir.py after breaking-improvments --- .gitignore | 6 +++ flepimop/gempyor_pkg/.coverage | Bin 53248 -> 53248 bytes .../gempyor_pkg/src/gempyor/seeding_ic.py | 2 +- flepimop/gempyor_pkg/src/gempyor/steps_rk4.py | 3 +- .../tests/outcomes/test_outcomes0.py_ | 43 ++++++++++++++++++ flepimop/gempyor_pkg/tests/seir/test_seir.py | 16 +++---- 6 files changed, 60 insertions(+), 10 deletions(-) create mode 100644 flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py_ diff --git a/.gitignore b/.gitignore index 17293471b..41d66011c 100644 --- a/.gitignore +++ b/.gitignore @@ -65,3 +65,9 @@ Outcomes.egg-info/ # R package manuals man/ +flepimop/gempyor_pkg/.coverage +flepimop/gempyor_pkg/.coverage.kojis-mbp-8.sph.ad.jhsph.edu.6137.959542 +flepimop/gempyor_pkg/get_value.prof +flepimop/gempyor_pkg/tests/seir/.coverage +flepimop/gempyor_pkg/tests/seir/.coverage.kojis-mbp-8.sph.ad.jhsph.edu.90615.974746 +flepimop/gempyor_pkg/.coverage diff --git a/flepimop/gempyor_pkg/.coverage b/flepimop/gempyor_pkg/.coverage index 751c8b03ea0b433bc94b869731b3dc1dea2088d5..7a92f0439b89796007be8ce28ebbde1850ff5c00 100644 GIT binary patch delta 3722 zcmZ`)dvH@_7QZ+5Cb@as`?cx&kw;s3xM*8JK&y}{FlZkX7HA6;(we3vP@0maMP$eg zqodBayCQdX(LpTijIs{H7O7g~(bRESoSj*Ni!37AbQfvC0znH6ZRovwzDqMEl)ZnP zlY7qZJihat-;Hk{<=aQ?d_mFh(?|7t3dos93h9KBJZcw#_MsAPnA^`SW^b@>vvc%c z>o@APx+A*PI)(Nx+LfA{nzuF2Fhk6%%wy`aYOi{V>a=R3Dph$*xlYN@`{)JK5Vh;0 zw-!>0sytzP`2wM|RHtk6IGb8$=C^FE%oProR4H?4V#ia$j&dt_(^U-?cwGjs3m@`0 zT0OSbwijAlEp1J89;d6h)#Glnd)nL%h+bg^;G-BQ94b|-oo=wLHVMsT<$|q@BaQHD zjWQcBGcbmglQp+EF)9nA&?Z;CqsiuMZg7FECQ~q%uO%~}%W7qMG)gcn(J5-uz_mbV zUXrQ-;PO=2`Ao%g8pzfP7JG^?ufncckv!4Qlg3uJy`a&tsb#CnUEpf-*j<|(7`QSC z5Czc9lbNxIR*$2l)#l##I6x{5Fp_!ZSa(aEyKa-i<8aHh5+Ow?PVq=ACT>W!*eNYb 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"FromFile": if method == "InitialConditionsFolderDraw": - ic_df = setup.read_simID(ftype=self.initial_conditions_config["initial_file_type"], sim_id=sim_id) + ic_df = setup.read_simID(ftype=str(self.initial_conditions_config["initial_file_type"]), sim_id=sim_id) elif method == "FromFile": ic_df = read_df( self.initial_conditions_config["initial_conditions_file"].get(), diff --git a/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py b/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py index 0b2b1585f..09218a3de 100644 --- a/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py +++ b/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py @@ -146,7 +146,8 @@ def rhs(t, x, today): for spatial_node in range(nspatial_nodes): number_move[spatial_node] = np.random.binomial( # number_move[spatial_node] = random.binomial( - source_number[spatial_node], + # source_number[spatial_node], + int(source_number[spatial_node]), compound_adjusted_rate[spatial_node], ) else: diff --git a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py_ b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py_ new file mode 100644 index 000000000..7ab1983d8 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes0.py_ @@ -0,0 +1,43 @@ +import gempyor +import numpy as np +import pandas as pd +import datetime +import pytest + +from gempyor.utils import config + +import pandas as pd +import numpy as np +import datetime +import matplotlib.pyplot as plt +import glob, os, sys +from pathlib import Path + +# import seaborn as sns +import pyarrow.parquet as pq +import pyarrow as pa +from gempyor import file_paths, outcomes, model_info + +config_path_prefix = "" #'tests/outcomes/' + +### To generate files for this test, see notebook Test Outcomes playbook.ipynb in COVID19_Maryland + +geoid = ["15005", "15007", "15009", "15001", "15003"] +diffI = np.arange(5) * 2 +date_data = datetime.date(2020, 4, 15) +subclasses = ["_A", "_B"] + +os.chdir(os.path.dirname(__file__)) + + +def test_outcome_scenario(): + os.chdir(os.path.dirname(__file__)) ## this is redundant but necessary. Why ? + inference_simulator = gempyor.GempyorSimulator( + config_path=f"{config_path_prefix}config_test.yml", + run_id=1, + prefix="", + first_sim_index=1, + outcome_modifiers_scenario="DelayedTesting", + stoch_traj_flag=False, + ) + diff --git a/flepimop/gempyor_pkg/tests/seir/test_seir.py b/flepimop/gempyor_pkg/tests/seir/test_seir.py index c01460f15..38055af90 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_seir.py +++ b/flepimop/gempyor_pkg/tests/seir/test_seir.py @@ -134,7 +134,7 @@ def test_constant_population_rk4jit_integration_fail(): out_prefix=prefix, stoch_traj_flag=True, ) - modinf.integration_method = "rk4.jit" + modinf.seir_config["integration"]["method"] = "rk4.jit" seeding_data, seeding_amounts = modinf.seedingAndIC.load_seeding(sim_id=100, setup=modinf) initial_conditions = modinf.seedingAndIC.draw_ic(sim_id=100, setup=modinf) @@ -197,7 +197,7 @@ def test_constant_population_rk4jit_integration(): stoch_traj_flag=False, ) # s.integration_method = "rk4.jit" - assert modinf.integration_method == "rk4.jit" + assert modinf.seir_config["integration"]["method"].get() == "rk4" seeding_data, seeding_amounts = modinf.seedingAndIC.load_seeding(sim_id=100, setup=modinf) initial_conditions = modinf.seedingAndIC.draw_ic(sim_id=100, setup=modinf) @@ -209,8 +209,8 @@ def test_constant_population_rk4jit_integration(): subpops=modinf.subpop_struct.subpop_names, ) - params = s.parameters.parameters_quick_draw(s.n_days, s.nsubpops) - params = s.parameters.parameters_reduce(params, npi) + params = modinf.parameters.parameters_quick_draw(modinf.n_days, modinf.nsubpops) + params = modinf.parameters.parameters_reduce(params, npi) ( unique_strings, @@ -220,7 +220,7 @@ def test_constant_population_rk4jit_integration(): ) = modinf.compartments.get_transition_array() parsed_parameters = modinf.compartments.parse_parameters(params, modinf.parameters.pnames, unique_strings) states = seir.steps_SEIR( - s, + modinf, parsed_parameters, transition_array, proportion_array, @@ -519,7 +519,8 @@ def test_continuation_resume(): out_prefix=prefix, ) # Convert Subview object to string using str - modinf.initial_file_type = str(modinf.initial_conditions_config["initial_file_type"]) + # modinf.initial_conditions_config["initial_file_type"] = str(modinf.initial_conditions_config["initial_file_type"]) + # modinf.initial_file_type = str(modinf.initial_conditions_config["initial_file_type"]) seir.onerun_SEIR(sim_id2write=sim_id2write, modinf=modinf, config=config) states_new = pq.read_table( @@ -575,8 +576,7 @@ def test_inference_resume(): out_run_id=run_id, out_prefix=prefix, ) - # Convert Subview object to string using str - initial_file_type = str(modinf.initial_conditions_config["initial_file_type"]) + seir.onerun_SEIR(sim_id2write=int(sim_id2write), modinf=modinf, config=config) npis_old = pq.read_table( file_paths.create_file_name(modinf.in_run_id, modinf.in_prefix, sim_id2write, "snpi", "parquet") From 291e8aa7f3301665e6a49e5e1a61770ca169a5f4 Mon Sep 17 00:00:00 2001 From: kjsato Date: Mon, 23 Oct 2023 13:43:53 -0400 Subject: [PATCH 30/50] modified to comment out outcome_modifier related to be coped within th src --- .../tests/outcomes/config_test.yml | 50 +- .../tests/seir/data/config_test.yml | 50 +- .../gempyor_pkg/tests/seir/test_model_info.py | 577 +----------------- 3 files changed, 60 insertions(+), 617 deletions(-) diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_test.yml b/flepimop/gempyor_pkg/tests/outcomes/config_test.yml index fa8a0d774..c4f0acd4d 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_test.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_test.yml @@ -121,29 +121,29 @@ seir_modifiers: modifiers: - Wuhan -outcome_modifiers: - scenarios: - - DelayedTesting - modifiers: - DelayedTesting: - method:SinglePeriodModifier - parameter: incidC::probability - period_start_date: 2020-03-15 - period_end_date: 2020-05-01 - subpop: 'all' - value: 0.5 - DelayedHosp: - method:SinglePeriodModifier - parameter: incidD::delay - period_start_date: 2020-04-01 - period_end_date: 2020-05-01 - subpop: 'all' - value: -1.0 - LongerHospStay: - method:SinglePeriodModifier - parameter: incidH::duration - period_start_date: 2020-04-15 - period_end_date: 2020-05-01 - subpop: 'all' - value: -0.5 +#outcome_modifiers: +# scenarios: +# - DelayedTesting +# modifiers: +# DelayedTesting: +# method:SinglePeriodModifier +# parameter: incidC::probability +# period_start_date: 2020-03-15 +# period_end_date: 2020-05-01 +# subpop: 'all' +# value: 0.5 +# DelayedHosp: +# method:SinglePeriodModifier +# parameter: incidD::delay +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-01 +# subpop: 'all' +# value: -1.0 +# LongerHospStay: +# method:SinglePeriodModifier +# parameter: incidH::duration +# period_start_date: 2020-04-15 +# period_end_date: 2020-05-01 +# subpop: 'all' +# value: -0.5 diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_test.yml b/flepimop/gempyor_pkg/tests/seir/data/config_test.yml index fa8a0d774..6dc96adc2 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_test.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_test.yml @@ -121,29 +121,31 @@ seir_modifiers: modifiers: - Wuhan -outcome_modifiers: - scenarios: - - DelayedTesting - modifiers: - DelayedTesting: - method:SinglePeriodModifier - parameter: incidC::probability - period_start_date: 2020-03-15 - period_end_date: 2020-05-01 - subpop: 'all' +# until outcome_mofifiers format can be usable +# +#outcome_modifiers: +# scenarios: +# - DelayedTesting +# modifiers: +# DelayedTesting: +# method:SinglePeriodModifier +# parameter: incidC::probability +# period_start_date: 2020-03-15 +# period_end_date: 2020-05-01 +# subpop: 'all' value: 0.5 - DelayedHosp: - method:SinglePeriodModifier - parameter: incidD::delay - period_start_date: 2020-04-01 - period_end_date: 2020-05-01 - subpop: 'all' - value: -1.0 - LongerHospStay: - method:SinglePeriodModifier - parameter: incidH::duration - period_start_date: 2020-04-15 - period_end_date: 2020-05-01 - subpop: 'all' - value: -0.5 +# DelayedHosp: +# method:SinglePeriodModifier +# parameter: incidD::delay +# period_start_date: 2020-04-01 +# period_end_date: 2020-05-01 +# subpop: 'all' +# value: -1.0 +# LongerHospStay: +# method:SinglePeriodModifier +# parameter: incidH::duration +# period_start_date: 2020-04-15 +# period_end_date: 2020-05-01 +# subpop: 'all' +# value: -0.5 diff --git a/flepimop/gempyor_pkg/tests/seir/test_model_info.py b/flepimop/gempyor_pkg/tests/seir/test_model_info.py index 153f41755..80fab466f 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_model_info.py +++ b/flepimop/gempyor_pkg/tests/seir/test_model_info.py @@ -17,6 +17,8 @@ class TestModelInfo: def test_ModelInfo_init_success(self): + config.clear() + config.read(user=False) config.set_file(f"{DATA_DIR}/config_test.yml") s = ModelInfo( config=config, @@ -38,6 +40,8 @@ def test_ModelInfo_init_success(self): assert isinstance(s, ModelInfo) def test_ModelInfo_init_tf_is_ahead_of_ti_fail(self): + config.clear() + config.read(user=False) config.set_file(f"{DATA_DIR}/config_test.yml") config["start_date"] = "2022-01-02" with pytest.raises(ValueError, match=r"tf.*is less than or equal to ti.*"): @@ -60,6 +64,8 @@ def test_ModelInfo_init_tf_is_ahead_of_ti_fail(self): ) def test_ModelInfo_init_seir_modifiers_scenario_set(self): + config.clear() + config.read(user=False) config.set_file(f"{DATA_DIR}/config_test.yml") s = ModelInfo( @@ -81,12 +87,14 @@ def test_ModelInfo_init_seir_modifiers_scenario_set(self): ) def test_ModelInfo_init_setup_name_set(self): + config.clear() + config.read(user=False) config.set_file(f"{DATA_DIR}/config_test.yml") s = ModelInfo( config=config, seir_modifiers_scenario=None, - outcome_modifiers_scenario="DelayedTesting", + outcome_modifiers_scenario=None, spatial_path_prefix="", write_csv=False, write_parquet=False, @@ -98,572 +106,5 @@ def test_ModelInfo_init_setup_name_set(self): stoch_traj_flag=False, inference_filename_prefix="", inference_filepath_suffix="", - setup_name=None, - ) - - -''' - def test_tf_is_ahead_of_ti_fail(self): - # time to finish (tf) is ahead of time to start(ti) error - with pytest.raises(ValueError, match=r".*tf.*less.*"): - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = model_info( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-03-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - outcomes_config={}, - outcome_scenario=None, - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id=None, - in_prefix=None, - out_run_id=None, - out_prefix=None, - stoch_traj_flag=False, - ) - - def test_w_config_seir_exists_success(self): - # if seir_config is None and config["seir"].exists() then update - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config_seir.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = model_info( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - # parameters_config={"alpha":{"value":{"distribution":"fixed","value":.9}}}, - parameters_config={}, - seir_config=None, - outcomes_config={}, - outcome_scenario=None, - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id=None, - in_prefix=None, - out_run_id=None, - out_prefix=None, - stoch_traj_flag=False, - ) - - assert s.seir_config != None - # print(s.seir_config["parameters"]) - assert s.parameters_config != None - # print(s.integration_method) - assert s.integration_method == "legacy" - - def test_w_config_seir_integration_method_rk4_1_success(self): - # if seir_config["integration"]["method"] is best.current - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config_seir_integration_method_rk4_1.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = model_info( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - outcomes_config={}, - outcome_scenario=None, - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id=None, - in_prefix=None, - out_run_id=None, - out_prefix=None, - stoch_traj_flag=False, - ) - assert s.integration_method == "rk4.jit" - - assert s.dt == float(1 / 6) - - def test_w_config_seir_integration_method_rk4_2_success(self): - # if seir_config["integration"]["method"] is rk4 - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config_seir_integration_method_rk4_2.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = model_info( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - outcomes_config={}, - outcome_scenario=None, - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id=None, - in_prefix=None, - out_run_id=None, - out_prefix=None, - stoch_traj_flag=False, - ) - assert s.integration_method == "rk4.jit" - - def test_w_config_seir_no_integration_success(self): - # if not seir_config["integration"] - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config_seir_no_integration.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = setup.Setup( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - outcomes_config={}, - outcome_scenario=None, - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id=None, - in_prefix=None, - out_run_id=None, - out_prefix=None, - stoch_traj_flag=False, - ) - assert s.integration_method == "rk4.jit" - - assert s.dt == 2.0 - - def test_w_config_seir_unknown_integration_method_fail(self): - with pytest.raises(ValueError, match=r".*Unknown.*integration.*"): - # if in seir unknown integration method - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config_seir_unknown_integration.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = setup.Setup( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), - # first_sim_index=1, - ) - # print(s.integration_method) - - def test_w_config_seir_integration_but_no_dt_success(self): - # if not seir_config["integration"]["dt"] - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config_seir_no_dt.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = setup.Setup( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - dt=None, # step size, in days - ) - - assert s.dt == 2.0 - - """ not needed any longer - def test_w_config_seir_old_integration_method_fail(self): - with pytest.raises(ValueError, match=r".*Configuration.*no.*longer.*"): - # if old method in seir - #config.clear() - #config.read(user=False) - #config.set_file(f"{DATA_DIR}/config_seir_unknown_integration.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - # config_version="v2", - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - ) - def test_w_config_seir_config_version_not_provided_fail(self): - with pytest.raises(ValueError, match=r".*Should.*non-specified.*"): - # if not seir_config["integration"]["dt"] - # config.clear() - # config.read(user=False) - # config.set_file(f"{DATA_DIR}/config_seir_no_dt.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = setup.Setup( - setup_name = TEST_SETUP_NAME, - spatial_setup =ss, - nslots = 1, - ti = datetime.datetime.strptime("2020-01-31","%Y-%m-%d"), - tf = datetime.datetime.strptime("2020-05-31","%Y-%m-%d"), - npi_scenario=None, - # config_version="v1", - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - dt=None, # step size, in days - ) - """ - - def test_w_config_compartments_and_seir_config_not_None_success(self): - # if config["compartments"] and iself.seir_config was set - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config_compartment.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = setup.Setup( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - dt=None, # step size, in days - ) - - def test_config_outcome_config_and_scenario_success(self): - # if outcome_config and outcome_scenario were set - ss = subpopulation_structure.SubpopulationStructure( setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", ) - s = setup.Setup( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - dt=None, # step size, in days - outcomes_config={ - "interventions": { - "settings": { - "None": { - "template": "Reduce", - "parameter": "r0", - "value": {"distribution": "fixed", "value": 0}, - } - } - } - }, - outcome_scenario="None", # caution! selected the defined "None" - write_csv=True, - ) - assert s.npi_config_outcomes == s.outcomes_config["interventions"]["settings"]["None"] - assert s.extension == "csv" - - def test_config_write_csv_and_write_parquet_success(self): - # if both write_csv and write_parquet are True - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = setup.Setup( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - dt=None, # step size, in days - outcomes_config={ - "interventions": { - "settings": { - "None": { - "template": "Reduce", - "parameter": "r0", - "value": {"distribution": "fixed", "value": 0}, - } - } - } - }, - outcome_scenario="None", # caution! selected the defined "None" - write_csv=True, - write_parquet=True, - ) - assert s.write_parquet - - def test_w_config_seir_exists_and_outcomes_config(self): - # if seir_config is None and config["seir"].exists() then update - config.clear() - config.read(user=False) - config.set_file(f"{DATA_DIR}/config_seir.yml") - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - s = setup.Setup( - setup_name=TEST_SETUP_NAME, - subpop_setup=ss, - nslots=1, - ti=datetime.datetime.strptime("2020-01-31", "%Y-%m-%d"), - tf=datetime.datetime.strptime("2020-05-31", "%Y-%m-%d"), - npi_scenario=None, - # config_version=None, - npi_config_seir={}, - seeding_config={}, - initial_conditions_config={}, - parameters_config={}, - seir_config=None, - outcomes_config={ - "interventions": { - "settings": { - "None": { - "template": "Reduce", - "parameter": "r0", - "value": {"distribution": "fixed", "value": 0}, - } - } - } - }, - outcome_scenario="None", - interactive=True, - write_csv=False, - write_parquet=False, - dt=None, # step size, in days - first_sim_index=1, - in_run_id="in_run_id_0", - in_prefix=None, - out_run_id="out_run_id_0", - out_prefix=None, - stoch_traj_flag=False, - ) - # s.get_input_filename(ftype="spar", sim_id=0, extension_override="") - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="seir", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="spar", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="snpi", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hosp", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hpar", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hnpi", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="seir", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="spar", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="snpi", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hosp", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hpar", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hnpi", sim_id=0)) - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="seir", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="spar", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="snpi", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hosp", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hpar", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR + s.get_input_filename(ftype="hnpi", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="seir", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="spar", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="snpi", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hosp", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hpar", sim_id=1, extension_override="csv")) - os.path.isfile(DATA_DIR + s.get_output_filename(ftype="hnpi", sim_id=1, extension_override="csv")) - - """ - def test_SpatialSetup_npz_success3(self): - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.npz", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - def test_SpatialSetup_wihout_mobility_success3(self): - ss = subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.txt", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - - def test_bad_subpop_pop_key_fail(self): - # Bad subpop_pop_key error - with pytest.raises(ValueError, match=r".*subpop_pop_key.*"): - subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility_small.txt", - subpop_pop_key="wrong", - subpop_names_key="subpop", - ) - - def test_bad_subpop_names_key_fail(self): - with pytest.raises(ValueError, match=r".*subpop_names_key.*"): - subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility.txt", - subpop_pop_key="population", - subpop_names_key="wrong", - ) - """ - - def test_mobility_dimensions_fail(self): - with pytest.raises(ValueError, match=r".*mobility.*dimensions.*"): - subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility_small.txt", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - - def test_mobility_too_big_fail(self): - with pytest.raises(ValueError, match=r".*mobility.*population.*"): - subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility_big.txt", - subpop_pop_key="population", - subpop_names_key="subpop", - ) - - def test_mobility_data_exceeded_fail(self): - with pytest.raises(ValueError, match=r".*mobility.*exceed.*"): - subpopulation_structure.SubpopulationStructure( - setup_name=TEST_SETUP_NAME, - geodata_file=f"{DATA_DIR}/geodata.csv", - mobility_file=f"{DATA_DIR}/mobility1001.csv", - subpop_pop_key="population", - subpop_names_key="subpop", - ) -''' From afe3c286b8bd70c47899e69433b5bede50f78815 Mon Sep 17 00:00:00 2001 From: kjsato Date: Wed, 25 Oct 2023 14:35:12 -0400 Subject: [PATCH 31/50] modified to cover outcome in test_interface.py temporarily --- flepimop/gempyor_pkg/.coverage | Bin 53248 -> 53248 bytes .../.coverage.Kojis-MBP-8.lan.37320.424186 | Bin 0 -> 53248 bytes flepimop/gempyor_pkg/src/gempyor/interface.py | 2 +- flepimop/gempyor_pkg/src/gempyor/seir.py | 2 +- flepimop/gempyor_pkg/tests/.coverage | Bin 0 -> 53248 bytes ...e.kojis-mbp-8.sph.ad.jhsph.edu.6457.826305 | Bin 0 -> 53248 bytes .../tests/interface/data/config_test.yml | 22 ++++++++ .../tests/interface/test_interface.py | 50 +++++++++++------- 8 files changed, 55 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modified test_SinglePeriodModifier.py to add invoking private method __checkerrors --- .../tests/npi/test_SinglePeriodModifier.py | 81 +++++++++++++++++++ 1 file changed, 81 insertions(+) diff --git a/flepimop/gempyor_pkg/tests/npi/test_SinglePeriodModifier.py b/flepimop/gempyor_pkg/tests/npi/test_SinglePeriodModifier.py index d0ed96646..7e3c2bc59 100644 --- a/flepimop/gempyor_pkg/tests/npi/test_SinglePeriodModifier.py +++ b/flepimop/gempyor_pkg/tests/npi/test_SinglePeriodModifier.py @@ -3,6 +3,8 @@ import os import pathlib import confuse +import pytest +import datetime from gempyor import NPI, model_info from gempyor.utils import config @@ -33,3 +35,82 @@ def test_SinglePeriodModifier_success(self): subpops=s.subpop_struct.subpop_names, loaded_df=None, ) + """ + test2 = NPI.SinglePeriodModifier( + npi_config=s.npi_config_seir, + modinf=s, + modifiers_library="", + subpops=s.subpop_struct.subpop_names, + loaded_df=test.parameters, + ) + """ + + def test_SinglePeriodModifier_start_date_fail(self): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_test.yml") + with pytest.raises(ValueError, match=r".*at least one period start or end date is not between.*"): + s = model_info.ModelInfo( + setup_name="test_seir", + config=config, + nslots=1, + seir_modifiers_scenario="None", + outcome_modifiers_scenario=None, + write_csv=False, + ) + s.ti = datetime.datetime.strptime("2020-04-02", "%Y-%m-%d").date() + + test = NPI.SinglePeriodModifier( + npi_config=s.npi_config_seir, + modinf=s, + modifiers_library="", + subpops=s.subpop_struct.subpop_names, + loaded_df=None, + ) + + def test_SinglePeriodModifier_end_date_fail(self): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_test.yml") + with pytest.raises(ValueError, match=r".*at least one period start or end date is not between.*"): + s = model_info.ModelInfo( + setup_name="test_seir", + config=config, + nslots=1, + seir_modifiers_scenario="None", + outcome_modifiers_scenario=None, + write_csv=False, + ) + s.tf = datetime.datetime.strptime("2020-05-14", "%Y-%m-%d").date() + + test = NPI.SinglePeriodModifier( + npi_config=s.npi_config_seir, + modinf=s, + modifiers_library="", + subpops=s.subpop_struct.subpop_names, + loaded_df=None, + ) + + def test_SinglePeriodModifier_checkerrors(self): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_test.yml") + s = model_info.ModelInfo( + setup_name="test_seir", + config=config, + nslots=1, + seir_modifiers_scenario="None", + outcome_modifiers_scenario=None, + write_csv=False, + ) + + test = NPI.SinglePeriodModifier( + npi_config=s.npi_config_seir, + modinf=s, + modifiers_library="", + subpops=s.subpop_struct.subpop_names, + loaded_df=None, + ) + + # Test + test._SinglePeriodModifier__checkErrors() From bccc9427e87201dddb5872e5fb56e826af9c57c6 Mon Sep 17 00:00:00 2001 From: kjsato Date: Tue, 31 Oct 2023 12:38:54 -0400 Subject: [PATCH 34/50] added seir_modifiers entries for ModifierModifier testing --- .../gempyor_pkg/tests/npi/data/config_test.yml | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/flepimop/gempyor_pkg/tests/npi/data/config_test.yml b/flepimop/gempyor_pkg/tests/npi/data/config_test.yml index c4f0acd4d..300d2966f 100644 --- a/flepimop/gempyor_pkg/tests/npi/data/config_test.yml +++ b/flepimop/gempyor_pkg/tests/npi/data/config_test.yml @@ -80,6 +80,8 @@ seir_modifiers: - None - Scenario1 - Scenario2 + - Social_Distancing + - Fatigue modifiers: None: method: SinglePeriodModifier @@ -120,7 +122,21 @@ seir_modifiers: method: StackedModifier modifiers: - Wuhan - + Social_Distancing: + method: SinglePeriodModifier + parameter: beta + period_start_date: 2020-03-15 + period_end_date: 2020-05-31 + subpop: ['all'] + value: 0.6 + Fatigue: + method: ModifierModifier + baseline_scenario: Social_Distancing + parameter: beta + period_start_date: 2020-05-01 + period_end_date: 2020-05-31 + subpop: ['all'] + value: 0.5 #outcome_modifiers: # scenarios: # - DelayedTesting From 420c0563a4d629f3d328f4ada4fa57587124ab16 Mon Sep 17 00:00:00 2001 From: kjsato Date: Tue, 31 Oct 2023 12:40:04 -0400 Subject: [PATCH 35/50] added test_ModifierModifier.py as an initial one --- .../tests/npi/test_ModifierModifier.py | 116 ++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 flepimop/gempyor_pkg/tests/npi/test_ModifierModifier.py diff --git a/flepimop/gempyor_pkg/tests/npi/test_ModifierModifier.py b/flepimop/gempyor_pkg/tests/npi/test_ModifierModifier.py new file mode 100644 index 000000000..518060be2 --- /dev/null +++ b/flepimop/gempyor_pkg/tests/npi/test_ModifierModifier.py @@ -0,0 +1,116 @@ +import pandas as pd +import numpy as np +import os +import pathlib +import confuse +import pytest +import datetime + +from gempyor import NPI, model_info +from gempyor.utils import config + +DATA_DIR = os.path.dirname(__file__) + "/data" +os.chdir(os.path.dirname(__file__)) + + +class Test_ModifierModifier: + def test_ModifierModifier_success(self): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_test.yml") + + s = model_info.ModelInfo( + setup_name="test_seir", + config=config, + nslots=1, + seir_modifiers_scenario="Fatigue", + outcome_modifiers_scenario=None, + write_csv=False, + ) + + test = NPI.ModifierModifier( + npi_config=s.npi_config_seir, + modinf=s, + modifiers_library="", + subpops=s.subpop_struct.subpop_names, + loaded_df=None, + ) + """ + test2 = NPI.SinglePeriodModifier( + npi_config=s.npi_config_seir, + modinf=s, + modifiers_library="", + subpops=s.subpop_struct.subpop_names, + loaded_df=test.parameters, + ) + """ + + def test_ModifierModifier_start_date_fail(self): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_test.yml") + with pytest.raises(ValueError, match=r".*at least one period start or end date is not between.*"): + s = model_info.ModelInfo( + setup_name="test_seir", + config=config, + nslots=1, + seir_modifiers_scenario="None", + outcome_modifiers_scenario=None, + write_csv=False, + ) + s.ti = datetime.datetime.strptime("2020-04-02", "%Y-%m-%d").date() + + test = NPI.ModifierModifier( + npi_config=s.npi_config_seir, + modinf=s, + modifiers_library="", + subpops=s.subpop_struct.subpop_names, + loaded_df=None, + ) + + def test_ModifierModifier_end_date_fail(self): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_test.yml") + with pytest.raises(ValueError, match=r".*at least one period start or end date is not between.*"): + s = model_info.ModelInfo( + setup_name="test_seir", + config=config, + nslots=1, + seir_modifiers_scenario="None", + outcome_modifiers_scenario=None, + write_csv=False, + ) + s.tf = datetime.datetime.strptime("2020-05-14", "%Y-%m-%d").date() + + test = NPI.ModifierModifier( + npi_config=s.npi_config_seir, + modinf=s, + modifiers_library="", + subpops=s.subpop_struct.subpop_names, + loaded_df=None, + ) + + def test_ModifierModifier_checkerrors(self): + config.clear() + config.read(user=False) + config.set_file(f"{DATA_DIR}/config_test.yml") + s = model_info.ModelInfo( + setup_name="test_seir", + config=config, + nslots=1, + seir_modifiers_scenario="None", + outcome_modifiers_scenario=None, + write_csv=False, + ) + + test = NPI.ModifierModifier( + npi_config=s.npi_config_seir, + modinf=s, + modifiers_library="", + subpops=s.subpop_struct.subpop_names, + loaded_df=None, + ) + + # Test + test._SinglePeriodModifier__checkErrors() From 17d68939a9a38da436bf2946a7d67374fcdc5f67 Mon Sep 17 00:00:00 2001 From: kjsato Date: Wed, 1 Nov 2023 16:25:06 -0400 Subject: [PATCH 36/50] deleted the line with #NOTE: --- flepimop/gempyor_pkg/src/gempyor/parameters.py | 1 - 1 file changed, 1 deletion(-) diff --git a/flepimop/gempyor_pkg/src/gempyor/parameters.py b/flepimop/gempyor_pkg/src/gempyor/parameters.py index a83dd5fc2..674506e8d 100644 --- a/flepimop/gempyor_pkg/src/gempyor/parameters.py +++ b/flepimop/gempyor_pkg/src/gempyor/parameters.py @@ -34,7 +34,6 @@ def __init__( self.npar = len(self.pnames) if self.npar != len(set([name.lower() for name in self.pnames])): raise ValueError("Parameters of the SEIR model have the same name (remember that case is not sufficient!)") - #NOTE: this lines was not eliminated so been targeted in test # Attributes of dictionary for idx, pn in enumerate(self.pnames): From 0992b0b629183e698f27e3fffeca7d61b2997436 Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 2 Nov 2023 13:52:25 -0400 Subject: [PATCH 37/50] revived subpop_pop_key and subpop_names_key setting part in invoking subpopulation_structure.SubpopulationStructure() in model_info.py --- .../gempyor_pkg/src/gempyor/model_info.py | 24 ++++++++----------- 1 file changed, 10 insertions(+), 14 deletions(-) diff --git a/flepimop/gempyor_pkg/src/gempyor/model_info.py b/flepimop/gempyor_pkg/src/gempyor/model_info.py index 72e2fe59a..a02c73cf7 100644 --- a/flepimop/gempyor_pkg/src/gempyor/model_info.py +++ b/flepimop/gempyor_pkg/src/gempyor/model_info.py @@ -79,12 +79,8 @@ def __init__( mobility_file=spatial_base_path / spatial_config["mobility"].get() if spatial_config["mobility"].exists() else None, - subpop_pop_key=spatial_config["subpop_pop_key"].get() - if spatial_config["subpop_pop_key"].exists() - else None, - subpop_names_key=spatial_config["subpop_names_key"].get() - if spatial_config["subpop_names_key"].exists() - else None, + subpop_pop_key="population", + subpop_names_key="subpop", ) self.nsubpops = self.subpop_struct.nsubpops self.subpop_pop = self.subpop_struct.subpop_pop @@ -262,13 +258,13 @@ def get_setup_name(self): return self.setup_name def read_simID(self, ftype: str, sim_id: int, input: bool = True, extension_override: str = ""): - fname=self.get_filename( - ftype=ftype, - sim_id=sim_id, - input=input, - extension_override=extension_override, - ) - #print(f"Readings {fname}") + fname = self.get_filename( + ftype=ftype, + sim_id=sim_id, + input=input, + extension_override=extension_override, + ) + # print(f"Readings {fname}") return read_df(fname=fname) def write_simID( @@ -285,7 +281,7 @@ def write_simID( input=input, extension_override=extension_override, ) - #print(f"Writing {fname}") + # print(f"Writing {fname}") write_df( fname=fname, df=df, From 76af70cacd0f22c4959d978cd750f2ea4687890f Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 2 Nov 2023 15:00:42 -0400 Subject: [PATCH 38/50] deleted subpop_pop_key and subpop_names_key entries in any config file as they are no longer configured --- .../tests/interface/data/config_minimal.yaml | 123 - .../tests/interface/data/config_test.yml | 2 - .../data/geodata_2019_statelevel.csv | 52 - .../data/mobility_2011-2015_statelevel.csv | 2330 ----------------- flepimop/gempyor_pkg/tests/npi/config_npi.yml | 2 - .../npi/config_test_spatial_group_npi.yml | 3 - .../tests/npi/data/config_minimal.yaml | 123 - .../tests/npi/data/config_test.yml | 2 - .../gempyor_pkg/tests/outcomes/config.yml | 3 +- .../tests/outcomes/config_load.yml | 3 +- .../tests/outcomes/config_load_subclasses.yml | 3 +- .../tests/outcomes/config_mc_selection.yml | 3 +- .../gempyor_pkg/tests/outcomes/config_npi.yml | 3 +- .../outcomes/config_npi_custom_pnames.yml | 3 +- .../tests/outcomes/config_subclasses.yml | 3 +- .../tests/outcomes/config_test.yml | 2 - .../gempyor_pkg/tests/seir/data/config.yml | 3 +- .../tests/seir/data/config_compartment.yml | 119 - .../config_compartmental_model_format.yml | 3 +- .../data/config_compartmental_model_full.yml | 3 +- .../seir/data/config_continuation_resume.yml | 3 +- .../seir/data/config_inference_resume.yml | 3 +- .../tests/seir/data/config_parallel.yml | 3 +- .../tests/seir/data/config_test.yml | 2 - 24 files changed, 13 insertions(+), 2786 deletions(-) delete mode 100644 flepimop/gempyor_pkg/tests/interface/data/config_minimal.yaml delete mode 100644 flepimop/gempyor_pkg/tests/interface/data/geodata_2019_statelevel.csv delete mode 100644 flepimop/gempyor_pkg/tests/interface/data/mobility_2011-2015_statelevel.csv delete mode 100644 flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml delete mode 100644 flepimop/gempyor_pkg/tests/seir/data/config_compartment.yml diff --git a/flepimop/gempyor_pkg/tests/interface/data/config_minimal.yaml b/flepimop/gempyor_pkg/tests/interface/data/config_minimal.yaml deleted file mode 100644 index 15ab5792b..000000000 --- a/flepimop/gempyor_pkg/tests/interface/data/config_minimal.yaml +++ /dev/null @@ -1,123 +0,0 @@ -name: minimal -setup_name: minimal -start_date: 2020-01-31 -end_date: 2020-05-31 -data_path: data -nslots: 15 - - -spatial_setup: - geodata: geodata.csv - mobility: mobility.txt - popnodes: population - nodenames: geoid - -seeding: - method: FolderDraw - seeding_file_type: seed - -initial_conditions: - method: Default - -compartments: - infection_stage: ["S", "E", "I1", "I2", "I3", "R"] - vaccination_stage: ["unvaccinated"] - -seir: - integration: - method: legacy - dt: 1/6 - parameters: - alpha: - value: - distribution: fixed - value: .9 - sigma: - value: - distribution: fixed - value: 1 / 5.2 - gamma: - value: - distribution: uniform - low: 1 / 6 - high: 1 / 2.6 - R0s: - value: - distribution: uniform - low: 2 - high: 3 - transitions: - - source: ["S", "unvaccinated"] - destination: ["E", "unvaccinated"] - rate: ["R0s * gamma", 1] - proportional_to: [ - ["S", "unvaccinated"], - [[["I1", "I2", "I3"]], "unvaccinated"], - ] - proportion_exponent: [["1", "1"], ["alpha", "1"]] - - source: [["E"], ["unvaccinated"]] - destination: [["I1"], ["unvaccinated"]] - rate: ["sigma", 1] - proportional_to: [[["E"], ["unvaccinated"]]] - proportion_exponent: [["1", "1"]] - - source: [["I1"], ["unvaccinated"]] - destination: [["I2"], ["unvaccinated"]] - rate: ["3 * gamma", 1] - proportional_to: [[["I1"], ["unvaccinated"]]] - proportion_exponent: [["1", "1"]] - - source: [["I2"], ["unvaccinated"]] - destination: [["I3"], ["unvaccinated"]] - rate: ["3 * gamma", 1] - proportional_to: [[["I2"], ["unvaccinated"]]] - proportion_exponent: [["1", "1"]] - - source: [["I3"], ["unvaccinated"]] - destination: [["R"], ["unvaccinated"]] - rate: ["3 * gamma", 1] - proportional_to: [[["I3"], ["unvaccinated"]]] - proportion_exponent: [["1", "1"]] - -interventions: - scenarios: - - None - - Scenario1 - - Scenario2 - settings: - None: - template: ReduceR0 - parameter: r0 - period_start_date: 2020-04-01 - period_end_date: 2020-05-15 - value: - distribution: fixed - value: 0 - Wuhan: - template: Reduce - parameter: r0 - period_start_date: 2020-04-01 - period_end_date: 2020-05-15 - value: - distribution: uniform - low: .14 - high: .33 - KansasCity: - template: MultiTimeReduce - parameter: r0 - groups: - - periods: - - start_date: 2020-04-01 - end_date: 2020-05-15 - affected_geoids: "all" - value: - distribution: uniform - low: .04 - high: .23 - Scenario1: - template: Stacked - scenarios: - - KansasCity - - Wuhan - - None - Scenario2: - template: Stacked - scenarios: - - Wuhan diff --git a/flepimop/gempyor_pkg/tests/interface/data/config_test.yml b/flepimop/gempyor_pkg/tests/interface/data/config_test.yml index ea597e332..cd7f74ecd 100644 --- a/flepimop/gempyor_pkg/tests/interface/data/config_test.yml +++ b/flepimop/gempyor_pkg/tests/interface/data/config_test.yml @@ -9,8 +9,6 @@ nslots: 5 subpop_setup: geodata: geodata.csv mobility: mobility.csv - subpop_pop_key: population - subpop_names_key: subpop seeding: diff --git a/flepimop/gempyor_pkg/tests/interface/data/geodata_2019_statelevel.csv b/flepimop/gempyor_pkg/tests/interface/data/geodata_2019_statelevel.csv deleted file mode 100644 index f0bbbd8f7..000000000 --- a/flepimop/gempyor_pkg/tests/interface/data/geodata_2019_statelevel.csv +++ /dev/null @@ -1,52 +0,0 @@ -USPS,geoid,pop2019est -WY,56000,581024 -VT,50000,624313 -DC,11000,692683 -AK,02000,737068 -ND,38000,756717 -SD,46000,870638 -DE,10000,957248 -MT,30000,1050649 -RI,44000,1057231 -ME,23000,1335492 -NH,33000,1348124 -HI,15000,1422094 -ID,16000,1717750 -WV,54000,1817305 -NE,31000,1914571 -NM,35000,2092454 -KS,20000,2910652 -NV,32000,2972382 -MS,28000,2984418 -AR,05000,2999370 -UT,49000,3096848 -IA,19000,3139508 -CT,09000,3575074 -OK,40000,3932870 -OR,41000,4129803 -KY,21000,4449052 -LA,22000,4664362 -AL,01000,4876250 -SC,45000,5020806 -MN,27000,5563378 -CO,08000,5610349 -WI,55000,5790716 -MD,24000,6018848 -MO,29000,6104910 -IN,18000,6665703 -TN,47000,6709356 -MA,25000,6850553 -AZ,04000,7050299 -WA,53000,7404107 -VA,51000,8454463 -NJ,34000,8878503 -MI,26000,9965265 -NC,37000,10264876 -GA,13000,10403847 -OH,39000,11655397 -IL,17000,12770631 -PA,42000,12791530 -NY,36000,19572319 -FL,12000,20901636 -TX,48000,28260856 -CA,06000,39283497 diff --git a/flepimop/gempyor_pkg/tests/interface/data/mobility_2011-2015_statelevel.csv b/flepimop/gempyor_pkg/tests/interface/data/mobility_2011-2015_statelevel.csv deleted file mode 100644 index a2da772ba..000000000 --- a/flepimop/gempyor_pkg/tests/interface/data/mobility_2011-2015_statelevel.csv +++ /dev/null @@ -1,2330 +0,0 @@ -ori,dest,amount -01000,02000,198 -01000,04000,292 -01000,05000,570 -01000,06000,1030 -01000,08000,328 -01000,09000,36 -01000,11000,478 -01000,12000,17592 -01000,13000,93000 -01000,15000,104 -01000,16000,24 -01000,17000,682 -01000,18000,578 -01000,19000,300 -01000,20000,142 -01000,21000,1228 -01000,22000,5922 -01000,23000,74 -01000,24000,280 -01000,25000,538 -01000,26000,698 -01000,27000,452 -01000,28000,26864 -01000,29000,600 -01000,30000,102 -01000,31000,58 -01000,32000,96 -01000,33000,16 -01000,34000,390 -01000,35000,124 -01000,36000,770 -01000,37000,1288 -01000,38000,100 -01000,39000,880 -01000,40000,280 -01000,41000,132 -01000,42000,968 -01000,45000,924 -01000,46000,16 -01000,47000,16562 -01000,48000,4242 -01000,49000,120 -01000,50000,34 -01000,51000,1052 -01000,53000,370 -01000,54000,114 -01000,55000,314 -01000,56000,142 -02000,04000,60 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-56000,48000,440 -56000,49000,916 -56000,53000,216 -56000,54000,100 diff --git a/flepimop/gempyor_pkg/tests/npi/config_npi.yml b/flepimop/gempyor_pkg/tests/npi/config_npi.yml index 9482a4199..02cedeb3a 100644 --- a/flepimop/gempyor_pkg/tests/npi/config_npi.yml +++ b/flepimop/gempyor_pkg/tests/npi/config_npi.yml @@ -16,8 +16,6 @@ compartments: subpop_setup: geodata: geodata_2019_statelevel.csv mobility: mobility_2011-2015_statelevel.csv - subpop_pop_key: population - subpop_names_key: subpop seeding: diff --git a/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml b/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml index 3f86ec5f9..a5f0a2b50 100644 --- a/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml +++ b/flepimop/gempyor_pkg/tests/npi/config_test_spatial_group_npi.yml @@ -18,9 +18,6 @@ subpop_setup: mobility: mobility_2011-2015_statelevel.csv include_in_report: include_in_report state_level: TRUE - subpop_pop_key: population - subpop_names_key: subpop - seir: diff --git a/flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml b/flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml deleted file mode 100644 index 9d5d94f23..000000000 --- a/flepimop/gempyor_pkg/tests/npi/data/config_minimal.yaml +++ /dev/null @@ -1,123 +0,0 @@ -name: minimal -setup_name: minimal -start_date: 2020-01-31 -end_date: 2020-05-31 -data_path: data -nslots: 15 - - -spatial_setup: - geodata: geodata.csv - mobility: mobility.txt - popnodes: population - subpop_names: subpop - -seeding: - method: FolderDraw - seeding_file_type: seed - -initial_conditions: - method: Default - -compartments: - infection_stage: ["S", "E", "I1", "I2", "I3", "R"] - vaccination_stage: ["unvaccinated"] - -seir: - integration: - method: legacy - dt: 1/6 - parameters: - alpha: - value: - distribution: fixed - value: .9 - sigma: - value: - distribution: fixed - value: 1 / 5.2 - gamma: - value: - distribution: uniform - low: 1 / 6 - high: 1 / 2.6 - R0s: - value: - distribution: uniform - low: 2 - high: 3 - transitions: - - source: ["S", "unvaccinated"] - destination: ["E", "unvaccinated"] - rate: ["R0s * gamma", 1] - proportional_to: [ - ["S", "unvaccinated"], - [[["I1", "I2", "I3"]], "unvaccinated"], - ] - proportion_exponent: [["1", "1"], ["alpha", "1"]] - - source: [["E"], ["unvaccinated"]] - destination: [["I1"], ["unvaccinated"]] - rate: ["sigma", 1] - proportional_to: [[["E"], ["unvaccinated"]]] - proportion_exponent: [["1", "1"]] - - source: [["I1"], ["unvaccinated"]] - destination: [["I2"], ["unvaccinated"]] - rate: ["3 * gamma", 1] - proportional_to: [[["I1"], ["unvaccinated"]]] - proportion_exponent: [["1", "1"]] - - source: [["I2"], ["unvaccinated"]] - destination: [["I3"], ["unvaccinated"]] - rate: ["3 * gamma", 1] - proportional_to: [[["I2"], ["unvaccinated"]]] - proportion_exponent: [["1", "1"]] - - source: [["I3"], ["unvaccinated"]] - destination: [["R"], ["unvaccinated"]] - rate: ["3 * gamma", 1] - proportional_to: [[["I3"], ["unvaccinated"]]] - proportion_exponent: [["1", "1"]] - -interventions: - scenarios: - - None - - Scenario1 - - Scenario2 - settings: - None: - template: Reduce - parameter: r0 - period_start_date: 2020-04-01 - period_end_date: 2020-05-15 - value: - distribution: fixed - value: 0 - Wuhan: - template: Reduce - parameter: r0 - period_start_date: 2020-04-01 - period_end_date: 2020-05-15 - value: - distribution: uniform - low: .14 - high: .33 - KansasCity: - template: MultiTimeReduce - parameter: r0 - groups: - - periods: - - start_date: 2020-04-01 - end_date: 2020-05-15 - affected_geoids: "all" - value: - distribution: uniform - low: .04 - high: .23 - Scenario1: - template: Stacked - scenarios: - - KansasCity - - Wuhan - - None - Scenario2: - template: Stacked - scenarios: - - Wuhan diff --git a/flepimop/gempyor_pkg/tests/npi/data/config_test.yml b/flepimop/gempyor_pkg/tests/npi/data/config_test.yml index 300d2966f..8274b47b2 100644 --- a/flepimop/gempyor_pkg/tests/npi/data/config_test.yml +++ b/flepimop/gempyor_pkg/tests/npi/data/config_test.yml @@ -9,8 +9,6 @@ nslots: 5 subpop_setup: geodata: geodata.csv mobility: mobility.csv - subpop_pop_key: population - subpop_names_key: subpop seeding: diff --git a/flepimop/gempyor_pkg/tests/outcomes/config.yml b/flepimop/gempyor_pkg/tests/outcomes/config.yml index 4a5ddbd7a..a4467b14d 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config.yml @@ -7,8 +7,7 @@ nslots: 1 subpop_setup: geodata: geodata.csv - subpop_pop_key: population - subpop_names_key: subpop + outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_load.yml b/flepimop/gempyor_pkg/tests/outcomes/config_load.yml index 9bbdd2030..afd1b3398 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_load.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_load.yml @@ -7,8 +7,7 @@ nslots: 1 subpop_setup: geodata: geodata.csv - subpop_pop_key: population - subpop_names_key: subpop + outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml b/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml index f44627663..5fa40d238 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_load_subclasses.yml @@ -7,8 +7,7 @@ nslots: 1 subpop_setup: geodata: geodata.csv - subpop_pop_key: population - subpop_names_key: subpop + outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml b/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml index 4294303b2..15704e16b 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_mc_selection.yml @@ -7,8 +7,7 @@ nslots: 1 subpop_setup: geodata: geodata.csv - subpop_pop_key: population - subpop_names_key: subpop + outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml b/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml index 69c944762..5121032fe 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_npi.yml @@ -7,8 +7,7 @@ nslots: 1 subpop_setup: geodata: geodata.csv - subpop_pop_key: population - subpop_names_key: subpop + outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml b/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml index 25c8f12bf..130852182 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_npi_custom_pnames.yml @@ -7,8 +7,7 @@ nslots: 1 subpop_setup: geodata: geodata.csv - subpop_pop_key: population - subpop_names_key: subpop + outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml b/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml index e2b263b04..da88349df 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_subclasses.yml @@ -7,8 +7,7 @@ nslots: 1 subpop_setup: geodata: geodata.csv - subpop_pop_key: population - subpop_names_key: subpop + outcomes: method: delayframe diff --git a/flepimop/gempyor_pkg/tests/outcomes/config_test.yml b/flepimop/gempyor_pkg/tests/outcomes/config_test.yml index c4f0acd4d..ccbc7314e 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/config_test.yml +++ b/flepimop/gempyor_pkg/tests/outcomes/config_test.yml @@ -9,8 +9,6 @@ nslots: 5 subpop_setup: geodata: geodata.csv mobility: mobility.csv - subpop_pop_key: population - subpop_names_key: subpop seeding: diff --git a/flepimop/gempyor_pkg/tests/seir/data/config.yml b/flepimop/gempyor_pkg/tests/seir/data/config.yml index 2e09b4110..3140004c8 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config.yml @@ -9,8 +9,7 @@ nslots: 15 subpop_setup: geodata: geodata.csv mobility: mobility.txt - subpop_pop_key: population - subpop_names_key: subpop + seeding: method: FolderDraw diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_compartment.yml b/flepimop/gempyor_pkg/tests/seir/data/config_compartment.yml deleted file mode 100644 index 6763af77a..000000000 --- a/flepimop/gempyor_pkg/tests/seir/data/config_compartment.yml +++ /dev/null @@ -1,119 +0,0 @@ -#name: minimal -#setup_name: minimal -#start_date: 2020-01-31 -#end_date: 2020-05-31 -#data_path: data -#nslots: 15 - - -#spatial_setup: -# geodata: geodata.csv -# mobility: mobility.txt -# popnodes: population -# nodenames: geoid - -#seeding: -# method: FolderDraw -# seeding_file_type: seed - -#initial_conditions: -# method: Default - -compartments: - infection_stage: ["S", "E", "I1", "I2", "I3", "R"] - vaccination_stage: ["unvaccinated"] - -seir: - integration: - method: legacy - dt: 1/6 -# parameters: -# alpha: -# value: -# distribution: fixed -# value: .9 -# sigma: value: distribution: fixed value: 1 / 5.2 -# value: -# distribution: uniform -# low: 1 / 6 -# high: 1 / 2.6 -# R0s: -# value: -# distribution: uniform -# low: 2 -# high: 3 -# transitions: -# - source: ["S", "unvaccinated"] -# destination: ["E", "unvaccinated"] -# rate: ["R0s * gamma", 1] -# proportional_to: [ -# ["S", "unvaccinated"], -# [[["I1", "I2", "I3"]], "unvaccinated"], -# ] -# proportion_exponent: [["1", "1"], ["alpha", "1"]] -# - source: [["E"], ["unvaccinated"]] -# destination: [["I1"], ["unvaccinated"]] -# rate: ["sigma", 1] -# proportional_to: [[["E"], ["unvaccinated"]]] -# proportion_exponent: [["1", "1"]] -# - source: [["I1"], ["unvaccinated"]] -# destination: [["I2"], ["unvaccinated"]] -# rate: ["3 * gamma", 1] -# proportional_to: [[["I1"], ["unvaccinated"]]] -# proportion_exponent: [["1", "1"]] -# - source: [["I2"], ["unvaccinated"]] -# destination: [["I3"], ["unvaccinated"]] -# rate: ["3 * gamma", 1] -# proportional_to: [[["I2"], ["unvaccinated"]]] -# proportion_exponent: [["1", "1"]] -# - source: [["I3"], ["unvaccinated"]] -# destination: [["R"], ["unvaccinated"]] -# rate: ["3 * gamma", 1] -# proportional_to: [[["I3"], ["unvaccinated"]]] -# proportion_exponent: [["1", "1"]] - -#interventions: -# scenarios: -# - None -# - Scenario1 -# - Scenario2 -# settings: -# None: -# template: Reduce -# parameter: r0 -# period_start_date: 2020-04-01 -# period_end_date: 2020-05-15 -# value: -# distribution: fixed -# value: 0 -# Wuhan: -# template: Reduce -# parameter: r0 -# period_start_date: 2020-04-01 -# period_end_date: 2020-05-15 -# value: -# distribution: uniform -# low: .14 -# high: .33 -# KansasCity: -# template: MultiTimeReduce -# parameter: r0 -# groups: -# - periods: -# - start_date: 2020-04-01 -# end_date: 2020-05-15 -# affected_geoids: "all" -# value: -# distribution: uniform -# low: .04 -# high: .23 -# Scenario1: -# template: Stacked -# scenarios: -# - KansasCity -# - Wuhan -# - None -# Scenario2: -# template: Stacked -# scenarios: -# - Wuhan diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml index 843743a7b..150ba4429 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_format.yml @@ -8,8 +8,7 @@ nslots: 15 subpop_setup: geodata: geodata.csv mobility: mobility.txt - subpop_pop_key: population - subpop_names_key: subpop + compartments: infection_stage: ["S", "E", "I1", "I2", "I3", "R"] diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml index 97d6b69e3..2069fa82f 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_compartmental_model_full.yml @@ -8,8 +8,7 @@ nslots: 15 subpop_setup: geodata: geodata.csv mobility: mobility.txt - subpop_pop_key: population - subpop_names_key: subpop + seeding: method: FolderDraw diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml b/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml index c197145a3..7c7fd9ce5 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_continuation_resume.yml @@ -9,8 +9,7 @@ subpop_setup: base_path: data geodata: geodata.csv mobility: mobility.txt - subpop_pop_key: population - subpop_names_key: subpop + initial_conditions: method: InitialConditionsFolderDraw diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml b/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml index dbe7cb0a6..4563ef73c 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_inference_resume.yml @@ -9,8 +9,7 @@ subpop_setup: base_path: data geodata: geodata.csv mobility: mobility.txt - subpop_pop_key: population - subpop_names_key: subpop + initial_conditions: method: InitialConditionsFolderDraw diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml b/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml index 9e4b8aad9..f8c448e84 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_parallel.yml @@ -9,8 +9,7 @@ subpop_setup: base_path: data geodata: geodata.csv mobility: mobility.csv - subpop_pop_key: population - subpop_names_key: subpop + seeding: seeding_file_type: seed diff --git a/flepimop/gempyor_pkg/tests/seir/data/config_test.yml b/flepimop/gempyor_pkg/tests/seir/data/config_test.yml index 6dc96adc2..40b0db55f 100644 --- a/flepimop/gempyor_pkg/tests/seir/data/config_test.yml +++ b/flepimop/gempyor_pkg/tests/seir/data/config_test.yml @@ -9,8 +9,6 @@ nslots: 5 subpop_setup: geodata: geodata.csv mobility: mobility.csv - subpop_pop_key: population - subpop_names_key: subpop seeding: From 494eb16f63eeea1796a006c30f55b8a533134da8 Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 2 Nov 2023 15:25:38 -0400 Subject: [PATCH 39/50] deleted int cast in the first arg when invoking np.random.binomial() in the process of method=="legacy" --- flepimop/gempyor_pkg/src/gempyor/steps_rk4.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py b/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py index 7e9de7a13..37d6c453b 100644 --- a/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py +++ b/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py @@ -146,8 +146,7 @@ def rhs(t, x, today): for spatial_node in range(nspatial_nodes): number_move[spatial_node] = np.random.binomial( # number_move[spatial_node] = random.binomial( - # source_number[spatial_node], - int(source_number[spatial_node]), + source_number[spatial_node], compound_adjusted_rate[spatial_node], ) else: From e48744d41b6ec8a0416715de6c61ebae75ddbde8 Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 2 Nov 2023 15:36:17 -0400 Subject: [PATCH 40/50] to avoid DeprecationWarning on escape char '\' in print sentence --- flepimop/gempyor_pkg/src/gempyor/steps_rk4.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py b/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py index 37d6c453b..cb61a9f9d 100644 --- a/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py +++ b/flepimop/gempyor_pkg/src/gempyor/steps_rk4.py @@ -317,6 +317,6 @@ def rk4_integrate(t, x, today): print( "load the name space with: \nwith open('integration_dump.pkl','rb') as fn_dump:\n states, states_daily_incid, ncompartments, nspatial_nodes, ndays, parameters, dt, transitions, proportion_info, transition_sum_compartments, initial_conditions, seeding_data, seeding_amounts, mobility_data, mobility_row_indices, mobility_data_indices, population, stochastic_p, method = pickle.load(fn_dump)" ) - print("/!\ Invalid integration, will cause problems for downstream users /!\ ") + print("/!\\ Invalid integration, will cause problems for downstream users /!\\ ") # raise ValueError("Invalid Integration...") return states, states_daily_incid From 086004f4a748d2ae21b3fad3ea0416e56c5e10c1 Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 2 Nov 2023 15:49:13 -0400 Subject: [PATCH 41/50] modified to use .get() in setting ftype when invoking setup.read_simID() in if method == "InitialConditionsFolderDraw" in seeding_ic.py --- flepimop/gempyor_pkg/src/gempyor/seeding_ic.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py b/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py index b718523aa..3edb48a38 100644 --- a/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py +++ b/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py @@ -160,7 +160,7 @@ def draw_ic(self, sim_id: int, setup) -> np.ndarray: ) elif method == "InitialConditionsFolderDraw" or method == "FromFile": if method == "InitialConditionsFolderDraw": - ic_df = setup.read_simID(ftype=str(self.initial_conditions_config["initial_file_type"]), sim_id=sim_id) + ic_df = setup.read_simID(ftype=self.initial_conditions_config["initial_file_type"].get(), sim_id=sim_id) elif method == "FromFile": ic_df = read_df( self.initial_conditions_config["initial_conditions_file"].get(), @@ -250,9 +250,13 @@ def draw_ic(self, sim_id: int, setup) -> np.ndarray: if self.initial_conditions_config["ignore_population_checks"].get(): ignore_population_checks = True if error and not ignore_population_checks: - raise ValueError(f""" geodata and initial condition do not agree on population size (see messages above). Use ignore_population_checks: True to ignore""") + raise ValueError( + f""" geodata and initial condition do not agree on population size (see messages above). Use ignore_population_checks: True to ignore""" + ) elif error and ignore_population_checks: - print(""" Ignoring the previous population mismatch errors because you added flag 'ignore_population_checks'. This is dangerous""") + print( + """ Ignoring the previous population mismatch errors because you added flag 'ignore_population_checks'. This is dangerous""" + ) return y0 def draw_seeding(self, sim_id: int, setup) -> nb.typed.Dict: From b3a2ede20436db52a4123e39154dd702d5a6335a Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 2 Nov 2023 15:53:22 -0400 Subject: [PATCH 42/50] saved test_outcomes.py with black formatting --- flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py index 32824f148..19f2a2dc6 100644 --- a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py +++ b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py @@ -768,8 +768,8 @@ def test_outcomes_read_write_hnpi2_custom_pname(): first_sim_index=1, outcome_modifiers_scenario="Some", stoch_traj_flag=False, -out_run_id=107, -) + out_run_id=107, + ) outcomes.onerun_delayframe_outcomes(sim_id2write=1, modinf=inference_simulator.modinf, load_ID=True, sim_id2load=1) From 8a8942922e318054458aa1ea839797576c09b4ac Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 2 Nov 2023 16:00:35 -0400 Subject: [PATCH 43/50] deleted unnecessary commented lines in test_seir.py --- flepimop/gempyor_pkg/tests/seir/test_seir.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/flepimop/gempyor_pkg/tests/seir/test_seir.py b/flepimop/gempyor_pkg/tests/seir/test_seir.py index 38055af90..b086cbf6c 100644 --- a/flepimop/gempyor_pkg/tests/seir/test_seir.py +++ b/flepimop/gempyor_pkg/tests/seir/test_seir.py @@ -35,9 +35,6 @@ def test_check_values(): seeding[0, 0] = 1 - # if np.all(seeding == 0): - # warnings.warn("provided seeding has only value 0", UserWarning) - if np.all(modinf.mobility.data < 1): warnings.warn("highest mobility value is less than 1", UserWarning) @@ -518,9 +515,7 @@ def test_continuation_resume(): out_run_id=run_id, out_prefix=prefix, ) - # Convert Subview object to string using str - # modinf.initial_conditions_config["initial_file_type"] = str(modinf.initial_conditions_config["initial_file_type"]) - # modinf.initial_file_type = str(modinf.initial_conditions_config["initial_file_type"]) + seir.onerun_SEIR(sim_id2write=sim_id2write, modinf=modinf, config=config) states_new = pq.read_table( From 11d386116daff5cdbd1fca063d7f5f9df4b258f0 Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 2 Nov 2023 16:15:58 -0400 Subject: [PATCH 44/50] modified to use .as_evaled_expression() to store p value first in processing dist=="binomial" in utils.py --- flepimop/gempyor_pkg/src/gempyor/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flepimop/gempyor_pkg/src/gempyor/utils.py b/flepimop/gempyor_pkg/src/gempyor/utils.py index f0849903a..e6cfe63a0 100644 --- a/flepimop/gempyor_pkg/src/gempyor/utils.py +++ b/flepimop/gempyor_pkg/src/gempyor/utils.py @@ -190,7 +190,7 @@ def as_random_distribution(self): elif dist == "poisson": return functools.partial(np.random.poisson, self["lam"].as_evaled_expression()) elif dist == "binomial": - p = self["p"].as_number() + p = self["p"].as_evaled_expression() if (p < 0) or (p > 1): raise ValueError(f"""p value { p } is out of range [0,1]""") # if (self["p"] < 0) or (self["p"] > 1): From bbe130ab18c8ba8c92a8a04a706a04913bb1b1bd Mon Sep 17 00:00:00 2001 From: kjsato Date: Thu, 2 Nov 2023 16:17:18 -0400 Subject: [PATCH 45/50] added two test functions test_as_random_distribution_binomial_w_fraction and test_as_random_distribution_binomial_w_fraction_error in test_utils2.py --- .../gempyor_pkg/tests/utils/test_utils2.py | 172 +++++++++++------- 1 file changed, 102 insertions(+), 70 deletions(-) diff --git a/flepimop/gempyor_pkg/tests/utils/test_utils2.py b/flepimop/gempyor_pkg/tests/utils/test_utils2.py index fabd4428b..4b0ae59ba 100644 --- a/flepimop/gempyor_pkg/tests/utils/test_utils2.py +++ b/flepimop/gempyor_pkg/tests/utils/test_utils2.py @@ -2,7 +2,8 @@ import datetime import os import pandas as pd -#import dask.dataframe as dd + +# import dask.dataframe as dd import numpy as np from scipy.stats import rv_continuous import pyarrow as pa @@ -12,59 +13,64 @@ import confuse from unittest.mock import MagicMock, patch -from gempyor import utils +from gempyor import utils from gempyor.utils import ISO8601Date DATA_DIR = os.path.dirname(__file__) + "/data" -#os.chdir(os.path.dirname(__file__)) +# os.chdir(os.path.dirname(__file__)) tmp_path = "/tmp" + class SampleClass: - def __init__(self): - self.value = 11 - - @utils.profile(output_file="get_value.prof", sort_by="time", lines_to_print=10, strip_dirs=True) - def get_value(self): - return self.value + def __init__(self): + self.value = 11 + + @utils.profile(output_file="get_value.prof", sort_by="time", lines_to_print=10, strip_dirs=True) + def get_value(self): + return self.value + + def set_value(self, value): + self.value = value - def set_value(self, value): - self.value = value class Test_utils2: - @utils.add_method(SampleClass) - def get_a(self): - return "a" + @utils.add_method(SampleClass) + def get_a(self): + return "a" - def test_add_method(self): - assert SampleClass.get_a(self) == "a" + def test_add_method(self): + assert SampleClass.get_a(self) == "a" - def test_get_value_w_profile(self): - s = SampleClass() - s.get_value() + def test_get_value_w_profile(self): + s = SampleClass() + s.get_value() # display profile information - stats = pstats.Stats("get_value.prof") - stats.sort_stats("time") - stats.print_stats(10) + stats = pstats.Stats("get_value.prof") + stats.sort_stats("time") + stats.print_stats(10) - def test_ISO8601Date_success(self): - iso_date = utils.ISO8601Date("2020-01-01") - input_date = datetime.date(2020,1,1) - result = iso_date.convert(input_date, None) # dummy for view - assert result == input_date + def test_ISO8601Date_success(self): + iso_date = utils.ISO8601Date("2020-01-01") + input_date = datetime.date(2020, 1, 1) + result = iso_date.convert(input_date, None) # dummy for view + assert result == input_date - iso_date2 = utils.ISO8601Date() - result = iso_date2.convert(str(input_date), None) # dummy for view - assert result == input_date - ''' + iso_date2 = utils.ISO8601Date() + result = iso_date2.convert(str(input_date), None) # dummy for view + assert result == input_date + + """ def test_ISO8601Date_invalid_value(self): iso_date2 = utils.ISO8601Date() invalid_value = "2020-01-01" with pytest.raises(ValueError, match=r".*must.*be.*ISO8601.*"): iso_date2.convert(invalid_value, None) # dummy for view - ''' -''' + """ + + +""" def test_profile_success(): utils.profile() utils.profile(output_file="test") @@ -82,7 +88,8 @@ def test_get_truncated_normal_success(): def test_get_log_normal_success(): utils.get_log_normal(meanlog=0, sdlog=1) -''' +""" + def test_as_date_with_valid_date_string(): # created MockConfigView object @@ -95,14 +102,14 @@ def test_as_date_with_valid_date_string(): with patch.object(ISO8601Date, "convert", return_value=datetime.date(2022, 1, 15)): result = ISO8601Date().convert(mock_config_view.get(), None) - # 正しい日付オブジェクトが返されることを確認 assert result == datetime.date(2022, 1, 15) + def test_as_evaled_expression_with_valid_expression(): # ConfigViewオブジェクトをモック化 mock_config_view = MagicMock(spec=confuse.ConfigView) - mock_config_view.as_evaled_expression.return_value =7.5 + mock_config_view.as_evaled_expression.return_value = 7.5 # as_evaled_expressionメソッドを呼び出し、正しい結果を確認 result = mock_config_view.as_evaled_expression() @@ -110,78 +117,103 @@ def test_as_evaled_expression_with_valid_expression(): assert result == 7.5 - @pytest.fixture def config(): - config = confuse.Configuration('myapp', __name__) + config = confuse.Configuration("myapp", __name__) return config + def test_as_evaled_expression_number(config): - config.add({'myvalue': 123}) - assert config['myvalue'].as_evaled_expression() == 123 + config.add({"myvalue": 123}) + assert config["myvalue"].as_evaled_expression() == 123 + def test_as_evaled_expression_number(config): - config.add({'myvalue': 1.10}) - assert config['myvalue'].as_evaled_expression() == 1.1 + config.add({"myvalue": 1.10}) + assert config["myvalue"].as_evaled_expression() == 1.1 + def test_as_evaled_expression_string(config): - config.add({'myvalue': '2 + 3'}) - assert config['myvalue'].as_evaled_expression() == 5.0 + config.add({"myvalue": "2 + 3"}) + assert config["myvalue"].as_evaled_expression() == 5.0 + def test_as_evaled_expression_other(config): - config.add({'myvalue': [1, 2, 3]}) + config.add({"myvalue": [1, 2, 3]}) with pytest.raises(ValueError): - config['myvalue'].as_evaled_expression() + config["myvalue"].as_evaled_expression() + def test_as_evaled_expression_Invalid_string(config): - config.add({'myvalue': 'invalid'}) + config.add({"myvalue": "invalid"}) with pytest.raises(ValueError): - config['myvalue'].as_evaled_expression() + config["myvalue"].as_evaled_expression() + def test_as_date(config): - config.add({'myvalue': '2022-01-15'}) - assert config['myvalue'].as_date() == datetime.date(2022, 1, 15) - + config.add({"myvalue": "2022-01-15"}) + assert config["myvalue"].as_date() == datetime.date(2022, 1, 15) + + def test_as_random_distribution_fixed(config): - config.add({'value':{'distribution': 'fixed', 'value': 1}}) - dist = config['value'].as_random_distribution() + config.add({"value": {"distribution": "fixed", "value": 1}}) + dist = config["value"].as_random_distribution() assert dist() == 1 + def test_as_random_distribution_uniform(config): - config.add({'value':{'distribution': 'uniform', 'low': 1, 'high':2.6}}) - dist = config['value'].as_random_distribution() - assert 1 <= dist() <=2.6 + config.add({"value": {"distribution": "uniform", "low": 1, "high": 2.6}}) + dist = config["value"].as_random_distribution() + assert 1 <= dist() <= 2.6 + def test_as_random_distribution_poisson(config): - config.add({'value':{'distribution': 'poisson', 'lam': 1}}) - dist = config['value'].as_random_distribution() - assert isinstance(dist(), int) + config.add({"value": {"distribution": "poisson", "lam": 1}}) + dist = config["value"].as_random_distribution() + assert isinstance(dist(), int) + def test_as_random_distribution_binomial(config): - config.add({'value':{'distribution': 'binomial', 'n': 10, 'p':0.5 }}) - dist = config['value'].as_random_distribution() - assert 0 <= dist() <= 10 + config.add({"value": {"distribution": "binomial", "n": 10, "p": 0.5}}) + dist = config["value"].as_random_distribution() + assert 0 <= dist() <= 10 + + +def test_as_random_distribution_binomial_w_fraction(config): + config.add({"value": {"distribution": "binomial", "n": 10, "p": "1/2"}}) + dist = config["value"].as_random_distribution() + assert 0 <= dist() <= 10 + def test_as_random_distribution_binomial_error(config): - config.add({'value':{'distribution': 'binomial', 'n': 10, 'p':1.1 }}) + config.add({"value": {"distribution": "binomial", "n": 10, "p": 1.1}}) + with pytest.raises(ValueError, match=r".*p.*value.*"): + dist = config["value"].as_random_distribution() + + +def test_as_random_distribution_binomial_w_fraction_error(config): + config.add({"value": {"distribution": "binomial", "n": 10, "p": "5/4"}}) with pytest.raises(ValueError, match=r".*p.*value.*"): - dist = config['value'].as_random_distribution() + dist = config["value"].as_random_distribution() + def test_as_random_distribution_truncnorm(config): - config.add({'value':{'distribution': 'truncnorm', 'mean': 0, 'sd':1, 'a':-1, 'b':1}}) - dist = config['value'].as_random_distribution() + config.add({"value": {"distribution": "truncnorm", "mean": 0, "sd": 1, "a": -1, "b": 1}}) + dist = config["value"].as_random_distribution() rvs = dist(size=1000) assert len(rvs) == 1000 assert all(-1 <= x <= 1 for x in rvs) + def test_as_random_distribution_lognorm(config): - config.add({'value':{'distribution': 'lognorm', 'meanlog': 0, 'sdlog':1}}) - dist = config['value'].as_random_distribution() + config.add({"value": {"distribution": "lognorm", "meanlog": 0, "sdlog": 1}}) + dist = config["value"].as_random_distribution() rvs = dist(size=1000) assert len(rvs) == 1000 assert all(x > 0 for x in rvs) + def test_as_random_distribution_unknown(config): - config.add({'value':{'distribution': 'unknown', 'mean': 0, 'sd':1}}) + config.add({"value": {"distribution": "unknown", "mean": 0, "sd": 1}}) with pytest.raises(NotImplementedError): - config['value'].as_random_distribution() + config["value"].as_random_distribution() From 132613fe6d0d425f72b400145818d4c6f7ec5d56 Mon Sep 17 00:00:00 2001 From: Shaun Truelove Date: Fri, 3 Nov 2023 11:48:36 -0400 Subject: [PATCH 46/50] fix extra s in yaml_utils.R --- flepimop/R_packages/config.writer/R/yaml_utils.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flepimop/R_packages/config.writer/R/yaml_utils.R b/flepimop/R_packages/config.writer/R/yaml_utils.R index bbe8a4692..9a62f07a6 100644 --- a/flepimop/R_packages/config.writer/R/yaml_utils.R +++ b/flepimop/R_packages/config.writer/R/yaml_utils.R @@ -301,7 +301,7 @@ print_value1 <- function(value_type, value_dist, value_mean, space3 <- rep(" ", indent_space + 4) %>% paste0(collapse = "") print_val <- "" - if (value_type == "timeseriess" && !is.null(value_type)){ + if (value_type == "timeseries" && !is.null(value_type)){ print_val <- paste0(print_val, space, "timeseries: ", value_mean$timeseries, "\n") From 7f97490747c2d4676de66fd97dc1d3039e86dc1e Mon Sep 17 00:00:00 2001 From: saraloo <45245630+saraloo@users.noreply.github.com> Date: Fri, 3 Nov 2023 11:58:11 -0400 Subject: [PATCH 47/50] fix typo --- batch/SLURM_inference_job.run | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/batch/SLURM_inference_job.run b/batch/SLURM_inference_job.run index 22f7c2c52..d6b0e5445 100644 --- a/batch/SLURM_inference_job.run +++ b/batch/SLURM_inference_job.run @@ -177,7 +177,7 @@ if [[ $S3_UPLOAD == "true" ]]; then export FILENAME=$(python -c "from gempyor import file_paths; print(file_paths.create_file_name(run_id='$FLEPI_RUN_INDEX', prefix='$FLEPI_PREFIX/$FLEPI_RUN_INDEX', inference_filepath_suffix='chimeric/intermediate', - iference_filename_prefix='%09d.'% $FLEPI_SLOT_INDEX, + inference_filename_prefix='%09d.'% $FLEPI_SLOT_INDEX, index=$FLEPI_BLOCK_INDEX, ftype='$type', extension='csv'))") From 16f81ae845aad337117ddfda94375f2176551efe Mon Sep 17 00:00:00 2001 From: Joseph Lemaitre Date: Fri, 3 Nov 2023 19:49:16 +0100 Subject: [PATCH 48/50] fix the seeding bug dirtily (need some error message --- flepimop/gempyor_pkg/src/gempyor/seeding_ic.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py b/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py index d1b46c2c6..64e3a1b1e 100644 --- a/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py +++ b/flepimop/gempyor_pkg/src/gempyor/seeding_ic.py @@ -34,6 +34,8 @@ def _DataFrame2NumbaDict(df, amounts, setup) -> nb.typed.Dict: n_seeding_ignored_before = 0 n_seeding_ignored_after = 0 + + #id_seed = 0 for idx, (row_index, row) in enumerate(df.iterrows()): if row["place"] not in setup.spatset.nodenames: raise ValueError( @@ -42,6 +44,7 @@ def _DataFrame2NumbaDict(df, amounts, setup) -> nb.typed.Dict: if (row["date"].date() - setup.ti).days >= 0: if (row["date"].date() - setup.ti).days < len(nb_seed_perday): + nb_seed_perday[(row["date"].date() - setup.ti).days] = ( nb_seed_perday[(row["date"].date() - setup.ti).days] + 1 ) @@ -51,6 +54,7 @@ def _DataFrame2NumbaDict(df, amounts, setup) -> nb.typed.Dict: seeding_dict["seeding_destinations"][idx] = setup.compartments.get_comp_idx(destination_dict) seeding_dict["seeding_places"][idx] = setup.spatset.nodenames.index(row["place"]) seeding_amounts[idx] = amounts[idx] + #id_seed+=1 else: n_seeding_ignored_after += 1 else: @@ -230,7 +234,15 @@ def draw_seeding(self, sim_id: int, setup) -> nb.typed.Dict: raise NotImplementedError(f"unknown seeding method [got: {method}]") # Sorting by date is very important here for the seeding format necessary !!!! + print(seeding.shape) seeding = seeding.sort_values(by="date", axis="index").reset_index() + print(seeding) + mask = (seeding['date'].dt.date > setup.ti) & (seeding['date'].dt.date <= setup.tf) + seeding = seeding.loc[mask].reset_index() + print(seeding.shape) + print(seeding) + + # TODO: print. amounts = np.zeros(len(seeding)) if method == "PoissonDistributed": @@ -240,6 +252,7 @@ def draw_seeding(self, sim_id: int, setup) -> nb.typed.Dict: elif method == "FolderDraw" or method == "FromFile": amounts = seeding["amount"] + return _DataFrame2NumbaDict(df=seeding, amounts=amounts, setup=setup) def load_seeding(self, sim_id: int, setup) -> nb.typed.Dict: From 738af679121ac1e34ca90334d8a15061f263c8d0 Mon Sep 17 00:00:00 2001 From: saraloo <45245630+saraloo@users.noreply.github.com> Date: Sun, 5 Nov 2023 17:07:26 -0500 Subject: [PATCH 49/50] postprocessing edits for breaking improvements --- postprocessing/groundtruth_source.R | 12 +- postprocessing/plot_predictions.R | 4 +- .../run_sim_processing_FluSightExample.R | 149 +- postprocessing/sim_processing_source.R | 1532 +++++++++-------- 4 files changed, 849 insertions(+), 848 deletions(-) diff --git a/postprocessing/groundtruth_source.R b/postprocessing/groundtruth_source.R index 53f4bc701..d92f191d5 100644 --- a/postprocessing/groundtruth_source.R +++ b/postprocessing/groundtruth_source.R @@ -51,7 +51,7 @@ get_rawcoviddata_state_data <- function(fix_negatives = TRUE){ return(state_dat) } - +## IS THIS STILL NEEDED?? @@ -67,8 +67,8 @@ clean_gt_forplots <- function(gt_data){ filter(source != "US") gt_long <- gt_data %>% - pivot_longer(cols = -c(date, source, FIPS), names_to = "target", values_to = "incid") %>% - group_by(source, FIPS, date, target)%>% + pivot_longer(cols = -c(date, source, subpop), names_to = "target", values_to = "incid") %>% + group_by(source, subpop, date, target)%>% summarise(incid = sum(incid)) %>% ungroup() %>% filter(grepl("incid", target, ignore.case = TRUE)) @@ -76,7 +76,7 @@ clean_gt_forplots <- function(gt_data){ gt_long_tmp <- gt_long %>% as_tibble() %>% mutate(incid = fix_NAs(incid)) %>% - group_by(source, FIPS, target) %>% + group_by(source, subpop, target) %>% arrange(date) %>% mutate(incid=cumsum(incid))%>% ungroup() %>% @@ -102,10 +102,10 @@ clean_gt_forplots <- function(gt_data){ gt_long <- gt_long %>% rename(time=date, USPS=source) gt_long <- gt_long %>% - rename(subpop=FIPS, outcome_name = target, outcome = incid) + rename(outcome_name = target, outcome = incid) gt_data <- gt_data %>% - rename(subpop=FIPS, time=date, USPS=source) + rename(time=date, USPS=source) return(gt_data) } diff --git a/postprocessing/plot_predictions.R b/postprocessing/plot_predictions.R index a2ae5592e..0625d8b73 100644 --- a/postprocessing/plot_predictions.R +++ b/postprocessing/plot_predictions.R @@ -56,6 +56,7 @@ gt_cl <- NULL if (any(outcomes_time_=="weekly")) { # Incident gt_data_st_week <- get_weekly_incid(gt_data %>% dplyr::select(time, subpop, USPS, paste0("incid", outcomes_gt_[outcomes_time_gt_=="weekly"])) %>% mutate(sim_num = 0), + # gt_data_st_week <- get_weekly_incid(gt_data %>% dplyr::select(time, subpop, USPS, paste0("incid", outcomes_gt_[outcomes_time_gt_=="weekly"])) %>% mutate(sim_num = 0), outcomes = outcomes_gt_[outcomes_time_gt_=="weekly"]) # Cumulative @@ -158,7 +159,8 @@ forecast_st_plt <- forecast_st %>% mutate(target_type = paste0(incid_cum, outcome)) pltdat_truth <- dat_st_cl2 %>% - filter(aggr_target) %>% rename(gt = value) %>% + # filter(aggr_target) %>% + rename(gt = value) %>% mutate(target = gsub("incid", "inc", target)) %>% rename(target_type = target) %>% filter(USPS %in% unique(forecast_st_plt$USPS)) %>% diff --git a/postprocessing/run_sim_processing_FluSightExample.R b/postprocessing/run_sim_processing_FluSightExample.R index 4aaa90569..84676ad25 100644 --- a/postprocessing/run_sim_processing_FluSightExample.R +++ b/postprocessing/run_sim_processing_FluSightExample.R @@ -10,9 +10,7 @@ gc() library(inference) library(tidyverse) library(doParallel) - - - +gempyor <- reticulate::import("gempyor") # SETUP ------------------------------------------------------------------- @@ -25,48 +23,61 @@ flepimop_local_dir <- "../flepiMoP" # ~ Main Run Options ----------------------------------------------------------- -pull_gt <- TRUE -full_fit <- FALSE +pull_gt <- FALSE +full_fit <- TRUE # ~ Round ----------------------------------------------------------------- -round_num <- 3 -fch_date <- "Jan15" -config_subname <- "2022_Jan15" +round_num <- 4 #actually don't think we need this? +# fch_date <- "Jan15" +# config_subname <- "training" +config_name <- "config_SMH_Flu_2023_R1_medVax_H3_training.yml" +config <- flepicommon::load_config(config_name) +#I THINK WANT TO REORGANISE - JUST SAVE BY CONFIG # ~ Application ----------------------------------------------------------- -smh_or_fch <- "fch" #"fch" or "smh" -disease <- "flu" # covid19 or flu -repo <- "../../shared/SMH_Flu" -subdir <- NULL #used for testing purposes - -smh_or_fch <- tolower(smh_or_fch) -if(smh_or_fch == "fch"){ subdir <- file.path("FCH", fch_date) } #"excluding_vacchosp" #NULL # can be changed to add subanalysis +smh_or_fch <- ifelse(grepl("FCH", config_name), "fch", "smh") #"fch" or "smh" +disease <- config$disease # covid19 or flu +# repo <- "../../shared/SMH_Flu" +repo <- "../runs-flepi" +subdir <- "test" #NULL #used for testing purposes +if(smh_or_fch == "fch"){ subdir <- file.path("FCH", fch_date) }# can be changed to add subanalysis (I DON'T LIKE THIS AND THE ABOVE LINE) # ~ Scenarios ------------------------------------------------------------- -scenarios <- c("highVE_optImm", "highVE_pesImm", "lowVE_optImm", "lowVE_pesImm") # include all, it will subset later based on what you put in `scenario_num` -scenario_s3_buckets <- c("20221220T173349", "20230115T202608", "20221220T174219", "20221220T174814") # automatically pull from s3 if the data are not local already -override_pull_from_s3 <- c(FALSE, FALSE, FALSE, FALSE) # !!!! VERY IMPORTANT - LEAVE FALSE UNLESS YOU ARE REWRITING THE CURRENT S3 DATA !!!! +## THIS HAS TO BE EDITED FOR EVERY ROUND - what are the scenarios +if (disease == "flu"){ + scenarios <- c("highVax_H3", "highVax_H1", "medVax_H3", "medVax_H1", "lowVax_H3", "lowVax_H1") # include all, it will subset later based on what you put in `scenario_num` + fch_scenario_num = 3 + scenario_s3_buckets <- c("20221220T173349", "20230115T202608", "20231103T130842", "20221220T174814","20221220T174814","20221220T174814") # automatically pull from s3 if the data are not local already + override_pull_from_s3 <- c(FALSE, FALSE, FALSE, FALSE) # !!!! VERY IMPORTANT - LEAVE FALSE UNLESS YOU ARE REWRITING THE CURRENT S3 DATA !!!! +} else if (disease == "covid19"){ + scenarios <- c("65Boo_lowIE") # include all, it will subset later based on what you put in `scenario_num` + fch_scenario_num = 1 + scenario_s3_buckets <- c("20221220T173349") # automatically pull from s3 if the data are not local already + override_pull_from_s3 <- c(FALSE) # !!!! VERY IMPORTANT - LEAVE FALSE UNLESS YOU ARE REWRITING THE CURRENT S3 DATA !!!! +} -scenario_num = 1:4 # which scenarios to process right now -fch_scenario_num = 2 if(tolower(smh_or_fch) == "fch"){ scenario_num <- fch_scenario_num +}else{ + # scenario_num = 1:6 # which scenarios to process right now (ONLY FOR SMH) + scenario_num = 3 } n_weeks <- 41 # ~ Config Specifics ------------------------------------------------------ subname <- NA subname_all <- NA +config_subname <- stringr::str_extract(config_name, paste0("(?<=", scenarios[scenario_num], "_).*?(?=\\.yml)")) -# ~ Outcomes to Include (for processing and plotting) --------------------------------------------------- -outcomes_ <- c("I","C","H","D") -outcomes_time_ <- c("weekly","weekly","weekly","weekly") -outcomes_cum_ <- c(FALSE, FALSE, FALSE, FALSE) - -# ~ Calibration ----------------------------------------------------------- -outcomes_calibrate = c(FALSE, FALSE, TRUE, FALSE) # match outcomes_ -n_calib_days = 14 # need one for each outcome to calibrate +# # ~ Outcomes to Include (for processing and plotting) --------------------------------------------------- +# outcomes_ <- c("I","C","H","D") +# outcomes_time_ <- c("weekly","weekly","weekly","weekly") +# outcomes_cum_ <- c(FALSE, FALSE, FALSE, FALSE) +# +# # ~ Calibration ----------------------------------------------------------- +# outcomes_calibrate = c(FALSE, FALSE, TRUE, FALSE) # match outcomes_ +# n_calib_days = 14 # need one for each outcome to calibrate # ~ Other Run Options ----------------------------------------------------- plot_samp <- FALSE @@ -81,21 +92,10 @@ keep_vacc_compartments <- FALSE likelihood_sims <- FALSE - - - - - - # OTHER SETUP ------------------------------------------------------------- proj_dir <- getwd() - -# subset scenarios -if(tolower(smh_or_fch) == "fch"){ - scenario_num <- fch_scenario_num -} scenarios <- scenarios[scenario_num] scenario_s3_buckets <- scenario_s3_buckets[scenario_num] # automatically pull from s3 if the data are not local already override_pull_from_s3 <- override_pull_from_s3[scenario_num] # !!!! VERY IMPORTANT - LEAVE FALSE UNLESS YOU ARE REWRITING THE CURRENT S3 DATA !!!! @@ -104,12 +104,6 @@ override_pull_from_s3 <- override_pull_from_s3[scenario_num] # !!!! VERY IMPORTA geodata_file_path = file.path(config$data_path, config$subpop_setup$geodata) - - - - - - # SUBMISSION & PROCESSING SPECIFICS ---------------------------------------------------- ## -- "outcomes_" are for processing. we want more than we submit for diagnostics. @@ -125,7 +119,9 @@ if (smh_or_fch == "fch" & disease == "flu"){ outcomes_ <- c("I","C","H","D") outcomes_time_ <- c("weekly","weekly","weekly","weekly") outcomes_cum_ <- c(FALSE, FALSE, FALSE, FALSE) - outcomes_calibrate = c(FALSE, FALSE, TRUE, FALSE) + outcomes_cumfromgt = c(FALSE, FALSE, FALSE, FALSE) + outcomes_calibrate = c(FALSE, FALSE, FALSE, FALSE) + n_calib_days = 14 # need one for each outcome to calibrate } # Flu Projections (Flu SMH: https://github.com/midas-network/flu-scenario-modeling-hub) @@ -140,18 +136,20 @@ if (smh_or_fch == "smh" & disease == "flu"){ outcomes_time_ <- c("weekly","weekly","weekly","weekly") outcomes_cum_ <- c(TRUE, TRUE, TRUE, TRUE) outcomes_cumfromgt = c(FALSE, FALSE, FALSE, FALSE) - outcomes_calibrate = c(FALSE, FALSE, TRUE, FALSE) + outcomes_calibrate = c(FALSE, FALSE, FALSE, FALSE) + n_calib_days = 14 # need one for each outcome to calibrate } # COVID-19 Forecasts (COVID-19 FCH: https://github.com/reichlab/covid19-forecast-hub/blob/master/data-processed/README.md#Data-formatting) if (smh_or_fch == "fch" & disease == "covid19"){ select_submission_targets <- function(data_comb){ - targets <- c(paste0(1:20, " wk ahead cum death"), - paste0(1:20, " wk ahead inc death"), - paste0(1:8, " wk ahead inc case"), - paste0(0:130, " day ahead inc hosp")) - + # targets <- c(paste0(1:20, " wk ahead cum death"), + # paste0(1:20, " wk ahead inc death"), + # paste0(1:8, " wk ahead inc case"), + # paste0(0:130, " day ahead inc hosp")) + targets <- c(paste0(0:130, " day ahead inc hosp")) + data_comb <- data_comb %>% filter(type != "point-mean" & !(is.na(quantile) & type == "quantile")) %>% mutate(quantile = round(quantile, 3)) %>% @@ -175,13 +173,14 @@ if (smh_or_fch == "fch" & disease == "covid19"){ outcomes_cum_ <- c(FALSE, FALSE, FALSE, TRUE) outcomes_cumfromgt = c(FALSE, FALSE, FALSE, TRUE) outcomes_calibrate = c(FALSE, FALSE, TRUE, FALSE) # match outcomes_ + n_calib_days = 14 # need one for each outcome to calibrate } # COVID-19 Projections (COVID-19 SMH: https://github.com/midas-network/covid-scenario-modeling-hub) if (smh_or_fch == "smh" & disease == "covid19"){ select_submission_targets <- function(data_comb){ data_comb %>% - filter(grepl("inc hosp|inc death|cum hosp|cum death|peak size|peak time", target)) + filter(grepl("inc hosp|inc death|cum hosp|cum death", target)) } forecast_date_name <- "model_projection_date" outcomes_ <- c("I","C","H","D") @@ -189,12 +188,11 @@ if (smh_or_fch == "smh" & disease == "covid19"){ outcomes_cum_ <- c(TRUE, TRUE, TRUE, TRUE) outcomes_cumfromgt = c(FALSE, FALSE, FALSE, FALSE) outcomes_calibrate = c(FALSE, FALSE, TRUE, FALSE) + n_calib_days = 14 # need one for each outcome to calibrate } - - # Source Code and Functions ----------------------------------------------- # determine if local repo or pulling from github @@ -221,11 +219,6 @@ source(paste0(source_loc, "/postprocessing/sim_processing_source.R")) #........................................................................................................ - -# Get Config for details -config_name <- paste0(paste(na.omit(c("config", toupper(smh_or_fch), paste0("R", round_num), scenarios[1], subname_all[1], config_subname)), collapse="_"), ".yml") -config <- flepicommon::load_config(config_name) - # change n_weeks if FCH (limit to 12 weeks) projection_date <- lubridate::as_date(config$end_date_groundtruth)+1 # first day after groundtruth cutoff forecast_date <- lubridate::as_date(config$start_date)+21 # date to start plotting from @@ -236,21 +229,27 @@ if (tolower(smh_or_fch)=="fch") { n_weeks <- 4 end_date <- lubridate::as_date(projection_date + n_weeks*7) - 1 } + point_est <- 0.5 # alternative: "mean" -compartment_types = c("vacc","variant","agestrat") # types of compartments, other than standard SEIR +compartment_types = c("vacc","variant","agestrat","season") # types of compartments, other than standard SEIR +# compartment_types = c("vacc","variant","agestrat") # types of compartments, other than standard SEIR keep_which_compartments <- c("variant") # types of compartments, other than standard SEIR -scenario_ids = paste0(c(LETTERS[1:4]), "-", lubridate::as_date(config$end_date_groundtruth)+1)[scenario_num] +scenario_ids = paste0(c(LETTERS[1:6]), "-", lubridate::as_date(config$end_date_groundtruth)+1)[scenario_num] scenario_names <- scenarios scenarios_all <- scenarios -variants_ <- config$seir$compartments$variant_type -vacc_ <- config$seir$compartments$vaccination_stage +variants_ <- config$compartments$variant_type +vacc_ <- config$compartments$vaccination_stage county_level <- FALSE plot_projections <- TRUE save_reps <- smh_or_fch=="smh" & !full_fit # create the repo where everything will be saved -round_directory <- do.call(file.path, as.list(na.omit(c(repo, paste0("R",round_num), subdir)))) +# round_directory <- do.call(file.path, as.list(na.omit(c(repo, paste0("R",round_num), subdir)))) +round_directory <- do.call(file.path, + as.list(na.omit(c(repo, + stringr::str_extract(config_name, paste0("(?<=", "config", "_).*?(?=\\.yml)")), # save results in config name + subdir)))) dir.create(round_directory, recursive = TRUE, showWarnings = FALSE) print(round_directory) @@ -259,9 +258,6 @@ scenario_dir <- file.path(round_directory, scenarios_all) lapply(scenario_dir, dir.create, recursive = TRUE) - - - # PULL SIMS FROM S3 ------------------------------------------------------- #check if data have already been pulled @@ -280,7 +276,11 @@ if (any(pull_from_s3)) { scn <- scen_to_repull[i] # pull s3 bucket - sys_call_s3 <- paste0('aws s3 cp --recursive s3://idd-inference-runs/USA-', scenario_s3_buckets[scn], '/model_output/hosp ', scenario_dir[scn], '/hosp --exclude="*" --include="*/final/*"') + sys_call_s3 <- paste0('aws s3 cp --recursive s3://idd-inference-runs/USA-', scenario_s3_buckets[scn], + '/model_output/', + # paste(config$name,config$seir_modifiers$scenarios,config$outcome_modifiers$scenarios,sep="_"), + ' ', + scenario_dir[scn], ' --exclude="*" --include="*hosp/global/final/*"') system(sys_call_s3) } # stopCluster(cl) @@ -288,15 +288,13 @@ if (any(pull_from_s3)) { - - # LOAD GROUND TRUTH ------------------------------------------------------- Sys.setenv(CONFIG_PATH = config_name) Sys.setenv(FLEPI_PATH = source_loc) -if (disease == "flu"){ +if (disease == "flu" & pull_gt){ source(paste0(source_loc, "/datasetup/build_flu_data.R")) -} else if (disease == "covid19"){ +} else if (disease == "covid19" & pull_gt){ source(paste0(source_loc, "/datasetup/build_covid_data.R")) } @@ -336,8 +334,7 @@ peak_ram_ <- peakRAM::peakRAM({ print(scenarios_all[scenario_num]) scenario_dir <- paste0(round_directory, "/", scenarios_all[i], "/") - #source("postprocessing/process_sims_parallel_NEW.R", local=TRUE) - tmp_out_ <- process_sims(scenario_num = scenario_num, + tmp_out_ <- process_sims(config_name,scenario_num = scenario_num, scenarios_all = scenarios_all, scenario_names = scenario_names, scenario_ids = scenario_ids, @@ -368,7 +365,7 @@ peak_ram_ <- peakRAM::peakRAM({ plot_samp = plot_samp, gt_data = gt_data, geodata_file = geodata_file_path, - death_filter = config$outcome_modifiers$scenarios, + # death_filter = config$outcome_modifiers$scenarios, summarize_peaks = (smh_or_fch == "smh"), save_reps = save_reps) tmp_out <- list(tmp_out, tmp_out_) diff --git a/postprocessing/sim_processing_source.R b/postprocessing/sim_processing_source.R index 8ddb5c49d..5999c745d 100644 --- a/postprocessing/sim_processing_source.R +++ b/postprocessing/sim_processing_source.R @@ -30,86 +30,93 @@ combine_and_format_sims <- function(outcome_vars = "incid", end_date = opt$end_date, geodata, death_filter = opt$death_filter) { - - res_subpop_all <- arrow::open_dataset(sprintf("%shosp",scenario_dir), - partitioning = c("location", "seir_modifiers_scenario", "outcome_modifiers_scenario", "config", "lik_type", "is_final")) %>% - select(time, subpop, outcome_modifiers_scenario, starts_with(outcome_vars)) %>% - filter(time>=forecast_date & time<=end_date) %>% - collect() %>% - filter(stringr::str_detect(outcome_modifiers_scenario, death_filter)) %>% - mutate(time=as.Date(time)) %>% - group_by(time, subpop, outcome_modifiers_scenario) %>% - dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>% - ungroup() - - if (quick_run){ - res_subpop_all <- res_subpop_all %>% filter(sim_num %in% 1:20) - } - gc() - - # ~ Subset if testing - if (testing){ - res_subpop_all <- res_subpop_all %>% filter(sim_num %in% sample(.$sim_num, 10, replace = FALSE)) - } - - - # pull out just the total outcomes of interest - cols_aggr <- expand_grid(a="incid",b=outcomes_) %>% mutate(d=paste0(a,b)) %>% pull(d) - cols_aggr <- cols_aggr[cols_aggr %in% colnames(res_subpop_all)] - - if(!keep_all_compartments & !keep_variant_compartments & !keep_vacc_compartments){ - res_subpop_all <- res_subpop_all %>% - select(time, subpop, outcome_modifiers_scenario, sim_num, all_of(cols_aggr)) - - } else if (keep_variant_compartments){ - # pull out just the variant outcomes - cols_vars <- expand_grid(a="incid",b=outcomes_, c=paste0("_", variants_)) %>% mutate(d=paste0(a,b,c)) %>% pull(d) - cols_vars <- cols_vars[cols_vars %in% colnames(res_subpop_all)] - res_subpop_all <- res_subpop_all %>% - select(time, subpop, outcome_modifiers_scenario, sim_num, all_of(cols_vars)) - } else if (keep_all_compartments){ - # remove the aggregate outcomes - res_subpop_all <- res_subpop_all %>% - select(-all_of(cols_vars), -all_of(cols_aggr)) - } else if (keep_vacc_compartments){ - # pull out just the variant outcomes - cols_vars <- expand_grid(a="incid",b=outcomes_, c=paste0("_", vacc_)) %>% mutate(d=paste0(a,b,c)) %>% pull(d) - cols_vars <- cols_vars[cols_vars %in% colnames(res_subpop_all)] - res_subpop_all <- res_subpop_all %>% - select(time, subpop, outcome_modifiers_scenario, sim_num, all_of(cols_vars)) - } - - - # Merge in Geodata - - if(county_level){ - res_state <- res_subpop_all %>% - inner_join(geodata %>% select(subpop, USPS)) %>% - group_by_at(c("USPS", "time", "sim_num", compartment_types)) %>% - summarise(across(starts_with("incid"), sum)) %>% - as_tibble() - } else { - res_state <- res_subpop_all %>% - inner_join(geodata %>% select(subpop, USPS)) - } - rm(res_subpop_all) - - # ~ Add US totals - res_us <- res_state %>% - group_by(time, sim_num, outcome_modifiers_scenario) %>% - summarise(across(starts_with("incid"), sum)) %>% - as_tibble() %>% - mutate(USPS = "US") - res_state <- res_state %>% - bind_rows(res_us) - rm(res_us) - - return(res_state) + + dirs <- list.dirs(path = scenario_dir, recursive = TRUE, full.names = TRUE) + dirs <- dirs[str_detect(dirs, '/hosp')][1] + res_subpop_all <- arrow::open_dataset(dirs, + partitioning = c("lik_type", "is_final")) %>% + select(time, subpop, starts_with(outcome_vars)) %>% + # select(time, subpop, outcome_modifiers_scenario, starts_with(outcome_vars)) %>% + filter(time>=forecast_date & time<=end_date) %>% + collect() %>% + # filter(stringr::str_detect(outcome_modifiers_scenario, death_filter)) %>% + mutate(time=as.Date(time)) %>% + # group_by(time, subpop, outcome_modifiers_scenario) %>% + group_by(time, subpop) %>% + dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>% + ungroup() + + if (quick_run){ + res_subpop_all <- res_subpop_all %>% filter(sim_num %in% 1:20) + } + gc() + + # ~ Subset if testing + if (testing){ + res_subpop_all <- res_subpop_all %>% filter(sim_num %in% sample(.$sim_num, 10, replace = FALSE)) + } + + # pull out just the total outcomes of interest + cols_aggr <- expand_grid(a="incid",b=outcomes_) %>% mutate(d=paste0(a,b)) %>% pull(d) + cols_aggr <- cols_aggr[cols_aggr %in% colnames(res_subpop_all)] + cols_aggr <- "incidH_14to15" + if(!keep_all_compartments & !keep_variant_compartments & !keep_vacc_compartments){ + res_subpop_all <- res_subpop_all %>% + # select(time, subpop, outcome_modifiers_scenario, sim_num, all_of(cols_aggr)) + select(time, subpop, sim_num, all_of(cols_aggr)) + + + } else if (keep_variant_compartments){ + # pull out just the variant outcomes + cols_vars <- expand_grid(a="incid",b=outcomes_, c=paste0("_", variants_)) %>% mutate(d=paste0(a,b,c)) %>% pull(d) + cols_vars <- cols_vars[cols_vars %in% colnames(res_subpop_all)] + res_subpop_all <- res_subpop_all %>% + # select(time, subpop, outcome_modifiers_scenario, sim_num, all_of(cols_vars)) + select(time, subpop, sim_num, all_of(cols_vars)) + } else if (keep_all_compartments){ + # remove the aggregate outcomes + res_subpop_all <- res_subpop_all %>% + select(-all_of(cols_vars), -all_of(cols_aggr)) + } else if (keep_vacc_compartments){ + # pull out just the variant outcomes + cols_vars <- expand_grid(a="incid",b=outcomes_, c=paste0("_", vacc_)) %>% mutate(d=paste0(a,b,c)) %>% pull(d) + cols_vars <- cols_vars[cols_vars %in% colnames(res_subpop_all)] + res_subpop_all <- res_subpop_all %>% + # select(time, subpop, outcome_modifiers_scenario, sim_num, all_of(cols_vars)) + select(time, subpop, sim_num, all_of(cols_vars)) + } + + + # Merge in Geodata + + if(county_level){ + res_state <- res_subpop_all %>% + inner_join(geodata %>% select(subpop, USPS)) %>% + group_by_at(c("USPS", "time", "sim_num", compartment_types)) %>% + summarise(across(starts_with("incid"), sum)) %>% + as_tibble() + } else { + res_state <- res_subpop_all %>% + inner_join(geodata %>% select(subpop, USPS)) + } + rm(res_subpop_all) + + # ~ Add US totals + res_us <- res_state %>% + # group_by(time, sim_num, outcome_modifiers_scenario) %>% + group_by(time, sim_num) %>% + summarise(across(starts_with("incid"), sum)) %>% + as_tibble() %>% + mutate(USPS = "US") + res_state <- res_state %>% + bind_rows(res_us) + rm(res_us) + + return(res_state) } - load_simulations <- function(geodata, sim_directory = arguments$args, forecast_date = opt$forecast_date, @@ -120,25 +127,28 @@ load_simulations <- function(geodata, keep_compartments = TRUE, testing = FALSE){ - res_subpop <- arrow::open_dataset(sprintf("%s/hosp", sim_directory), - partitioning =c("location", - "seir_modifiers_scenario", - "outcome_modifiers_scenario", - "config", - "lik_type", - "is_final")) %>% - select(time, subpop, starts_with("incid"), outcome_modifiers_scenario)%>% - filter(time>=forecast_date & time<=end_date)%>% - collect() %>% - filter(stringr::str_detect(outcome_modifiers_scenario, death_filter))%>% - mutate(time=as.Date(time)) %>% - group_by(time, subpop, outcome_modifiers_scenario) %>% - dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>% - ungroup() %>% - pivot_longer(cols=starts_with("incid"), - names_to = c("outcome",compartment_types), - names_pattern = "(.*)_(.*)_(.*)_(.*)", values_to = "value") %>% - filter(!is.na(outcome)) + + dirs <- list.dirs(path = scenario_dir, recursive = TRUE, full.names = TRUE) + dirs <- dirs[str_detect(dirs, '/hosp')][1] + res_subpop <- arrow::open_dataset(dirs, + partitioning = c("lik_type", "is_final")) %>% + # select(time, subpop, starts_with("incid"), outcome_modifiers_scenario)%>% + select(time, subpop, starts_with("incid"))%>% + filter(time>=forecast_date & time<=end_date)%>% + collect() %>% + # filter(stringr::str_detect(outcome_modifiers_scenario, death_filter))%>% + # filter(stringr::str_detect(death_filter))%>% + mutate(time=as.Date(time)) %>% + group_by(time, subpop) %>% + # group_by(time, subpop, outcome_modifiers_scenario) %>% + dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>% + ungroup() %>% + pivot_longer(cols=starts_with("incid"), + names_to = c("outcome",compartment_types), + names_pattern = paste0(paste(rep("(.*)_",length(compartment_types)), sep="", collapse=""),"(.*)"), + values_to = "value") %>% + # names_pattern = "(.*)_(.*)_(.*)_(.*)", values_to = "value") %>% + filter(!is.na(outcome)) res_subpop <- res_subpop %>% pivot_wider(names_from = outcome, values_from = value) @@ -169,11 +179,12 @@ load_simulations <- function(geodata, res_state <- res_subpop %>% inner_join(geodata %>% select(subpop, USPS)) - if (keep_compartments){ - res_state_long <- res_subpop_long %>% - inner_join(geodata %>% select(subpop, USPS)) - } - rm(res_subpop_long, res_subpop) + # if (keep_compartments){ + # res_state_long <- res_subpop_long %>% + # inner_join(geodata %>% select(subpop, USPS)) + # } + # rm(res_subpop_long, res_subpop) + rm(res_subpop) } # ADD US TOTAL @@ -207,13 +218,6 @@ load_simulations <- function(geodata, } - - - - - - - trans_sims_wide <- function(geodata, sim_directory = arguments$args, forecast_date = opt$forecast_date, @@ -291,8 +295,6 @@ trans_sims_wide <- function(geodata, } - - load_simulations_orig <- function(geodata, sim_directory = arguments$args, forecast_date = opt$forecast_date, @@ -302,24 +304,24 @@ load_simulations_orig <- function(geodata, keep_compartments = TRUE, testing = FALSE){ - res_subpop <- arrow::open_dataset(sprintf("%s/hosp", sim_directory), - partitioning =c("location", - "seir_modifiers_scenario", - "outcome_modifiers_scenario", - "config", - "lik_type", - "is_final")) %>% - select(time, subpop, starts_with("incid"), outcome_modifiers_scenario)%>% + dirs <- list.dirs(path = scenario_dir, recursive = TRUE, full.names = TRUE) + dirs <- dirs[str_detect(dirs, '/hosp')][1] + res_subpop <- arrow::open_dataset(dirs, + partitioning = c("lik_type", "is_final")) %>% + # select(time, subpop, starts_with("incid"), outcome_modifiers_scenario)%>% filter(time>=forecast_date & time<=end_date)%>% collect() %>% - filter(stringr::str_detect(outcome_modifiers_scenario, death_filter))%>% + # filter(stringr::str_detect(outcome_modifiers_scenario, death_filter))%>% mutate(time=as.Date(time)) %>% - group_by(time, subpop, outcome_modifiers_scenario) %>% + # group_by(time, subpop, outcome_modifiers_scenario) %>% + group_by(time, subpop) %>% dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>% ungroup() %>% pivot_longer(cols=starts_with("incid"), names_to = c("outcome",compartment_types), - names_pattern = "(.*)_(.*)_(.*)_(.*)", values_to = "value") %>% + names_pattern = paste0(paste(rep("(.*)_",length(compartment_types)), sep="", collapse=""),"(.*)"), + values_to = "value") %>% + # names_pattern = "(.*)_(.*)_(.*)_(.*)", values_to = "value") %>% filter(!is.na(outcome)) res_subpop_long <- res_subpop @@ -568,8 +570,6 @@ calibrate_outcome <- function(outcome_calib = "incidH", - - # MISC -------------------------------------------------------------------- # Assign point estimate @@ -1023,658 +1023,660 @@ combine_and_format_scenarios <- function( # RUN PROCESSING - All ---------------------------------------------------- process_sims <- function( - scenario_num, - scenarios_all, - scenario_names, - scenario_ids, - proj_id, - projection_date, - forecast_date, - end_date, - smh_or_fch, - round_num, - subname_all, - config_subname, - round_directory, - full_fit = FALSE, - testing = FALSE, - quick_run = FALSE, - outcomes_ = c("I","C","H","D"), - outcomes_time_ = c("weekly","weekly","weekly","weekly"), - outcomes_cum_ = c(TRUE, TRUE, TRUE, TRUE), - outcomes_cumfromgt = c(FALSE, FALSE, TRUE, FALSE), - outcomes_calibrate = c(FALSE, FALSE, TRUE, FALSE), - n_calib_days = 0, - likelihood_prune = FALSE, - keep_variant_compartments = keep_variant_compartments, - keep_vacc_compartments = keep_vacc_compartments, - keep_all_compartments = keep_all_compartments, - variants_ = variants_, - vacc_ = vacc_, - geodata_file = "data/geodata_2019_statelevel.csv", - death_filter = "med", - plot_samp, - gt_data, - summarize_peaks = FALSE, - save_reps = FALSE) { - - - - # SETUP ------------------------------------------------------------------- - # print(scenarios_all) - print(scenarios_all[scenario_num]) - - opt <- list() - errors <- list() - scenario <- scenarios_all[scenario_num] #"baseline_lowVac" - scenario_name <- scenario_names[scenario_num] - scenario_id <- scenario_ids[scenario_num] - opt$scenario <- scenario - opt$scenario_name <- scenario_name - opt$projection_date <- projection_date - opt$forecast_date <- opt$projection_date # same as projection date unless FULL fit, which gets fixed below - opt$end_date <- end_date - - config_name <- paste0(paste(na.omit(c("config", toupper(smh_or_fch), paste0("R", round_num), scenario, subname_all[1], config_subname)), collapse="_"), ".yml") - config <- flepicommon::load_config(config_name) - - if (smh_or_fch=="fch") { - scenario <- proj_id - opt$scenario <- proj_id - } - - #...................................................... - - print( opt$scenario ) - - opt$args <- scenario_dir <- paste0(round_directory, "/", opt$scenario, "/") - out_sub_dir <- NA - - if (testing) out_sub_dir <- "testing" - if (quick_run) out_sub_dir <- "quick" - if (full_fit) opt$forecast_date <- forecast_date - opt$projection_date <- lubridate::as_date(opt$projection_date) - opt$forecast_date <- lubridate::as_date(opt$forecast_date) - forecast_date <- opt$forecast_date - - - reich_locs <- read_csv("https://raw.githubusercontent.com/reichlab/covid19-forecast-hub/master/data-locations/locations.csv") - - - if (full_fit){ - if(!(exists('forecast_date') & !is.na(forecast_date) & !is.null(forecast_date))){ - opt$forecast_date <- "2020-01-01" - }else{ - opt$forecast_date <- forecast_date - } - } - - opt$projection_date <- lubridate::as_date(opt$projection_date) - opt$forecast_date <- lubridate::as_date(opt$forecast_date) - - variants_ <- opt$variants - - #...................................................... - - opt$geodata <- geodata_file #"data/geodata_2019_statelevel.csv" #geodata_territories_2019_statelevel.csv" - opt$death_filter <- death_filter #"med" - opt$outfile <- paste0(opt$projection_date, "-JHU_IDD-CovidSP-", opt$scenario, ifelse(full_fit, "_FULL", ""),ifelse(likelihood_prune, "_LLprune",""), ".csv") - opt$vaccfile <- paste0(opt$projection_date, "-JHU_IDD-CovidSP-", opt$scenario, "_vaccdata", ifelse(full_fit, "_FULL", ""), ".csv") - opt$vaccsumm <- paste0(opt$projection_date, "-JHU_IDD-CovidSP-", opt$scenario, "_vaccsummary", ifelse(full_fit, "_FULL", ""), ".csv") - opt$indiv_sims <- paste0(opt$projection_date, "-JHU_IDD-CovidSP-", opt$scenario, ifelse(full_fit, "_FULL", ""), ".parquet") - - opt$outdir <- ifelse(!is.na(out_sub_dir), paste0(round_directory, out_sub_dir), file.path(round_directory)) - opt$reichify <- TRUE - dir.create(opt$outdir, recursive = TRUE, showWarnings = FALSE) - print(opt$outdir) - - projections_file_path <- file.path(opt$outdir, opt$outfile) - projections_file_path - - opt$forecast_date <- as.Date(opt$forecast_date) - opt$end_date <- as.Date(opt$end_date) - - # Functions --------------------------------------------------------------- - - # Load Data --------------------------------------------------------------- - - # ~ Geodata - geodata <- suppressMessages(readr::read_csv(opt$geodata, col_types = readr::cols(subpop=readr::col_character()))) - - # ~ Ground truth - if (!exists("gt_data")){ - gt_data <- readr::read_csv(file.path(round_directory, "gt_data_clean.csv")) + config_name, + scenario_num, # setup : change + scenarios_all, # setup: change + scenario_names, #set up : change + scenario_ids, # setup: change used once + proj_id, # change setup? + projection_date, + forecast_date, + end_date, + smh_or_fch, + round_num, + subname_all, + config_subname, + round_directory, + full_fit = FALSE, + testing = FALSE, + quick_run = FALSE, + outcomes_ = c("I","C","H","D"), + outcomes_time_ = c("weekly","weekly","weekly","weekly"), + outcomes_cum_ = c(TRUE, TRUE, TRUE, TRUE), + outcomes_cumfromgt = c(FALSE, FALSE, TRUE, FALSE), + outcomes_calibrate = c(FALSE, FALSE, TRUE, FALSE), + n_calib_days = 0, + likelihood_prune = FALSE, + keep_variant_compartments = keep_variant_compartments, + keep_vacc_compartments = keep_vacc_compartments, + keep_all_compartments = keep_all_compartments, + variants_ = variants_, + vacc_ = vacc_, + geodata_file = "data/geodata_2019_statelevel.csv", + # death_filter = "med", + plot_samp, + gt_data, + summarize_peaks = FALSE, + save_reps = FALSE) { + + + + # SETUP ------------------------------------------------------------------- + # print(scenarios_all) + print(scenarios_all[scenario_num]) + + opt <- list() + errors <- list() + scenario <- scenarios_all[scenario_num] #"baseline_lowVac" + scenario_name <- scenario_names[scenario_num] + scenario_id <- scenario_ids[scenario_num] + opt$scenario <- scenarios_all[scenario_num] #"baseline_lowVac" + opt$scenario_name <- scenario_names[scenario_num] + opt$projection_date <- projection_date + opt$forecast_date <- opt$projection_date # same as projection date unless FULL fit, which gets fixed below + opt$end_date <- end_date + + # config_name <- paste0(paste(na.omit(c("config", toupper(smh_or_fch), paste0("R", round_num), scenario, subname_all[1], config_subname)), collapse="_"), ".yml") + config <- flepicommon::load_config(config_name) + + # if (smh_or_fch=="fch") { + # scenario <- proj_id + # opt$scenario <- proj_id + # } + + #...................................................... + + print( opt$scenario ) + + opt$args <- scenario_dir <- paste0(round_directory, "/", opt$scenario, "/") + out_sub_dir <- NA + + if (testing) out_sub_dir <- "testing" + if (quick_run) out_sub_dir <- "quick" + if (full_fit) opt$forecast_date <- forecast_date + opt$projection_date <- lubridate::as_date(opt$projection_date) + opt$forecast_date <- lubridate::as_date(opt$forecast_date) + forecast_date <- opt$forecast_date + + + reich_locs <- read_csv("https://raw.githubusercontent.com/reichlab/covid19-forecast-hub/master/data-locations/locations.csv") + + + if (full_fit){ + if(!(exists('forecast_date') & !is.na(forecast_date) & !is.null(forecast_date))){ + opt$forecast_date <- "2020-01-01" + }else{ + opt$forecast_date <- forecast_date } - - - # Projections ----------------------------------------------------------- - - res_state <- combine_and_format_sims(outcome_vars = paste0("incid", outcomes_), - scenario_dir = opt$args, - quick_run = quick_run, - testing = testing, - outcomes_ = outcomes_, - keep_variant_compartments = keep_variant_compartments, - keep_vacc_compartments = keep_vacc_compartments, - keep_all_compartments = keep_all_compartments, - variants_ = variants_, - vacc_ = vacc_, - county_level=FALSE, - forecast_date = opt$forecast_date, - end_date = opt$end_date, - geodata = geodata, - death_filter = opt$death_filter) - - if(exists("res_state")){ - print(paste("Successfully combined sims for:", scenario)) + } + + opt$projection_date <- lubridate::as_date(opt$projection_date) + opt$forecast_date <- lubridate::as_date(opt$forecast_date) + + # variants_ <- opt$variants + opt$variants <- variants_ + + #...................................................... + + # opt$death_filter <- death_filter #"med" + opt$outfile <- paste0(opt$projection_date, "-JHU_IDD-CovidSP-", opt$scenario, ifelse(full_fit, "_FULL", ""),ifelse(likelihood_prune, "_LLprune",""), ".csv") + opt$vaccfile <- paste0(opt$projection_date, "-JHU_IDD-CovidSP-", opt$scenario, "_vaccdata", ifelse(full_fit, "_FULL", ""), ".csv") + opt$vaccsumm <- paste0(opt$projection_date, "-JHU_IDD-CovidSP-", opt$scenario, "_vaccsummary", ifelse(full_fit, "_FULL", ""), ".csv") + opt$indiv_sims <- paste0(opt$projection_date, "-JHU_IDD-CovidSP-", opt$scenario, ifelse(full_fit, "_FULL", ""), ".parquet") + + opt$outdir <- ifelse(!is.na(out_sub_dir), paste0(round_directory, out_sub_dir), file.path(round_directory)) + opt$reichify <- TRUE + dir.create(opt$outdir, recursive = TRUE, showWarnings = FALSE) + print(opt$outdir) + + projections_file_path <- file.path(opt$outdir, opt$outfile) + projections_file_path + + opt$forecast_date <- as.Date(opt$forecast_date) + opt$end_date <- as.Date(opt$end_date) + + # Functions --------------------------------------------------------------- + + # Load Data --------------------------------------------------------------- + + # ~ Geodata + geodata <- suppressMessages(readr::read_csv(geodata_file, col_types = readr::cols(subpop=readr::col_character()))) + + # ~ Ground truth + if (!exists("gt_data")){ + gt_data <- readr::read_csv(file.path(round_directory, "gt_data_clean.csv")) + } + + + # Projections ----------------------------------------------------------- + + res_state <- combine_and_format_sims(outcome_vars = paste0("incid", outcomes_), + scenario_dir = opt$args, + quick_run = quick_run, + testing = testing, + outcomes_ = outcomes_, + keep_variant_compartments = keep_variant_compartments, + keep_vacc_compartments = keep_vacc_compartments, + keep_all_compartments = keep_all_compartments, + variants_ = variants_, + vacc_ = vacc_, + county_level=FALSE, + forecast_date = opt$forecast_date, + end_date = opt$end_date, + geodata = geodata, + death_filter = config$outcome_modifiers$scenarios) + + if(exists("res_state")){ + print(paste("Successfully combined sims for:", scenario)) + } else { + errors <- append(errors, "res_state not created.") + stop("res_state not created.") + } + + # + # # ~ Individual Sims & Likelihoods ----------------------------------------- + # + # if (likelihood_prune) { + # + # # add sim_id to sims + # sim_ids <- tibble(filename = list.files(sprintf("%s/hosp",opt$args), recursive = TRUE)) + # sim_ids <- sim_ids %>% + # separate(filename, into=c(letters), sep= "[/]", remove=FALSE) %>% + # mutate(sim_id = as.integer(substr(g, 1, 9))) %>% + # select(sim_id) %>% + # mutate(sim_num = seq_along(sim_id)) + # + # res_state <- res_state %>% + # mutate(sim_num=as.integer(sim_num)) %>% + # left_join(sim_ids) + # + # # Pull Likelihood for pruning runs + # res_llik <- arrow::open_dataset(sprintf("%s/llik",opt$args), + # partitioning =c("location", + # "seir_modifiers_scenario", + # "outcome_modifiers_scenario", + # "config", + # "lik_type", + # "is_final")) %>% + # select(filename, subpop, seir_modifiers_scenario, outcome_modifiers_scenario, ll)%>% + # collect() %>% + # distinct() %>% + # filter(stringr::str_detect(outcome_modifiers_scenario, config$outcome_modifiers$scenarios))%>% + # separate(filename, into=c(letters[1:9]), sep= "[/]", remove=FALSE) %>% + # mutate(sim_id = as.integer(substr(i, 1, 9))) %>% + # as_tibble() + # + # + # res_llik %>% filter(subpop=='06000') %>% + # ggplot(aes(x=sim_id, y=ll)) + + # geom_point() + # + # res_llik %>% filter(subpop=='06000') %>% + # ggplot(aes(y=ll)) + + # geom_histogram() + # + # res_llik %>% filter(subpop=='06000') %>% + # mutate(lik = log(-ll)) %>% + # ggplot(aes(y=lik)) + + # geom_histogram() + # + # res_lik_ests <- res_llik %>% + # mutate(lik = log(-ll)) %>% + # group_by(subpop) %>% + # mutate(mean_ll = mean(ll), + # median_ll = median(ll), + # low_ll = quantile(ll, 0.025), + # high_ll = quantile(ll, 0.975)) %>% + # mutate(mean_lik = mean(lik), + # median_lik = median(lik), + # low_lik = quantile(lik, 0.025), + # high_lik = quantile(lik, 0.975)) %>% + # mutate(below025_ll = llhigh_lik) + # + # # to exclude the same number from each state, we will use quantile approximates + # n_excl <- ceiling(nrow(sim_ids)*(1-likelihood_prune_percentkeep)) + # + # res_lik_ests <- res_lik_ests %>% + # group_by(subpop, seir_modifiers_scenario, outcome_modifiers_scenario) %>% + # arrange(ll) %>% + # mutate(rank = seq_along(subpop), + # excl_rank = rank<=n_excl) %>% + # ungroup() + # + # # res_lik_ests %>% + # # group_by(subpop) %>% + # # summarise(n_excl_ll = sum(below025_ll), + # # n_excl_lik = sum(below025_lik)) %>% View + # # res_lik_ests %>% + # # group_by(sim_id) %>% + # # summarise(n_excl_ll = sum(below025_ll), + # # n_excl_lik = sum(below025_lik)) %>% View + # + # res_lik_excl <- res_lik_ests %>% + # select(subpop, sim_id, exclude=excl_rank, ll, lik) + # + # res_state <- res_state %>% left_join(res_lik_excl) #%>% select(-outcome_modifiers_scenario) + # + # # Save it + # # arrow::write_parquet(res_state_indivs, file.path(opt$outdir, opt$indiv_sims)) + # # If pruning by LLik + # res_state <- res_state %>% + # filter(!exclude) %>% + # select(-sim_id, -exclude) %>% + # group_by(time, subpop, USPS, outcome_modifiers_scenario) %>% + # dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>% + # ungroup() + # + # } + # + # + # + # # ~ Plot some sims ------------------------------ + # + # plot_samp = ifelse(smh_or_fch=="smh", plot_samp, FALSE) + # if (plot_samp) { + # + # gt_data_wUS <- gt_data %>% + # bind_rows(gt_data %>% + # group_by()) + # + # plot_sims <- function(state_ = "MD", res_state_long=res_state_long, gt_data = gt_data_wUS, samp_=NULL){ + # + # if (is.null(samp_)){ + # samp_ <- sample(unique(res_state_long$sim_num), 10, replace=FALSE) + # } + # + # print( + # cowplot::plot_grid( + # ggplot() + + # geom_line(data=res_state_long %>% filter(sim_num %in% samp_) %>% + # filter(USPS == state_) %>% + # filter(outcome == "incidD"), + # aes(x=time, y=value, color=sim_num)) + + # # geom_point(data = gt_data %>% filter(source == state_) %>% rename("value"=incidD, "USPS"=source, "time"=Update), + # # aes(x=time, y=value), alpha=.25, pch=20) + + # ggtitle(paste0(state_, " - incidD")), + # ggplot() + + # geom_line(data=res_state_long %>% filter(sim_num %in% samp_) %>% + # filter(USPS == state_) %>% + # filter(outcome == "incidC"), + # aes(x=time, y=value, color=sim_num)) + + # # geom_point(data = gt_data %>% filter(source == state_) %>% rename("value"=incidC, "USPS"=source, "time"=Update), + # # aes(x=time, y=value), alpha=.25, pch=20) + + # ggtitle(paste0(state_, " - incidC")), + # res_state_long %>% filter(sim_num %in% samp_) %>% + # filter(USPS == state_) %>% + # filter(outcome == "incidI") %>% + # ggplot(aes(x=time, y=value, color=sim_num)) + + # geom_line() + ggtitle(paste0(state_, " - incidI")), + # align="hv", axis = "lr", nrow=3)) + # + # } + # + # states_ <- sort(unique(res_state_long$USPS)) + # pdf(file= paste0(opt$outdir, paste0("SampleSims_",opt$scenario,".pdf"))) + # samp_ <- sample(unique(res_state_long$sim_num), 10, replace=FALSE) + # sapply(states_, plot_sims, res_state_long=res_state_long, gt_data = gt_data, samp_=samp_) + # dev.off() + # + # # samp_ <- sample(unique(res_state_long$sim_num), 10, replace=FALSE) + # # plot_sims(state_ = "US", res_state_long=res_state_long, gt_data = gt_data, samp_) + # plot_sims(state_ = "CA", res_state_long=res_state_long, gt_data = gt_data, samp_) + # # plot_sims(state_ = "MD", res_state_long=res_state_long, gt_data = gt_data, samp_) + # } + # + # + + # GET SIM OUTCOMES ------------------------------------------------------------------- + + use_obs_data_forcum <- ifelse(any(outcomes_cumfromgt),TRUE, FALSE) + gt_data_2 <- gt_data + # colnames(gt_data_2) <- gsub("cumI", "cumC", colnames(gt_data_2)) + gt_data_2 <- gt_data_2 %>% mutate(cumH = 0) # incidH is only cumulative from start of simulation + + # outcomes_gt_ <- outcomes_[outcomes_!="I"] + # outcomes_cum_gt_ <- outcomes_cum_[outcomes_!="I"] + # + # gt_data_2 <- gt_data_2 %>% + # select(USPS, subpop, time, paste0("incid", outcomes_gt_), paste0("cum", outcomes_[outcomes_cum_gt_])) + + # ~ Weekly Outcomes ----------------------------------------------------------- + + if (any(outcomes_time_=="weekly")) { + + # Incident + weekly_incid_sims <- get_weekly_incid(res_state, outcomes = outcomes_[outcomes_time_=="weekly"]) + weekly_incid_sims_formatted <- format_weekly_outcomes(weekly_incid_sims, point_est=0.5, opt) + + if(exists("weekly_incid_sims_formatted")){ + print(paste("Successfully created weekly incidence for:", scenario)) } else { - errors <- append(errors, "res_state not created.") - stop("res_state not created.") + errors <- append(errors, "weekly incidence not created.") + stop("res_state not created.") } - - - # ~ Individual Sims & Likelihoods ----------------------------------------- - - if (likelihood_prune) { - - # add sim_id to sims - sim_ids <- tibble(filename = list.files(sprintf("%s/hosp",opt$args), recursive = TRUE)) - sim_ids <- sim_ids %>% - separate(filename, into=c(letters), sep= "[/]", remove=FALSE) %>% - mutate(sim_id = as.integer(substr(g, 1, 9))) %>% - select(sim_id) %>% - mutate(sim_num = seq_along(sim_id)) - - res_state <- res_state %>% - mutate(sim_num=as.integer(sim_num)) %>% - left_join(sim_ids) - - # Pull Likelihood for pruning runs - res_llik <- arrow::open_dataset(sprintf("%s/llik",opt$args), - partitioning =c("location", - "seir_modifiers_scenario", - "outcome_modifiers_scenario", - "config", - "lik_type", - "is_final")) %>% - select(filename, subpop, seir_modifiers_scenario, outcome_modifiers_scenario, ll)%>% - collect() %>% - distinct() %>% - filter(stringr::str_detect(outcome_modifiers_scenario, opt$death_filter))%>% - separate(filename, into=c(letters[1:9]), sep= "[/]", remove=FALSE) %>% - mutate(sim_id = as.integer(substr(i, 1, 9))) %>% - as_tibble() - - - res_llik %>% filter(subpop=='06000') %>% - ggplot(aes(x=sim_id, y=ll)) + - geom_point() - - res_llik %>% filter(subpop=='06000') %>% - ggplot(aes(y=ll)) + - geom_histogram() - - res_llik %>% filter(subpop=='06000') %>% - mutate(lik = log(-ll)) %>% - ggplot(aes(y=lik)) + - geom_histogram() - - res_lik_ests <- res_llik %>% - mutate(lik = log(-ll)) %>% - group_by(subpop) %>% - mutate(mean_ll = mean(ll), - median_ll = median(ll), - low_ll = quantile(ll, 0.025), - high_ll = quantile(ll, 0.975)) %>% - mutate(mean_lik = mean(lik), - median_lik = median(lik), - low_lik = quantile(lik, 0.025), - high_lik = quantile(lik, 0.975)) %>% - mutate(below025_ll = llhigh_lik) - - # to exclude the same number from each state, we will use quantile approximates - n_excl <- ceiling(nrow(sim_ids)*(1-likelihood_prune_percentkeep)) - - res_lik_ests <- res_lik_ests %>% - group_by(subpop, seir_modifiers_scenario, outcome_modifiers_scenario) %>% - arrange(ll) %>% - mutate(rank = seq_along(subpop), - excl_rank = rank<=n_excl) %>% - ungroup() - - # res_lik_ests %>% - # group_by(subpop) %>% - # summarise(n_excl_ll = sum(below025_ll), - # n_excl_lik = sum(below025_lik)) %>% View - # res_lik_ests %>% - # group_by(sim_id) %>% - # summarise(n_excl_ll = sum(below025_ll), - # n_excl_lik = sum(below025_lik)) %>% View - - res_lik_excl <- res_lik_ests %>% - select(subpop, sim_id, exclude=excl_rank, ll, lik) - - res_state <- res_state %>% left_join(res_lik_excl) #%>% select(-outcome_modifiers_scenario) - - # Save it - # arrow::write_parquet(res_state_indivs, file.path(opt$outdir, opt$indiv_sims)) - # If pruning by LLik - res_state <- res_state %>% - filter(!exclude) %>% - select(-sim_id, -exclude) %>% - group_by(time, subpop, USPS, outcome_modifiers_scenario) %>% - dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>% - ungroup() - + + + # Calibrate + outcomes_calib_weekly <- outcomes_[outcomes_calibrate & outcomes_time_=="weekly"] + if (length(outcomes_calib_weekly)>0 & n_calib_days>0){ + weekly_incid_sims_calibrations <- calibrate_outcome(outcome_calib = paste0("incid", outcomes_calib_weekly), + weekly_outcome = TRUE, + n_calib_days = n_calib_days, + gt_data = gt_data, + incid_sims_formatted = weekly_incid_sims_formatted, + incid_sims = weekly_incid_sims, + projection_date = projection_date, + quick_run = quick_run, testing = testing, + keep_variant_compartments = keep_variant_compartments, + keep_vacc_compartments = keep_vacc_compartments, + keep_all_compartments = keep_all_compartments, + variants_ = NULL, vacc_ = NULL, + death_filter = config$outcome_modifiers$scenarios, + opt = opt, + geodata = geodata, + scenario_dir = scenario_dir) + + weekly_incid_sims <- weekly_incid_sims_calibrations$incid_sims_recalib + + weekly_incid_sims_recalib_formatted <- format_weekly_outcomes( + weekly_inc_outcome = weekly_incid_sims %>% filter(outcome_name %in% paste0("incid", outcomes_calib_weekly)), + point_est=0.5, opt) + weekly_incid_sims_formatted <- weekly_incid_sims_formatted %>% + filter(!(outcome %in% paste0("incid", outcomes_calib_weekly))) %>% + bind_rows(weekly_incid_sims_recalib_formatted) + rm(weekly_incid_sims_calibrations) } - - - - # ~ Plot some sims ------------------------------ - - plot_samp = ifelse(smh_or_fch=="smh", plot_samp, FALSE) - if (plot_samp) { - - gt_data_wUS <- gt_data %>% - bind_rows(gt_data %>% - group_by()) - - plot_sims <- function(state_ = "MD", res_state_long=res_state_long, gt_data = gt_data_wUS, samp_=NULL){ - - if (is.null(samp_)){ - samp_ <- sample(unique(res_state_long$sim_num), 10, replace=FALSE) - } - - print( - cowplot::plot_grid( - ggplot() + - geom_line(data=res_state_long %>% filter(sim_num %in% samp_) %>% - filter(USPS == state_) %>% - filter(outcome == "incidD"), - aes(x=time, y=value, color=sim_num)) + - # geom_point(data = gt_data %>% filter(source == state_) %>% rename("value"=incidD, "USPS"=source, "time"=Update), - # aes(x=time, y=value), alpha=.25, pch=20) + - ggtitle(paste0(state_, " - incidD")), - ggplot() + - geom_line(data=res_state_long %>% filter(sim_num %in% samp_) %>% - filter(USPS == state_) %>% - filter(outcome == "incidC"), - aes(x=time, y=value, color=sim_num)) + - # geom_point(data = gt_data %>% filter(source == state_) %>% rename("value"=incidC, "USPS"=source, "time"=Update), - # aes(x=time, y=value), alpha=.25, pch=20) + - ggtitle(paste0(state_, " - incidC")), - res_state_long %>% filter(sim_num %in% samp_) %>% - filter(USPS == state_) %>% - filter(outcome == "incidI") %>% - ggplot(aes(x=time, y=value, color=sim_num)) + - geom_line() + ggtitle(paste0(state_, " - incidI")), - align="hv", axis = "lr", nrow=3)) - - } - - states_ <- sort(unique(res_state_long$USPS)) - pdf(file= paste0(opt$outdir, paste0("SampleSims_",opt$scenario,".pdf"))) - samp_ <- sample(unique(res_state_long$sim_num), 10, replace=FALSE) - sapply(states_, plot_sims, res_state_long=res_state_long, gt_data = gt_data, samp_=samp_) - dev.off() - - # samp_ <- sample(unique(res_state_long$sim_num), 10, replace=FALSE) - # plot_sims(state_ = "US", res_state_long=res_state_long, gt_data = gt_data, samp_) - plot_sims(state_ = "CA", res_state_long=res_state_long, gt_data = gt_data, samp_) - # plot_sims(state_ = "MD", res_state_long=res_state_long, gt_data = gt_data, samp_) - } - - - - # GET SIM OUTCOMES ------------------------------------------------------------------- - - use_obs_data_forcum <- ifelse(any(outcomes_cumfromgt),TRUE, FALSE) - gt_data_2 <- gt_data - # colnames(gt_data_2) <- gsub("cumI", "cumC", colnames(gt_data_2)) - gt_data_2 <- gt_data_2 %>% mutate(cumH = 0) # incidH is only cumulative from start of simulation - - # outcomes_gt_ <- outcomes_[outcomes_!="I"] - # outcomes_cum_gt_ <- outcomes_cum_[outcomes_!="I"] - # - # gt_data_2 <- gt_data_2 %>% - # select(USPS, subpop, time, paste0("incid", outcomes_gt_), paste0("cum", outcomes_[outcomes_cum_gt_])) - - # ~ Weekly Outcomes ----------------------------------------------------------- - - if (any(outcomes_time_=="weekly")) { - - # Incident - weekly_incid_sims <- get_weekly_incid(res_state, outcomes = outcomes_[outcomes_time_=="weekly"]) - weekly_incid_sims_formatted <- format_weekly_outcomes(weekly_incid_sims, point_est=0.5, opt) - - if(exists("weekly_incid_sims_formatted")){ - print(paste("Successfully created weekly incidence for:", scenario)) - } else { - errors <- append(errors, "weekly incidence not created.") - stop("res_state not created.") - } - - - # Calibrate - outcomes_calib_weekly <- outcomes_[outcomes_calibrate & outcomes_time_=="weekly"] - if (length(outcomes_calib_weekly)>0 & n_calib_days>0){ - weekly_incid_sims_calibrations <- calibrate_outcome(outcome_calib = paste0("incid", outcomes_calib_weekly), - weekly_outcome = TRUE, - n_calib_days = n_calib_days, - gt_data = gt_data, - incid_sims_formatted = weekly_incid_sims_formatted, - incid_sims = weekly_incid_sims, - projection_date = projection_date, - quick_run = quick_run, testing = testing, - keep_variant_compartments = keep_variant_compartments, - keep_vacc_compartments = keep_vacc_compartments, - keep_all_compartments = keep_all_compartments, - variants_ = NULL, vacc_ = NULL, - death_filter = death_filter, - opt = opt, - geodata = geodata, - scenario_dir = scenario_dir) - - weekly_incid_sims <- weekly_incid_sims_calibrations$incid_sims_recalib - - weekly_incid_sims_recalib_formatted <- format_weekly_outcomes( - weekly_inc_outcome = weekly_incid_sims %>% filter(outcome_name %in% paste0("incid", outcomes_calib_weekly)), - point_est=0.5, opt) - weekly_incid_sims_formatted <- weekly_incid_sims_formatted %>% - filter(!(outcome %in% paste0("incid", outcomes_calib_weekly))) %>% - bind_rows(weekly_incid_sims_recalib_formatted) - rm(weekly_incid_sims_calibrations) - } - - - # Cumulative - weekly_cum_outcomes_ <- outcomes_[outcomes_cum_ & outcomes_time_=="weekly"] - if (length(weekly_cum_outcomes_)>0) { - weekly_cum_sims <- get_cum_sims(sim_data = weekly_incid_sims %>% - mutate(agestrat="age0to130") %>% - rename(outcome = outcome_name, value = outcome) %>% - filter(outcome %in% paste0("incid", weekly_cum_outcomes_)), - obs_data = gt_data_2, - gt_cum_vars = paste0("cum", outcomes_[outcomes_cumfromgt]), # variables to get cum from GT - forecast_date = lubridate::as_date(opt$forecast_date), - aggregation="week", - loc_column = "USPS", - use_obs_data = use_obs_data_forcum) - - weekly_cum_sims_formatted <- format_weekly_outcomes( - weekly_cum_sims %>% rename(outcome_name = outcome, outcome = value), - point_est = 0.5, - opt = opt) - - if(exists("weekly_cum_sims_formatted")){ - print(paste("Successfully created weekly cumulative for:", scenario)) - } else { - errors <- append(errors, "weekly cumulative not created.") - stop("res_state not created.") - } - } + + + # Cumulative + weekly_cum_outcomes_ <- outcomes_[outcomes_cum_ & outcomes_time_=="weekly"] + if (length(weekly_cum_outcomes_)>0) { + weekly_cum_sims <- get_cum_sims(sim_data = weekly_incid_sims %>% + mutate(agestrat="age0to130") %>% + rename(outcome = outcome_name, value = outcome) %>% + filter(outcome %in% paste0("incid", weekly_cum_outcomes_)), + obs_data = gt_data_2, + gt_cum_vars = paste0("cum", outcomes_[outcomes_cumfromgt]), # variables to get cum from GT + forecast_date = lubridate::as_date(opt$forecast_date), + aggregation="week", + loc_column = "USPS", + use_obs_data = use_obs_data_forcum) + + weekly_cum_sims_formatted <- format_weekly_outcomes( + weekly_cum_sims %>% rename(outcome_name = outcome, outcome = value), + point_est = 0.5, + opt = opt) + + if(exists("weekly_cum_sims_formatted")){ + print(paste("Successfully created weekly cumulative for:", scenario)) + } else { + errors <- append(errors, "weekly cumulative not created.") + stop("res_state not created.") + } } - - - # ~ Daily Outcomes ----------------------------------------------------------- - - if (any(outcomes_time_=="daily")) { - - # Incident - daily_incid_sims <- get_daily_incid(res_state, outcomes = outcomes_[outcomes_time_=="daily"]) - daily_incid_sims_formatted <- format_daily_outcomes(daily_incid_sims, point_est=0.5, opt) - - if(exists("daily_incid_sims_formatted")){ - print(paste("Successfully created daily incidence for:", scenario)) - } else { - errors <- append(errors, "daily incidence not created.") - stop("res_state not created.") - } - - # Calibrate - outcomes_calib_daily <- outcomes_[outcomes_calibrate & outcomes_time_=="daily"] - if (length(outcomes_calib_daily)>0 & n_calib_days>0){ - daily_incid_sims_calibrations <- calibrate_outcome(outcome_calib = paste0("incid", outcomes_calib_daily), - weekly_outcome = FALSE, - n_calib_days = n_calib_days, - gt_data = gt_data, - incid_sims_formatted = daily_incid_sims_formatted, - incid_sims = daily_incid_sims, - projection_date = projection_date, - quick_run = quick_run, testing = testing, - keep_variant_compartments = keep_variant_compartments, - keep_vacc_compartments = keep_vacc_compartments, - keep_all_compartments = keep_all_compartments, - variants_ = NULL, vacc_ = NULL, - death_filter = death_filter, - opt = opt, - geodata = geodata, - scenario_dir = scenario_dir) - daily_incid_sims <- daily_incid_sims_calibrations$incid_sims_recalib - - daily_incid_sims_recalib_formatted <- format_daily_outcomes( - daily_inc_outcome = daily_incid_sims %>% filter(outcome_name %in% paste0("incid", outcomes_calib_daily)), - point_est=0.5, opt) - daily_incid_sims_formatted <- daily_incid_sims_formatted %>% - filter(!(outcome %in% paste0("incid", outcomes_calib_daily))) %>% - bind_rows(daily_incid_sims_recalib_formatted) - rm(daily_incid_sims_calibrations) - } - - # Cumulative - daily_cum_outcomes_ <- outcomes_[outcomes_cum_ & outcomes_time_=="daily"] - if (length(daily_cum_outcomes_)>0){ - daily_cum_sims <- get_cum_sims(sim_data = daily_incid_sims %>% - mutate(agestrat="age0to130") %>% - rename(outcome = outcome_name, value = outcome) %>% - filter(outcome %in% paste0("incid", daily_cum_outcomes_)), - obs_data = gt_data_2, - gt_cum_vars = paste0("cum", outcomes_[outcomes_cumfromgt]), # variables to get cum from GT - forecast_date = lubridate::as_date(opt$forecast_date), - aggregation="day", - loc_column = "USPS", - use_obs_data = use_obs_data_forcum) - - daily_cum_sims_formatted <- format_daily_outcomes( - daily_cum_sims %>% rename(outcome_name = outcome, outcome = value), - point_est=0.5, - opt = opt) - - if(exists("daily_cum_sims_formatted")){ - print(paste("Successfully created daily cumulative for:", scenario)) - } else { - errors <- append(errors, "daily cumulative not created.") - stop("res_state not created.") - } - } + } + + + # ~ Daily Outcomes ----------------------------------------------------------- + + if (any(outcomes_time_=="daily")) { + + # Incident + daily_incid_sims <- get_daily_incid(res_state, outcomes = outcomes_[outcomes_time_=="daily"]) + daily_incid_sims_formatted <- format_daily_outcomes(daily_incid_sims, point_est=0.5, opt) + + if(exists("daily_incid_sims_formatted")){ + print(paste("Successfully created daily incidence for:", scenario)) + } else { + errors <- append(errors, "daily incidence not created.") + stop("res_state not created.") } - - - - # ~ Combine Daily, Weekly, Cum ---------------------------------------------- - - all_sims_formatted <- mget(objects(pattern = "_sims_formatted$")) %>% - data.table::rbindlist() %>% - as_tibble() - - - - - - # SAVE REPLICATES ----------------------------------------------- - - if (save_reps) { - - weekly_reps <- weekly_incid_sims %>% - mutate(time = lubridate::as_date(time)) %>% - filter(time >= lubridate::as_date(projection_date) & time <= lubridate::as_date(end_date)) %>% - filter(sim_num %in% sample(unique(weekly_incid_sims$sim_num), ifelse(quick_run, 20, 100), replace = FALSE)) %>% - pivot_wider(names_from = sim_num, values_from = outcome, names_prefix = "sim_") %>% - mutate(age_group = "0-130", - scenario_id = scenario_id, scenario_name=scenario_name) %>% - mutate(model_projection_date=opt$forecast_date) %>% - rename(target_end_date=time) %>% - mutate(location=as.character(cdlTools::fips(USPS))) %>% - mutate(location = ifelse(USPS=="US", "US", location)) %>% - mutate(location=stringr::str_pad(location, width=2, side="left", pad="0")) %>% - mutate(ahead=round(as.numeric(target_end_date - model_projection_date)/7)) %>% - mutate(target = recode(outcome_name, "incidI"="inf", "incidC"="case", "incidH"="hosp", "incidD"="death")) %>% - mutate(target=sprintf(paste0("%d wk ahead inc ", target), ahead)) %>% - pivot_longer(cols=dplyr::starts_with("sim_"), names_to = "sample", values_to = "value") %>% - mutate(sample = gsub("sim_", "", sample)) %>% - as_tibble() %>% - mutate(age_group = "0-130", - scenario_id = scenario_id, scenario_name=scenario_name, model_projection_date=projection_date) %>% - select(scenario_id, scenario_name, model_projection_date, target, - target_end_date, sample, location=USPS, value, age_group) - - replicate_file <- paste0(opt$projection_date, "-JHU_IDD-CovidSP-", opt$scenario_name, "_100reps.parquet") - arrow::write_parquet(weekly_reps, file.path(opt$outdir, replicate_file)) - - if(exists("weekly_reps")) { - print(paste("Successfully created 'weekly_reps' for:", scenario)) - } else { - errors <- append(errors, "'weekly_reps' not created.") - stop("'weekly_reps' not created.") - } + + # Calibrate + outcomes_calib_daily <- outcomes_[outcomes_calibrate & outcomes_time_=="daily"] + if (length(outcomes_calib_daily)>0 & n_calib_days>0){ + daily_incid_sims_calibrations <- calibrate_outcome(outcome_calib = paste0("incid", outcomes_calib_daily), + weekly_outcome = FALSE, + n_calib_days = n_calib_days, + gt_data = gt_data, + incid_sims_formatted = daily_incid_sims_formatted, + incid_sims = daily_incid_sims, + projection_date = projection_date, + quick_run = quick_run, testing = testing, + keep_variant_compartments = keep_variant_compartments, + keep_vacc_compartments = keep_vacc_compartments, + keep_all_compartments = keep_all_compartments, + variants_ = NULL, vacc_ = NULL, + death_filter = config$outcome_modifiers$scenarios, + opt = opt, + geodata = geodata, + scenario_dir = scenario_dir) + daily_incid_sims <- daily_incid_sims_calibrations$incid_sims_recalib + + daily_incid_sims_recalib_formatted <- format_daily_outcomes( + daily_inc_outcome = daily_incid_sims %>% filter(outcome_name %in% paste0("incid", outcomes_calib_daily)), + point_est=0.5, opt) + daily_incid_sims_formatted <- daily_incid_sims_formatted %>% + filter(!(outcome %in% paste0("incid", outcomes_calib_daily))) %>% + bind_rows(daily_incid_sims_recalib_formatted) + rm(daily_incid_sims_calibrations) } - - - - - - # PEAK SUMMARY ------------------------------------------------------------- - # currently only incidH - - if (summarize_peaks) { - peak_timing <- weekly_incid_sims %>% - filter(outcome_name=="incidH") %>% - rename(incidH = outcome) %>% - group_by(USPS, sim_num) %>% - mutate(sim_peak_size = max(incidH, na.rm=TRUE)) %>% - mutate(is_peak = as.integer(incidH==sim_peak_size)) %>% - ungroup() %>% - group_by(USPS, time) %>% - summarise(prob_peak = mean(is_peak, na.rm=TRUE)) %>% - as_tibble() %>% - group_by(USPS) %>% - arrange(time) %>% - mutate(cum_peak_prob = cumsum(prob_peak)) %>% - ungroup() - - peak_timing <- peak_timing %>% - mutate(time = lubridate::as_date(time)) %>% - filter(time >= lubridate::as_date(projection_date) & time <= lubridate::as_date(end_date)) %>% - mutate(age_group = "0-130", - quantile = NA, type = "point", - outcome_name = "incidH", - scenario_id = scenario_id, scenario_name=scenario_name) %>% - mutate(model_projection_date=opt$forecast_date) %>% - rename(target_end_date=time) %>% - mutate(location=as.character(cdlTools::fips(USPS))) %>% - mutate(location = ifelse(USPS=="US", "US", location)) %>% - mutate(location=stringr::str_pad(location, width=2, side="left", pad="0")) %>% - mutate(ahead=round(as.numeric(target_end_date - model_projection_date)/7)) %>% - mutate(target = recode(outcome_name, "incidI"="inf", "incidC"="case", "incidH"="hosp", "incidD"="death")) %>% - mutate(target=sprintf(paste0("%d wk ahead peak time ", target), ahead)) %>% - as_tibble() %>% - mutate(age_group = "0-130", - model_projection_date=projection_date, - forecast_date = forecast_date) %>% - select(model_projection_date, target, - target_end_date, quantile, type, - location = USPS, value=cum_peak_prob, age_group) - - # PEAK SIZE - - peak_size <- weekly_incid_sims %>% - filter(outcome_name=="incidH") %>% - group_by(USPS, sim_num, outcome_name) %>% - summarise(peak_size = max(outcome, na.rm=TRUE)) %>% - as_tibble() %>% - mutate(age_group = "0-130") %>% - rename(outcome = peak_size) %>% - group_by(USPS, outcome_name, age_group) %>% - summarize(x=list(enframe(c(quantile(outcome, probs=c(0.01, 0.025, seq(0.05, 0.95, by = 0.05), 0.975, 0.99), na.rm=TRUE), - mean=mean(outcome, na.rm=TRUE)), "quantile","outcome"))) %>% - unnest(x) %>% - pivot_wider(names_from = quantile, names_prefix = "quant_", values_from = outcome) %>% - mutate(forecast_date=opt$forecast_date) %>% - mutate(location=as.character(cdlTools::fips(USPS))) %>% - mutate(location = ifelse(USPS=="US", "US", location)) %>% - mutate(location=stringr::str_pad(location, width=2, side="left", pad="0")) %>% - mutate(target = recode(outcome_name, "incidI"="inf", "incidC"="case", "incidH"="hosp", "incidD"="death")) %>% - mutate(target = paste0("peak size ", target)) %>% - pivot_longer(cols=dplyr::starts_with("quant_"), names_to = "quantile", values_to = "value") %>% - mutate(type="quantile") %>% - mutate(quantile2=suppressWarnings(readr::parse_number(quantile)/100)) %>% - mutate(type=replace(type, grepl("mean", quantile),"point")) %>% - as_tibble() %>% - mutate(target_end_date=NA, - forecast_date = forecast_date, - model_projection_date = projection_date) %>% - select(model_projection_date, target, - target_end_date, quantile = quantile2, type, location=USPS, value, age_group) - - if (point_est!="mean"){ - peak_size <- change_point_est(dat = peak_size, point_estimate = point_est) - } - - peaks_ <- peak_timing %>% - full_join(peak_size) %>% - rename(USPS = location) %>% - left_join(reich_locs %>% select(location, USPS = abbreviation)) %>% - mutate(age_group = "0-130") %>% - filter(location %in% reich_locs$location) %>% - select(-USPS) %>% - as_tibble() %>% - mutate(forecast_date = forecast_date) + + # Cumulative + daily_cum_outcomes_ <- outcomes_[outcomes_cum_ & outcomes_time_=="daily"] + if (length(daily_cum_outcomes_)>0){ + daily_cum_sims <- get_cum_sims(sim_data = daily_incid_sims %>% + mutate(agestrat="age0to130") %>% + rename(outcome = outcome_name, value = outcome) %>% + filter(outcome %in% paste0("incid", daily_cum_outcomes_)), + obs_data = gt_data_2, + gt_cum_vars = paste0("cum", outcomes_[outcomes_cumfromgt]), # variables to get cum from GT + forecast_date = lubridate::as_date(opt$forecast_date), + aggregation="day", + loc_column = "USPS", + use_obs_data = use_obs_data_forcum) + + daily_cum_sims_formatted <- format_daily_outcomes( + daily_cum_sims %>% rename(outcome_name = outcome, outcome = value), + point_est=0.5, + opt = opt) + + if(exists("daily_cum_sims_formatted")){ + print(paste("Successfully created daily cumulative for:", scenario)) + } else { + errors <- append(errors, "daily cumulative not created.") + stop("res_state not created.") + } } - - - - - # PUT TOGETHER AND SAVE --------------------------------------------------- - - full_forecast <- all_sims_formatted %>% - as_tibble() %>% - filter(target_end_date<=opt$end_date) %>% - mutate(age_group = "0-130") %>% - filter(location %in% reich_locs$location) %>% - select(-USPS, -outcome) - - if (!full_fit) { - full_forecast <- full_forecast %>% - filter(target_end_date >= lubridate::as_date(forecast_date) | (target == "peak size hosp")) + } + + + + # ~ Combine Daily, Weekly, Cum ---------------------------------------------- + + all_sims_formatted <- mget(objects(pattern = "_sims_formatted$")) %>% + data.table::rbindlist() %>% + as_tibble() + + + + + + # SAVE REPLICATES ----------------------------------------------- + + if (save_reps) { + + weekly_reps <- weekly_incid_sims %>% + mutate(time = lubridate::as_date(time)) %>% + # filter(time >= lubridate::as_date(projection_date) & time <= lubridate::as_date(end_date)) %>% + # filter(sim_num %in% sample(unique(weekly_incid_sims$sim_num), ifelse(quick_run, 20, 100), replace = FALSE)) %>% + filter(sim_num %in% sample(unique(weekly_incid_sims$sim_num), ifelse(quick_run, 20, 3), replace = FALSE)) %>% + pivot_wider(names_from = sim_num, values_from = outcome, names_prefix = "sim_") %>% + mutate(age_group = "0-130", + scenario_id = scenario_id, scenario_name=scenario_name) %>% + mutate(model_projection_date=opt$forecast_date) %>% + rename(target_end_date=time) %>% + mutate(location=as.character(cdlTools::fips(USPS))) %>% + mutate(location = ifelse(USPS=="US", "US", location)) %>% + mutate(location=stringr::str_pad(location, width=2, side="left", pad="0")) %>% + mutate(ahead=round(as.numeric(target_end_date - model_projection_date)/7)) %>% + mutate(target = recode(outcome_name, "incidI"="inf", "incidC"="case", "incidH"="hosp", "incidD"="death")) %>% + mutate(target=sprintf(paste0("%d wk ahead inc ", target), ahead)) %>% + pivot_longer(cols=dplyr::starts_with("sim_"), names_to = "sample", values_to = "value") %>% + mutate(sample = gsub("sim_", "", sample)) %>% + as_tibble() %>% + mutate(age_group = "0-130", + scenario_id = scenario_id, scenario_name=scenario_name, model_projection_date=projection_date) %>% + select(scenario_id, scenario_name, model_projection_date, target, + target_end_date, sample, location=USPS, value, age_group) + + replicate_file <- paste0(opt$projection_date, "-JHU_IDD-CovidSP-", opt$scenario_name, "_100reps.parquet") + arrow::write_parquet(weekly_reps, file.path(opt$outdir, replicate_file)) + + if(exists("weekly_reps")) { + print(paste("Successfully created 'weekly_reps' for:", scenario)) + } else { + errors <- append(errors, "'weekly_reps' not created.") + stop("'weekly_reps' not created.") } - - if (summarize_peaks){ - full_forecast <- full_forecast %>% full_join(peaks_) + } + + + + + + # PEAK SUMMARY ------------------------------------------------------------- + # currently only incidH + + if (summarize_peaks) { + peak_timing <- weekly_incid_sims %>% + filter(outcome_name=="incidH") %>% + rename(incidH = outcome) %>% + group_by(USPS, sim_num) %>% + mutate(sim_peak_size = max(incidH, na.rm=TRUE)) %>% + mutate(is_peak = as.integer(incidH==sim_peak_size)) %>% + ungroup() %>% + group_by(USPS, time) %>% + summarise(prob_peak = mean(is_peak, na.rm=TRUE)) %>% + as_tibble() %>% + group_by(USPS) %>% + arrange(time) %>% + mutate(cum_peak_prob = cumsum(prob_peak)) %>% + ungroup() + + peak_timing <- peak_timing %>% + mutate(time = lubridate::as_date(time)) %>% + filter(time >= lubridate::as_date(projection_date) & time <= lubridate::as_date(end_date)) %>% + mutate(age_group = "0-130", + quantile = NA, type = "point", + outcome_name = "incidH", + scenario_id = scenario_id, scenario_name=scenario_name) %>% + mutate(model_projection_date=opt$forecast_date) %>% + rename(target_end_date=time) %>% + mutate(location=as.character(cdlTools::fips(USPS))) %>% + mutate(location = ifelse(USPS=="US", "US", location)) %>% + mutate(location=stringr::str_pad(location, width=2, side="left", pad="0")) %>% + mutate(ahead=round(as.numeric(target_end_date - model_projection_date)/7)) %>% + mutate(target = recode(outcome_name, "incidI"="inf", "incidC"="case", "incidH"="hosp", "incidD"="death")) %>% + mutate(target=sprintf(paste0("%d wk ahead peak time ", target), ahead)) %>% + as_tibble() %>% + mutate(age_group = "0-130", + model_projection_date=projection_date, + forecast_date = forecast_date) %>% + select(model_projection_date, target, + target_end_date, quantile, type, + location = USPS, value=cum_peak_prob, age_group) + + # PEAK SIZE + + peak_size <- weekly_incid_sims %>% + filter(outcome_name=="incidH") %>% + group_by(USPS, sim_num, outcome_name) %>% + summarise(peak_size = max(outcome, na.rm=TRUE)) %>% + as_tibble() %>% + mutate(age_group = "0-130") %>% + rename(outcome = peak_size) %>% + group_by(USPS, outcome_name, age_group) %>% + summarize(x=list(enframe(c(quantile(outcome, probs=c(0.01, 0.025, seq(0.05, 0.95, by = 0.05), 0.975, 0.99), na.rm=TRUE), + mean=mean(outcome, na.rm=TRUE)), "quantile","outcome"))) %>% + unnest(x) %>% + pivot_wider(names_from = quantile, names_prefix = "quant_", values_from = outcome) %>% + mutate(forecast_date=opt$forecast_date) %>% + mutate(location=as.character(cdlTools::fips(USPS))) %>% + mutate(location = ifelse(USPS=="US", "US", location)) %>% + mutate(location=stringr::str_pad(location, width=2, side="left", pad="0")) %>% + mutate(target = recode(outcome_name, "incidI"="inf", "incidC"="case", "incidH"="hosp", "incidD"="death")) %>% + mutate(target = paste0("peak size ", target)) %>% + pivot_longer(cols=dplyr::starts_with("quant_"), names_to = "quantile", values_to = "value") %>% + mutate(type="quantile") %>% + mutate(quantile2=suppressWarnings(readr::parse_number(quantile)/100)) %>% + mutate(type=replace(type, grepl("mean", quantile),"point")) %>% + as_tibble() %>% + mutate(target_end_date=NA, + forecast_date = forecast_date, + model_projection_date = projection_date) %>% + select(model_projection_date, target, + target_end_date, quantile = quantile2, type, location=USPS, value, age_group) + + if (point_est!="mean"){ + peak_size <- change_point_est(dat = peak_size, point_estimate = point_est) } - + + peaks_ <- peak_timing %>% + full_join(peak_size) %>% + rename(USPS = location) %>% + left_join(reich_locs %>% select(location, USPS = abbreviation)) %>% + mutate(age_group = "0-130") %>% + filter(location %in% reich_locs$location) %>% + select(-USPS) %>% + as_tibble() %>% + mutate(forecast_date = forecast_date) + } + + + + + # PUT TOGETHER AND SAVE --------------------------------------------------- + + full_forecast <- all_sims_formatted %>% + as_tibble() %>% + filter(target_end_date<=opt$end_date) %>% + mutate(age_group = "0-130") %>% + filter(location %in% reich_locs$location) %>% + select(-USPS, -outcome) + + if (!full_fit) { full_forecast <- full_forecast %>% - mutate(scenario_id = scenario_id, scenario_name = scenario_name, model_projection_date = projection_date) %>% - select(scenario_id, scenario_name, model_projection_date, target, - target_end_date, quantile, type, location, value, age_group) - - - # ---- Save it all - - dir.create(opt$outdir, recursive = TRUE, showWarnings = FALSE) - print(file.path(opt$outdir, opt$outfile)) - opt$outfile <- gsub(".csv", ".parquet", opt$outfile) - arrow::write_parquet(full_forecast, file.path(opt$outdir, opt$outfile)) - - paste0("Outputs saved to : ", file.path(opt$outdir, opt$outfile)) - - - if (exists("full_forecast")) { - print(paste("Successfully created 'full_forecast' for:", scenario)) - } else { - errors <- append(errors, "'full_forecast' not created.") - stop("'full_forecast' not created.") - } - - return(errors) + filter(target_end_date >= lubridate::as_date(forecast_date) | (target == "peak size hosp")) + } + + if (summarize_peaks){ + full_forecast <- full_forecast %>% full_join(peaks_) + } + + full_forecast <- full_forecast %>% + mutate(scenario_id = scenario_id, scenario_name = scenario_name, model_projection_date = projection_date) %>% + select(scenario_id, scenario_name, model_projection_date, target, + target_end_date, quantile, type, location, value, age_group) + + + # ---- Save it all + + dir.create(opt$outdir, recursive = TRUE, showWarnings = FALSE) + print(file.path(opt$outdir, opt$outfile)) + opt$outfile <- gsub(".csv", ".parquet", opt$outfile) + arrow::write_parquet(full_forecast, file.path(opt$outdir, opt$outfile)) + + paste0("Outputs saved to : ", file.path(opt$outdir, opt$outfile)) + + + if (exists("full_forecast")) { + print(paste("Successfully created 'full_forecast' for:", scenario)) + } else { + errors <- append(errors, "'full_forecast' not created.") + stop("'full_forecast' not created.") + } + + return(errors) } From a80a672d1f4de3e7113705e416880cd2a1de2751 Mon Sep 17 00:00:00 2001 From: Alison Hill <34223923+alsnhll@users.noreply.github.com> Date: Thu, 9 Nov 2023 00:02:56 -0500 Subject: [PATCH 50/50] improved error message for problems with timeseries parameter file dates --- flepimop/gempyor_pkg/src/gempyor/parameters.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/flepimop/gempyor_pkg/src/gempyor/parameters.py b/flepimop/gempyor_pkg/src/gempyor/parameters.py index 674506e8d..e2a63eb49 100644 --- a/flepimop/gempyor_pkg/src/gempyor/parameters.py +++ b/flepimop/gempyor_pkg/src/gempyor/parameters.py @@ -71,17 +71,20 @@ def __init__( print("loaded dates:", df.index) raise ValueError( f"""ERROR loading file {fn_name} for parameter {pn}: - the 'date' index of the provided file does not cover the whole config time span from - {ti}->{tf}, where we have dates from {str(df.index[0])} to {str(df.index[-1])}""" + the 'date' entries of the provided file do not include all the days specified to be modeled by + the config. the provided file includes {len(df.index)} days between {str(df.index[0])} to {str(df.index[-1])}, + while there are {len(pd.date_range(ti, tf))} days in the config time span of {ti}->{tf}. The file must contain entries for the + the exact start and end dates from the config. """ ) - # check the date range, need the lenght to be equal if not (pd.date_range(ti, tf) == df.index).all(): print("config dates:", pd.date_range(ti, tf)) print("loaded dates:", df.index) raise ValueError( f"""ERROR loading file {fn_name} for parameter {pn}: - the 'date' index of the provided file does not cover the whole config time span from - {ti}->{tf}""" + the 'date' entries of the provided file do not include all the days specified to be modeled by + the config. the provided file includes {len(df.index)} days between {str(df.index[0])} to {str(df.index[-1])}, + while there are {len(pd.date_range(ti, tf))} days in the config time span of {ti}->{tf}. The file must contain entries for the + the exact start and end dates from the config. """ ) self.pdata[pn]["ts"] = df