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5_results_vaccsim_sensitivity.R
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5_results_vaccsim_sensitivity.R
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# Housekeeping -------------------------------------------------
# Authors: Felix Guenther, Fabienne Krauer
##########################################
library(tidyverse)
theme_set(theme_bw())
source("func_helpers.R")
# fig format
figform <- "png"
dpi <- 600
set.seed(240605122)
# Read and prep data -----------------------------------
inc_1_8 = read_csv('output/vacc_simulations/vaccsim_inc_sensitivity_0_vs_[1, 2, 3, 4, 5, 6, 7, 8].csv')
inc_9_16 = read_csv('output/vacc_simulations/vaccsim_inc_sensitivity_0_vs_[9, 10, 11, 12, 13, 14, 15, 16].csv')
inc_17_18 = read_csv('output/vacc_simulations/vaccsim_inc_sensitivity_0_vs_[17, 18].csv')
inc_19_23 = read_csv('output/vacc_simulations/vaccsim_inc_sensitivity_0_vs_[19, 20, 21, 22, 23].csv')
inc_main = read_csv('output/vacc_simulations/vaccsim_inc_0_vs_[1,2,3].csv') %>%
mutate(strategy_label = factor(strategy,
levels = 1:3,
labels = c("Nirs. HR",
"Nirs. all 70%",
"Mat. vacc. 40% & Pali. HR")),
analysis = "main")
inc_sens = inc_1_8 %>%
full_join(inc_9_16) %>%
full_join(inc_17_18) %>%
full_join(inc_19_23) %>%
mutate(strategy_label = factor(strategy,
levels = 1:23,
labels = c("Nirs. all 10%",
"Nirs. all 20%",
"Nirs. all 30%",
"Nirs. all 40%",
"Nirs. all 50%",
"Nirs. all 60%",
"Nirs. all 80%",
"Nirs. all 90%",
"Mat. vacc. 10% & Pali. HR",
"Mat. vacc. 20% & Pali. HR",
"Mat. vacc. 30% & Pali. HR",
"Mat. vacc. 50% & Pali. HR",
"Mat. vacc. 60% & Pali. HR",
"Mat. vacc. 70% & Pali. HR",
"Mat. vacc. 80% & Pali. HR",
"Mat. vacc. 90% & Pali. HR",
"Mat. vacc. seasonal & Pali. HR",
"Mat. vacc. year-round & Pali. HR",
"Nirs. all, Sep-Mar",
"Nirs. all, Okt-Mar",
"Nirs. all, Dec-Mar",
"Nirs. all, Jan-Mar",
"Nirs. all, Feb-Mar")),
analysis = "sens")
inc_orig = inc_main %>% full_join(inc_sens)
immu_1_8 = read_csv('output/vacc_simulations/vaccsim_immunisations_sensitivity_0_vs_[1, 2, 3, 4, 5, 6, 7, 8].csv')
immu_9_16 = read_csv('output/vacc_simulations/vaccsim_immunisations_sensitivity_0_vs_[9, 10, 11, 12, 13, 14, 15, 16].csv')
immu_17_18 = read_csv('output/vacc_simulations/vaccsim_immunisations_sensitivity_0_vs_[17, 18].csv')
immu_19_23 = read_csv('output/vacc_simulations/vaccsim_immunisations_sensitivity_0_vs_[19, 20, 21, 22, 23].csv')
immu_main = read_csv('output/vacc_simulations/vaccsim_immunisations_0_vs_[1,2,3].csv') %>%
mutate(strategy_label = factor(strategy, levels = 1:3,
labels = c("Nirs. HR",
"Nirs. all 70%",
"Mat. vacc. 40% & Pali. HR")),
analysis = "main")
immu_sens = immu_1_8 %>%
full_join(immu_9_16) %>%
full_join(immu_17_18) %>%
full_join(immu_19_23) %>%
mutate(strategy_label = factor(strategy, levels = 1:23,
labels = c("Nirs. all 10%",
"Nirs. all 20%",
"Nirs. all 30%",
"Nirs. all 40%",
"Nirs. all 50%",
"Nirs. all 60%",
"Nirs. all 80%",
"Nirs. all 90%",
"Mat. vacc. 10% & Pali. HR",
"Mat. vacc. 20% & Pali. HR",
"Mat. vacc. 30% & Pali. HR",
"Mat. vacc. 50% & Pali. HR",
"Mat. vacc. 60% & Pali. HR",
"Mat. vacc. 70% & Pali. HR",
"Mat. vacc. 80% & Pali. HR",
"Mat. vacc. 90% & Pali. HR",
"Mat. vacc. seasonal & Pali. HR",
"Mat. vacc. year-round & Pali. HR",
"Nirs. all, Sep-Mar",
"Nirs. all, Okt-Mar",
"Nirs. all, Dec-Mar",
"Nirs. all, Jan-Mar",
"Nirs. all, Feb-Mar")),
analysis = "sens")
immu = immu_main %>% full_join(immu_sens)
# Calculate ICU and mortality incidence ----------------------------------
inek_smry = read_csv2("data/INEK_derived_endpoint_rates.csv")
inc_extend <- calc_icu_deaths(inc_orig, inek_smry)
# Upscale older adults hospitalisations ----------------------------------------
inc <- inc_extend %>% upscale_oa(scaling_fac = 8)
# Fig. S17 ---------------------
dat_s17 = inc %>% filter(grepl("%", strategy_label),
seasonal=TRUE,
incidence %in% c("symptomatic",
"hospitalised",
"icu",
"mort")) %>%
group_by(incidence, strategy_label, replicate) %>%
summarise(n=sum(n),
n_base = sum(n_base),
n_avert = sum(n_avert)) %>%
group_by(incidence, strategy_label) %>%
summarise(post_med = median(n_avert),
q025 = quantile(n_avert, 0.025),
q975 = quantile(n_avert, 0.975)) %>%
mutate(uptake = str_replace(as.character(strategy_label), "Nirs. all ", ""),
uptake = str_replace(uptake, " & Pali. HR", ""),
uptake = str_replace(uptake, "Mat. vacc. ", ""),
uptake = str_replace(uptake, "%", ""),
uptake = as.numeric(uptake)/100,
strategy_2 = if_else(grepl("Nirs. all", strategy_label),
"Nirs. all",
"Mat. vacc."),
incidence = factor(incidence,
levels = c("symptomatic",
"hospitalised",
"icu",
"mort")))
figS17a = dat_s17 %>%
ggplot() +
geom_point(aes(uptake, post_med, col = strategy_2)) +
geom_linerange(aes(uptake, ymin = q025, ymax=q975, col = strategy_2),
show.legend = FALSE) +
geom_line(aes(uptake, post_med, col = strategy_2), lty = 2) +
facet_wrap(~incidence, scales = "free_y", ncol = 4) +
scale_x_continuous(labels = scales::percent, breaks = c(0.1, 0.3, 0.5, 0.7, 0.9)) +
scale_color_brewer(type="qual") +
ylab("Cases prevented\nvs. Palivizumab HR") +
xlab("Uptake in target group") +
expand_limits(y=0) +
theme(legend.title = element_blank())
dat_s17_b = dat_s17 %>%
left_join(immu %>% group_by(strategy_label, replicate) %>%
summarise(n=sum(n),
n_base = sum(n_base)) %>%
group_by(strategy_label) %>%
summarise(add_immu_post_med = median(n-n_base)/1e6)
)
figS17b = dat_s17_b %>%
ggplot() +
geom_segment(aes(x = `add_immu_post_med_Nirs. all`,
xend = `add_immu_post_med_Mat. vacc.`,
y = `post_med_Nirs. all`,
yend = `post_med_Mat. vacc.`),
data = dat_s17_b %>% select(incidence,
add_immu_post_med, strategy_2,
uptake, post_med) %>%
pivot_wider(values_from = c(add_immu_post_med, post_med),
names_from = strategy_2,
id_cols = c(incidence, uptake)),
col = "black", lty = 2, show.legend = FALSE) +
geom_point(aes(add_immu_post_med, post_med, col = strategy_2)) +
geom_linerange(aes(add_immu_post_med, ymin = q025, ymax=q975, col = strategy_2),
show.legend = FALSE) +
geom_line(aes(add_immu_post_med, post_med, col = strategy_2), lty = 2) +
facet_wrap(~incidence, scales = "free_y", ncol = 4) +
scale_x_continuous() +
scale_color_brewer(type="qual") +
ylab("Cases prevented\nvs. Palivizumab HR") +
xlab("Number of individuals additionally immunised (millions)") +
expand_limits(y=0, x=0) +
theme(legend.title = element_blank())
figS17 = ggpubr::ggarrange(figS17a, figS17b, nrow = 2, common.legend = T,
legend = "bottom",
labels = "AUTO")
ggsave(paste0("output/figures/figS17.", figform),
figS17, width = 10, height = 6, dpi = dpi)
# Fig. S18 ---------------------
s18_dat = inc %>%
filter(strategy_label %in% c("Mat. vacc. seasonal & Pali. HR",
"Mat. vacc. year-round & Pali. HR"),
incidence %in% c("symptomatic",
"hospitalised",
"icu",
"mort")) %>%
mutate(incidence = factor(incidence,
levels = c("symptomatic",
"hospitalised",
"icu",
"mort"))) %>%
left_join(immu %>%
mutate(add_immu = n-n_base) %>%
select(age, year, strategy_label, replicate, add_immu) %>%
group_by(age, year, strategy_label, replicate) %>%
summarise(add_immu = sum(add_immu))) %>%
group_by(incidence, strategy_label, replicate) %>%
summarise(n=sum(n),
n_base = sum(n_base),
n_avert = sum(n_avert),
add_immu = sum(add_immu)) %>%
group_by(incidence, strategy_label) %>%
summarise(post_med = median(n_avert/5),
q025 = quantile(n_avert/5, 0.025),
q975 = quantile(n_avert/5, 0.975),
nnv_med = median(add_immu/n_avert),
nnv_q025 = quantile(add_immu/n_avert, 0.025),
nnv_q975 = quantile(add_immu/n_avert, 0.975)) %>%
mutate(strategy_label = factor(strategy_label,
levels = c("Mat. vacc. seasonal & Pali. HR",
"Mat. vacc. year-round & Pali. HR"),
labels = c("Mat vacc. seasonal",
"Mat vacc. year-round")))
figS18a = s18_dat %>%
mutate(inc_avert_print = round_print(post_med)) %>%
ggplot() +
geom_col(aes(strategy_label, post_med),
fill = "lightgreen",
alpha = .5, col = "black") +
geom_label(aes(strategy_label,
post_med,
label = label_form(post_med),
hjust=1), fill = "lightgrey",
vjust=1, nudge_x = .45, size=5
) +
geom_linerange(aes(strategy_label, ymin = q025,
ymax=q975)) +
facet_wrap(~incidence, scales = "free_x", ncol = 4) +
ylab("Prevented cases") +
scale_x_discrete(guide = guide_axis(angle = 0)) + xlab("") +
scale_y_continuous(n.breaks = 3) +
scale_fill_brewer(type = "qual") +
coord_flip() +
theme(legend.position = "none")
figS18b = s18_dat %>%
mutate(nnv_print = round_print(nnv_med)) %>%
ggplot() +
geom_col(aes(strategy_label, nnv_med ),
fill = "lightgreen",
alpha = .5, col = "black") +
geom_label(aes(strategy_label,
nnv_med ,
label = label_form(nnv_med),
hjust=1), fill = "lightgrey",
vjust=1, nudge_x = .45, size=5
) +
geom_linerange(aes(strategy_label, ymin = nnv_q025,
ymax=nnv_q975)) +
facet_wrap(~incidence, scales = "free_x", ncol = 4) +
ylab("NNV") +
scale_x_discrete(guide = guide_axis(angle = 0)) + xlab("") +
scale_y_continuous(n.breaks = 3) +
scale_fill_brewer(type = "qual") +
coord_flip() +
theme(legend.position = "none")
figS18 = ggpubr::ggarrange(figS18a, figS18b, nrow = 2, common.legend = T,
legend = "bottom",
labels = "AUTO")
ggsave(paste0("output/figures/figS18.", figform),
figS18, width = 10, height = 6, dpi = dpi)