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loopingCISNETOverScenarios.m
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% DESCRIPTION: Looping vaxCEA_multSims_CISNET_UWRuns.m over all the scenario
% numbers, cleaning the output, turning into an array, and exporting to CSV for
% processing in R.
function loopingCISNETOverScenarios(username)
clear;
% Initialize variables
[stepsPerYear , timeStep , startYear , currYear , endYear , ...
years , disease , viral , hpvVaxStates , hpvNonVaxStates , endpoints , ...
intervens , gender , age , risk , hpvTypeGroups , dim , k , toInd , ...
annlz , ...
ageSexDebut , mInit , fInit , partnersM , partnersF , maleActs , ...
femaleActs , riskDist , fertility , fertility2 , fertility3 , fertility4 , ...
mue , mue2 , mue3 , mue4 , epsA_vec , epsR_vec , ...
yr , ...
hivOn , betaHIV_mod , muHIV , kCD4 , ...
hpvOn , beta_hpvVax_mod , beta_hpvNonVax_mod , fImm , rImmune , ...
kCin1_Inf , kCin2_Cin1 , kCin3_Cin2 , kCC_Cin3 , rNormal_Inf , kInf_Cin1 , ...
kCin1_Cin2 , kCin2_Cin3 , lambdaMultImm , hpv_hivClear , rImmuneHiv , ...
c3c2Mults , c2c1Mults , c2c3Mults , c1c2Mults , muCC , kRL , kDR , artHpvMult , ...
hpv_hivMult , maleHpvClearMult , ...
condUse , screenYrs , hpvScreenStartYear , ...
artYr , maxRateM , maxRateF , ...
artYr_vec , artM_vec , artF_vec , minLim , maxLim , ...
circ_aVec , vmmcYr_vec , vmmc_vec , vmmcYr , vmmcRate , ...
hivStartYear , circStartYear , circNatStartYear , vaxStartYear , ...
baseline , who , spCyto , spHpvDna , spGentyp , spAve , spHpvAve , ...
circProtect , condProtect , MTCTRate , hyst , ...
OMEGA , ...
ccInc2012_dObs , ccInc2018_dObs , cc_dist_dObs , cin3_dist_dObs , ...
cin1_dist_dObs , hpv_dist_dObs , cinPos2002_dObs , cinNeg2002_dObs , ...
cinPos2015_dObs , cinNeg2015_dObs , hpv_hiv_dObs , hpv_hivNeg_dObs , ...
hpv_hivM2008_dObs , hpv_hivMNeg2008_dObs , hivPrevM_dObs , hivPrevF_dObs , ...
popAgeDist_dObs , totPopSize_dObs , ...
hivCurr , ...
gar , hivSus , hpvVaxSus , hpvVaxImm , hpvNonVaxSus , hpvNonVaxImm , ...
toHiv , vaxInds , nonVInds , hpvVaxInf , hpvNonVaxInf , ...
hivInds , ...
cin3hpvVaxIndsFrom , ccLochpvVaxIndsTo , ccLochpvVaxIndsFrom , ...
ccReghpvVaxInds , ccDisthpvVaxInds , cin3hpvNonVaxIndsFrom , ...
ccLochpvNonVaxIndsTo , ccLochpvNonVaxIndsFrom , ccReghpvNonVaxInds , ...
ccDisthpvNonVaxInds , cin1hpvVaxInds , cin2hpvVaxInds , cin3hpvVaxInds , ...
cin1hpvNonVaxInds , cin2hpvNonVaxInds , cin3hpvNonVaxInds , normalhpvVaxInds , ...
immunehpvVaxInds , infhpvVaxInds , normalhpvNonVaxInds , immunehpvNonVaxInds , ...
infhpvNonVaxInds , fromVaxNoScrnInds , fromVaxScrnInds , toNonVaxNoScrnInds , ...
toNonVaxScrnInds , ageInd , riskInd , ...
hivNegNonVMMCinds , hivNegVMMCinds , ...
vlAdvancer , ...
fertMat , hivFertPosBirth , hivFertNegBirth , fertMat2 , ...
hivFertPosBirth2 , hivFertNegBirth2 , fertMat3 , hivFertPosBirth3 , hivFertNegBirth3 , ...
fertMat4 , hivFertPosBirth4 , hivFertNegBirth4 , ...
dFertPos1 , dFertNeg1 , dFertMat1 , dFertPos2 , dFertNeg2 , dFertMat2 , ...
dFertPos3 , dFertNeg3 , dFertMat3 , deathMat , deathMat2 , deathMat3 , deathMat4 , ...
dDeathMat , dDeathMat2 , dDeathMat3 , dMue] = loadUp2(1 , 0 , [] , [] , []);
% Indices of calib runs to plot
% Temporarily commenting out to only run one scenario first to test out
% code
fileInds = {'6_1' , '6_2' , '6_3' , '6_6' , '6_8' , '6_9' , '6_11' , ...
'6_12' , '6_13' , '6_15' , '6_20' , '6_21' , '6_22' , '6_26' , ...
'6_27' , '6_32' , '6_34' , '6_35' , '6_38' , '6_39' , '6_40' , ...
'6_41' , '6_42' , '6_45' , '6_47'}; % 22Apr20Ph2V11 ***************SET ME****************
% fileInds = {'6_1', '6_2'}; % FORTESTING
nRuns = length(fileInds);
lastYear = 2122; % manually set in futureSim
monthlyTimespan = [startYear : timeStep : lastYear]; % list all the timespans in a vector
monthlyTimespan = monthlyTimespan(1 : end-1); % remove the very last date
monthlyTimespanFut = [endYear : timeStep : lastYear]; % screening time span starts at 2021
monthlyTimespanFut = monthlyTimespanFut(1 : end-1);
nTimepoints = length(monthlyTimespan);
nTimepointsFut = length(monthlyTimespanFut);
% parallelizing the for loop
loopSegments = {0 , round(10/2) , 10}; % running 10 scenarios
loopSegmentsLength = length(loopSegments);
% for k = 1 : loopSegmentsLength-1
% parfor j = loopSegments{k}+1 : loopSegments{k+1}
% only run scenario 1
for j = [1] % scenario number
sceNum = j;
sceString = num2str(sceNum); % turn sceNum into string sceString
sce = sceNum; % add one since indices start at 1 (so scenarios will be 1-10 in this case)
% Initialize result matrices
deaths = zeros(nTimepoints, age+1, 3, nRuns); % time, age (1:17), 3 death data elements, number of parameters, 10 scenarios
screenTreat = zeros(nTimepointsFut, age+1, 5, nRuns); % time, age (1:16), 5 screen/treat data elements, number of parameters, 10 scenarios
hpvHealthState = zeros(nTimepoints, age+1, 7, nRuns); % time, age (1:16), 10 HPV/CC health states, number of parameters, 10 scenarios
ccHealthState = zeros(nTimepoints, age+1, 4, nRuns);
hivHealthState = zeros(nTimepoints, age+1, 7, nRuns); % time, age (1:16), 7 HIV health states, number of parameters , 10 scenarios
totalPerAge = zeros(nTimepoints, age+1, nRuns); % to pull the N per age group at each time point , 10 scenarios
vax = zeros(nTimepoints, age+1, nRuns); % number of vaccinations is not stratified by age. only time and parameter. , 10 scenarios
nonDisabHealthState = zeros(nTimepoints, age+1, nRuns);
scen0CinTx = zeros(nTimepointsFut, endpoints, 2, nRuns); % 2 because only LEEP and cryo (for the 3rd dimension, 1 is LEEP 2 is cryo)
newCC = zeros(nTimepoints, age+1, nRuns);
hivDeath = zeros(nTimepoints, nRuns);
womenCount = zeros(nTimepoints, nRuns);
womenCountAge = zeros(nTimepoints, age, gender, nRuns);
womenCountDisease = zeros(nTimepoints, disease, nRuns);
hivPrev = zeros(nTimepoints, age, gender, nRuns);
hivPrevTotal = zeros(nTimepoints, nRuns);
% Feeding in the zeroed result matrix, spitting out the same matrix but with all the counts added in for that scenario
[hivDeath, womenCount, hivPrev, hivPrevTotal, womenCountAge, womenCountDisease] = ...
vaxCEA_multSims_CISNET_UWRuns(1 , sceString , {'0'}, fileInds, hivDeath, womenCount, hivPrev, hivPrevTotal, womenCountAge, womenCountDisease);
% turn all the result matrices into 2D
for param = 1 : nRuns
if (param == 1)
hivDeathReshape = [transpose(monthlyTimespan), param.*ones(nTimepoints, 1), sce.*ones(nTimepoints,1), hivDeath(:, param)];
womenCountReshape = [transpose(monthlyTimespan), param.*ones(nTimepoints, 1), sce.*ones(nTimepoints,1), womenCount(:, param)];
else
hivDeathReshape = [hivDeathReshape; ...
transpose(monthlyTimespan), param.*ones(nTimepoints, 1), sce.*ones(nTimepoints, 1), hivDeath(:, param)];
womenCountReshape = [womenCountReshape; ...
transpose(monthlyTimespan), param.*ones(nTimepoints, 1), sce.*ones(nTimepoints,1), womenCount(:, param)];
end
for d = 1 : disease
if (param == 1 && d == 1)
womenCountDiseaseReshape = [transpose(monthlyTimespan), d.*ones(nTimepoints,1), param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), ...
womenCountDisease(:, d, param)];
else
womenCountDiseaseReshape = [womenCountDiseaseReshape; ...
transpose(monthlyTimespan), d.*ones(nTimepoints,1), param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), ...
womenCountDisease(:, d, param)];
end
end
for a = 1 : age
for g = 1 : gender
if (param == 1 && a == 1 && g == 1)
hivPrevReshape = [transpose(monthlyTimespan), a.*ones(nTimepoints,1), g.*ones(nTimepoints,1), param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), ...
hivPrev(:, a, g, param)];
womenCountAgeReshape = [transpose(monthlyTimespan), a.*ones(nTimepoints,1), g.*ones(nTimepoints,1), param.*ones(nTimepoints,1), ...
sce.*ones(nTimepoints,1), womenCountAge(:, a, g, param)];
else
hivPrevReshape = [hivPrevReshape; ...
transpose(monthlyTimespan), a.*ones(nTimepoints,1), g.*ones(nTimepoints,1), param.*ones(nTimepoints,1), ...
sce.*ones(nTimepoints,1), hivPrev(:, a, g, param)];
womenCountAgeReshape = [womenCountAgeReshape; ...
transpose(monthlyTimespan), a.*ones(nTimepoints,1), g.*ones(nTimepoints,1), param.*ones(nTimepoints,1), ...
sce.*ones(nTimepoints,1), womenCountAge(:, a, g, param)];
end
end
end
% for a = 1 : age + 1
% % turning deaths into 2D
% for var = 1 : 3
% if (param == 1 && a ==1 && var == 1)
% deathsReshape = [transpose(monthlyTimespan), a.*ones(nTimepoints,1), var.*ones(nTimepoints,1), ...
% param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), deaths(:, a, var, param)];
% else
% deathsReshape = [deathsReshape; ...
% transpose(monthlyTimespan), a.*ones(nTimepoints,1), var.*ones(nTimepoints,1), ...
% param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), deaths(:, a, var, param)];
% end
% end
% % turning screening/treatment matrix into 2D
% for var = 1 : 5
% if (param == 1 && a ==1 && var == 1)
% screenTreatReshape = [transpose(monthlyTimespanFut), a.*ones(nTimepointsFut,1), var.*ones(nTimepointsFut,1), ...
% param.*ones(nTimepointsFut,1), sce.*ones(nTimepointsFut,1), screenTreat(:, a, var, param)];
% else
% screenTreatReshape = [screenTreatReshape; ...
% transpose(monthlyTimespanFut), a.*ones(nTimepointsFut,1), var.*ones(nTimepointsFut,1), ...
% param.*ones(nTimepointsFut,1), sce.*ones(nTimepointsFut,1), screenTreat(:, a, var, param)];
% end
% end
% % turning hpv health states matrix into 2D
% for var = 1 : 7
% if (param == 1 && a ==1 && var == 1)
% hpvHealthStateReshape = [transpose(monthlyTimespan), a.*ones(nTimepoints,1), var.*ones(nTimepoints,1), ...
% param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), hpvHealthState(:, a, var, param)];
% else
% hpvHealthStateReshape = [hpvHealthStateReshape;
% transpose(monthlyTimespan), a.*ones(nTimepoints,1), var.*ones(nTimepoints,1), ...
% param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), hpvHealthState(:, a, var, param)];
% end
% end
% % turning cc health states matrix into 2D
% for var = 1 : 4
% if (param == 1 && a ==1 && var == 1)
% ccHealthStateReshape = [transpose(monthlyTimespan), a.*ones(nTimepoints,1), var.*ones(nTimepoints,1), ...
% param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), ccHealthState(:, a, var, param)];
% else
% ccHealthStateReshape = [ccHealthStateReshape;
% transpose(monthlyTimespan), a.*ones(nTimepoints,1), var.*ones(nTimepoints,1), ...
% param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), ccHealthState(:, a, var, param)];
% end
% end
% % turning hiv health states matrix into 2D
% for var = 1 : 7
% if (param == 1 && a ==1 && var == 1)
% hivHealthStateReshape = [transpose(monthlyTimespan), a.*ones(nTimepoints,1), var.*ones(nTimepoints,1), ...
% param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), hivHealthState(:, a, var, param)];
% else
% hivHealthStateReshape = [hivHealthStateReshape;
% transpose(monthlyTimespan), a.*ones(nTimepoints,1), var.*ones(nTimepoints,1), ...
% param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), hivHealthState(:, a, var, param)];
% end
% end
% % turning non disability health states matrix into 2D
% if (param == 1 && a == 1)
% nonDisabHealthStateReshape = [transpose(monthlyTimespan), a.*ones(nTimepoints,1), param.*ones(nTimepoints,1), ...
% sce.*ones(nTimepoints,1), nonDisabHealthState(:, a, param)];
% else
% nonDisabHealthStateReshape = [nonDisabHealthStateReshape; ...
% transpose(monthlyTimespan), a.*ones(nTimepoints,1), param.*ones(nTimepoints,1), ...
% sce.*ones(nTimepoints,1), nonDisabHealthState(:, a, param)];
% end
% % turning total per age matrix into 2D
% if (param == 1 && a == 1)
% totalPerAgeReshape = [transpose(monthlyTimespan), a.*ones(nTimepoints,1), param.*ones(nTimepoints,1), ...
% sce.*ones(nTimepoints,1), totalPerAge(:, a, param)];
% vaxReshape = [transpose(monthlyTimespan), a.*ones(nTimepoints,1), param.*ones(nTimepoints,1), ...
% sce.*ones(nTimepoints,1), vax(:, a, param)];
% newCCReshape = [transpose(monthlyTimespan), a.*ones(nTimepoints,1), param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), ...
% newCC(:, a, param)];
% else
% totalPerAgeReshape = [totalPerAgeReshape;
% transpose(monthlyTimespan), a.*ones(nTimepoints,1), param.*ones(nTimepoints,1), ...
% sce.*ones(nTimepoints,1), totalPerAge(:, a, param)];
% vaxReshape = [vaxReshape;
% transpose(monthlyTimespan), a.*ones(nTimepoints,1), param.*ones(nTimepoints,1), ...
% sce.*ones(nTimepoints,1), vax(:, a, param)];
% newCCReshape = [newCCReshape;
% transpose(monthlyTimespan), a.*ones(nTimepoints,1), param.*ones(nTimepoints,1), sce.*ones(nTimepoints,1), ...
% newCC(:, a, param)];
% end
% end
disp(['Complete Scenario ', num2str(sce), ', Parameter ', num2str(param)])
end
% turn result matrices for scenario 0 cin analysis into 2D
% for param = 1 : nRuns
% for h = 1 : hpvVaxStates
% % turning the cin treatment scenario 0 analysis into 2D
% for var = 1 : 2
% if (param == 1 && h ==1 && var == 1)
% scen0CinTxReshape = [transpose(monthlyTimespanFut), h.*ones(nTimepointsFut,1), var.*ones(nTimepointsFut,1), ...
% param.*ones(nTimepointsFut,1), sce.*ones(nTimepointsFut,1), scen0CinTx(:, h, var, param)];
% else
% scen0CinTxReshape = [scen0CinTxReshape;
% transpose(monthlyTimespanFut), h.*ones(nTimepointsFut,1), var.*ones(nTimepointsFut,1), ...
% param.*ones(nTimepointsFut,1), sce.*ones(nTimepointsFut,1), scen0CinTx(:, h, var, param)];
% end
% end
% end
% end
% turn into arrays
% deathsReshape1 = array2table(deathsReshape, 'VariableNames', {'year', 'age', 'categ', 'paramNum', ...
% 'sceNum', 'count'});
% screenTreatReshape1 = array2table(screenTreatReshape, 'VariableNames', {'year', 'age', 'categ', 'paramNum', ...
% 'sceNum', 'count'});
% hpvHealthStateReshape1 = array2table(hpvHealthStateReshape, 'VariableNames', {'year', 'age', 'categ', 'paramNum', ...
% 'sceNum', 'count'});
% ccHealthStateReshape1 = array2table(ccHealthStateReshape, 'VariableNames', {'year', 'age', 'categ', 'paramNum', ...
% 'sceNum', 'count'});
% hivHealthStateReshape1 = array2table(hivHealthStateReshape, 'VariableNames', {'year', 'age', 'categ', 'paramNum', ...
% 'sceNum', 'count'});
% totalPerAgeReshape1 = array2table(totalPerAgeReshape, 'VariableNames', {'year', 'age', 'paramNum', ...
% 'sceNum', 'count'});
% vaxReshape1 = array2table(vaxReshape, 'VariableNames', {'year', 'age', 'paramNum', ...
% 'sceNum', 'count'});
% nonDisabHealthStateReshape1 = array2table(nonDisabHealthStateReshape, 'VariableNames', {'year', 'age', 'paramNum', ...
% 'sceNum', 'count'});
% scen0CinTxReshape1 = array2table(scen0CinTxReshape, 'VariableNames', {'year', 'hpvState', 'categ', 'paramNum', 'sceNum', 'count'});
% newCCReshape1 = array2table(newCCReshape, 'VariableNames', {'year', 'age', 'paramNum', 'sceNum', 'count'});
hivDeathReshape1 = array2table(hivDeathReshape, 'VariableNames', {'year', 'paramNum', 'sceNum', 'count'});
womenCountReshape1 = array2table(womenCountReshape, 'VariableNames', {'year', 'paramNum', 'sceNum', 'count'});
womenCountAgeReshape1 = array2table(womenCountAgeReshape, 'VariableNames', {'year', 'age', 'gender', 'paramNum', 'sceNum', 'count'});
womenCountDiseaseReshape1 = array2table(womenCountDiseaseReshape, 'VariableNames', {'year', 'disease', 'paramNum', 'sceNum', 'count'});
hivPrevReshape1 = array2table(hivPrevReshape, 'VariableNames', {'year', 'age', 'gender', 'paramNum', 'sceNum', 'count'});
% spit out into CSV
% writetable(deathsReshape1,[pwd '/SACEA/deaths_S' sceString '.csv']);
% writetable(screenTreatReshape1, [pwd '/SACEA/screenTreat_S' sceString '.csv']);
% writetable(hpvHealthStateReshape1, [pwd '/SACEA/hpvHealthState_S' sceString '.csv']);
% writetable(ccHealthStateReshape1, [pwd '/SACEA/ccHealthState_S' sceString '.csv']);
% writetable(hivHealthStateReshape1, [pwd '/SACEA/hivHealthState_S' sceString '.csv']);
% writetable(totalPerAgeReshape1, [pwd '/SACEA/totalPerAge_S' sceString '.csv']);
% writetable(vaxReshape1, [pwd '/SACEA/vax_S' sceString '.csv']);
% writetable(nonDisabHealthStateReshape1, [pwd '/SACEA/nonDisabHealthState_S' sceString '.csv']);
% writetable(newCCReshape1, [pwd '/SACEA/newCC_S' sceString '.csv']);
writetable(hivDeathReshape1, [pwd '/CISNET/hivDeath_S' sceString '.csv']);
writetable(womenCountReshape1, [pwd '/CISNET/womenCount_S' sceString '.csv']);
writetable(womenCountAgeReshape1, [pwd '/CISNET/womenCountAgeGender_S' sceString '.csv']);
writetable(womenCountDiseaseReshape1, [pwd '/CISNET/womenCountDisease_S' sceString '.csv']);
writetable(hivPrevReshape1, [pwd '/CISNET/hivPrevAgeGender_S' sceString '.csv']);
% if sceNum == 0 % only write the cin / treatment file if in scenario 0
% writetable(scen0CinTxReshape1, [pwd '/SACEA/scen0CinTx_S0.csv']);
% end
end
% end
end