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vaxCEA_multSims_WHOsces.m
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vaxCEA_multSims_WHOsces.m
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%function vaxCEA_multSims_WHOsces()
%% LOAD PARAMETERS
paramDir = [pwd , '\Params\'];
[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 , [] , [] , []);
%% LOAD SAVED RESULTS
% ***SET ME***: save names of potential scenarios to analyze as variables
baseDirName = 'Vaccine22Apr20Ph2V11_noBaseVax_baseScreen_shortName_noVMMChpv_discontFxd_screenCovFxd_';
dirName_reductBaseline = [baseDirName , 'WHO-SCES012_6_1'];
dirName_reductBaseline2 = ['Vaccine22Apr20Ph2V11_whoS0bBaseVax_baseScreen_shortName_noVMMChpv_discontFxd_screenCovFxd_' , 'WHO-SCES012_6_1'];
dirName_reductBaseline3 = ['Vaccine22Apr20Ph2V11_whoS0cBaseVax_baseScreen_shortName_noVMMChpv_discontFxd_screenCovFxd_' , 'WHO-SCES012_6_1'];
dirName_reductBaseline4 = ['Vaccine22Apr20Ph2V11_noBaseVax_noBaseScreen_shortName_noVMMChpv_discontFxd_screenCovFxd_' , 'WHO-SCES012_6_1'];
dirName_P1_SCE34 = [baseDirName , 'WHO-SCES34_6_1'];
dirName_P1_SCE56 = [baseDirName , 'WHO-SCES56_6_1'];
dirName_P2_SCE7a7 = [baseDirName , 'WHO-SCES7a7_6_1'];
dirName_P2_SCE7b = [baseDirName , 'WHO-SCES7b_6_1'];
dirName_P2_SCE8 = [baseDirName , 'WHO-SCES8_6_1'];
dirName_P2_SCE9 = [baseDirName , 'WHO-SCES9_6_1'];
dirName_P2_SCE10 = [baseDirName , 'WHO-SCES10_6_1'];
dirName_P2_SCE11 = [baseDirName , 'WHO-SCES11_6_1'];
% ***SET ME***: choose which scenarios you want to save data in Excel for
simVec = {dirName_reductBaseline , dirName_reductBaseline , ...
dirName_P1_SCE34 , dirName_P1_SCE56 , dirName_P2_SCE9};
fileVec = {'sim0' , 'sim2' , 'sim2' , 'sim2' , 'sim2'};
fileVec2 = {'0' , '2' , '4' , '6'};
% ***SET ME***: make sure the names here correspond to scenarios in simVec above
fileTits = {'S0 (no vax, baseline screen)' , 'S2' , ...
'S4' , 'S6 (90% 9v, 2x WHO screen)' , 'S9 (90% 9v, HIV screening)'};
% 'S0- orig method, med' , 'range' , ...
% 'S0- new method, med' , 'range' , ...
% 'S2- orig method, med' , 'range' , ...
% 'S2- new method, med' , 'range'
% % ***SET ME***: choose which scenarios you want to save data in Excel for
% simVec = {dirName_reductBaseline , dirName_reductBaseline4 , ...
% dirName_reductBaseline , dirName_reductBaseline , ...
% dirName_P1_SCE34 , dirName_P1_SCE34 , dirName_P1_SCE56 , dirName_P1_SCE56 , ...
% dirName_P2_SCE9 , dirName_P2_SCE10 , dirName_P2_SCE11};
% fileVec = {'sim0' , 'sim0' , 'sim1' , 'sim2' , 'sim1' , 'sim2' , 'sim1' , 'sim2' , 'sim2' , 'sim2' , 'sim2'};
% % ***SET ME***: make sure the names here correspond to scenarios in simVec above
% fileTits = {'S0 (no vax, baseline screen)' , 'S0d (no vax, no screen)' , ...
% 'P1-S1 (80% 9v, baseline screen)' , 'P1-S2 (90% 9v, baseline screen)' , ...
% 'P1-S3 (80% 9v, screen@35)' , 'P1-S4 (90% 9v, screen@35)' , ...
% 'P1-S5 (80% 9v, screen@35,45)' , 'P1-S6 (90% 9v, screen@35,45)' , ...
% 'P2-S9 (90% 9v, screen by HIV status)' , 'P2-S10(90% 9v, 50% CU, screen by HIV status)' , ...
% 'P2-S11(90% 9v, 90% CU, screen by HIV status)'};
% Plot settings
reset(0)
set(0 , 'defaultlinelinewidth' , 1.5)
nResults = length(simVec);
for j = 1 : nResults
%% CC INCIDENCE - age standardized
% Note: the age-standardization process shifts the incidence rate of the
% last modelled age group to the next age group in the following year.
% However, CC incidence is NaN prior to HIV introduction in the
% HIV-positive no ART group, and NaN prior to ART introduction in the
% HIV-positive ART group. Since we have four age groups past the 16 we
% model, a NaN value is present for four years past the introduction of
% HIV/ART, leading to a NaN value for summed incidence during these
% years. We therefore lack data in this four-year interval in the
% saved/plotted results.
fac = 10 ^ 5;
worldStandard_WP2015 = [325428 311262 295693 287187 291738 299655 272348 ...
247167 240167 226750 201603 171975 150562 113118 82266 64484 42237 ...
23477 9261 2155];
diseaseLabels = {'General' , 'HIV_neg' , 'HIV_posAll' , 'HIV_posNoArt' , 'HIV_posArt'};
for dInd = 1 : 1 %length(diseaseLabels);
% Load results
fname = [pwd , '\HHCoM_Results\' , simVec{j} , '\' , ...
'ICC_' , diseaseLabels{dInd} , '_' , fileVec{j} , '.csv'];
ccIncHivAgeTime = xlsread(fname);
ccIncRefTot = zeros(1 , size(ccIncHivAgeTime,2)-1);
for aInd = 1:age+4
a = aInd;
if aInd >= age
a = age;
end
if aInd <= age
ccIncRef = ccIncHivAgeTime(a+1 , 2:end) .* worldStandard_WP2015(aInd);
if (dInd == 5) && (a < 3)
ccIncRef = zeros(1 , size(ccIncHivAgeTime,2)-1);
end
elseif aInd > age
ccIncRef = ccIncHivAgeTime(a+1 , 2:end);
ccIncRef = [(ones(1,aInd-a).*ccIncRef(1,1)) , ccIncRef(1,1:end-(aInd-a))];
ccIncRef = ccIncRef .* worldStandard_WP2015(aInd);
end
ccIncRefTot = ccIncRefTot + ccIncRef;
end
ccInc = ccIncRefTot ./ (sum(worldStandard_WP2015(1:age+4)));
ccIncRefTot_lb = zeros(1 , size(ccIncHivAgeTime,2)-1);
for aInd = 1:age+4
a = aInd;
if aInd >= age
a = age;
end
if aInd <= age
ccIncRef = ccIncHivAgeTime(a+1+age , 2:end) .* worldStandard_WP2015(aInd);
if (dInd == 5) && (a < 3)
ccIncRef = zeros(1 , size(ccIncHivAgeTime,2)-1);
end
elseif aInd > age
ccIncRef = ccIncHivAgeTime(a+1+age , 2:end);
ccIncRef = [(ones(1,aInd-a).*ccIncRef(1,1)) , ccIncRef(1,1:end-(aInd-a))];
ccIncRef = ccIncRef .* worldStandard_WP2015(aInd);
end
ccIncRefTot_lb = ccIncRefTot_lb + ccIncRef;
end
ccInc_lb = ccIncRefTot_lb ./ (sum(worldStandard_WP2015(1:age+4)));
ccIncRefTot_ub = zeros(1 , size(ccIncHivAgeTime,2)-1);
for aInd = 1:age+4
a = aInd;
if aInd >= age
a = age;
end
if aInd <= age
ccIncRef = ccIncHivAgeTime(a+1+age*2 , 2:end) .* worldStandard_WP2015(aInd);
if (dInd == 5) && (a < 3)
ccIncRef = zeros(1 , size(ccIncHivAgeTime,2)-1);
end
elseif aInd > age
ccIncRef = ccIncHivAgeTime(a+1+age*2 , 2:end);
ccIncRef = [(ones(1,aInd-a).*ccIncRef(1,1)) , ccIncRef(1,1:end-(aInd-a))];
ccIncRef = ccIncRef .* worldStandard_WP2015(aInd);
end
ccIncRefTot_ub = ccIncRefTot_ub + ccIncRef;
end
ccInc_ub = ccIncRefTot_ub ./ (sum(worldStandard_WP2015(1:age+4)));
% Plot baseline incidence
if (j == 1) && (dInd == 1)
fig = figure;
set(fig,'DefaultAxesFontSize' , 18);
end
hold all;
firstYrInd = (currYear-1-startYear)+2;
firstYrInd2 = (currYear-1-startYear)+1;
p = plot(ccIncHivAgeTime(1 , firstYrInd:end) , ccInc(1 , firstYrInd2:end) , '-');
% hold all;
% x2 = [ccIncHivAgeTime(1 , firstYrInd:end) , fliplr(ccIncHivAgeTime(1 , firstYrInd:end))];
% inBetween = [ccInc_ub(1 , firstYrInd2:end) , fliplr(ccInc_lb(1 , firstYrInd2:end))];
% colorP = get(p,'Color');
% h = fill(x2 , inBetween , colorP);
% h.FaceAlpha = 0.3;
% h.LineStyle = '--';
% set(h,'EdgeColor', colorP);
axis([2019 2120 0 90])
grid on;
xlabel('Year'); ylabel('AS ICC per 100K');
%legend('General' , 'HIV_neg' , 'HIV_posAll' , 'HIV_posNoArt' , 'HIV_posArt');
end
%% HPV INCIDENCE - age standardized
% % Note: the age-standardization process shifts the incidence rate of the
% % last modelled age group to the next age group in the following year.
% % However, HPV incidence is NaN prior to HIV introduction in the
% % HIV-positive no ART group, and NaN prior to ART introduction in the
% % HIV-positive ART group. Since we have four age groups past the 16 we
% % model, a NaN value is present for four years past the introduction of
% % HIV/ART, leading to a NaN value for summed incidence during these
% % years. We therefore lack data in this four-year interval in the
% % saved/plotted results.
%
% % Note: a limitation of these plots is that vax and nonVax HPV
% % incidence is currently double-counted in coinfected individuals
%
% fac = 100;
% worldStandard_WP2015 = [325428 311262 295693 287187 291738 299655 272348 ...
% 247167 240167 226750 201603 171975 150562 113118 82266 64484 42237 ...
% 23477 9261 2155];
%
% riskLabels = {'lr' , 'mr' , 'hr'};
% riskPlotLines = {'-' , '--' , ':'};
% diseaseLabels = {'General' , 'HIV_neg' , 'HIV_posAll' , 'HIV_posNoArt' , 'HIV_posArt'};
% for r = 1 : risk
% set(gca,'ColorOrderIndex',1)
% for dInd = 1 : length(diseaseLabels);
% % Load results
% fname = [pwd , '\HHCoM_Results\' , simVec{j} , '\' , ...
% 'HPVinc_' , diseaseLabels{dInd} , '_' , riskLabels{r} , '_' , fileVec{j} , '.csv'];
% hpvIncHivAgeTime = xlsread(fname);
%
% hpvIncRefTot = zeros(1 , size(hpvIncHivAgeTime,2)-1);
% for aInd = 1:age+4
% a = aInd;
% if aInd >= age
% a = age;
% end
%
% if aInd <= age
% hpvIncRef = hpvIncHivAgeTime(a+1 , 2:end) .* worldStandard_WP2015(aInd);
% if (a < 3)
% hpvIncRef = zeros(1 , size(hpvIncHivAgeTime,2)-1);
% end
% elseif aInd > age
% hpvIncRef = hpvIncHivAgeTime(a+1 , 2:end);
% hpvIncRef = [(ones(1,aInd-a).*hpvIncRef(1,1)) , hpvIncRef(1,1:end-(aInd-a))];
% hpvIncRef = hpvIncRef .* worldStandard_WP2015(aInd);
% end
% hpvIncRefTot = hpvIncRefTot + hpvIncRef;
% end
% hpvInc = hpvIncRefTot ./ (sum(worldStandard_WP2015(1:age+4)));
%
% % Plot baseline incidence
% if (j == 1) && (dInd == 1) && (r == 1)
% figure;
% end
% hold all;
% plot(hpvIncHivAgeTime(1 , 2:end) , hpvInc , riskPlotLines{r});
% hold all;
% axis([1980 2120 0 30])
% grid on;
% xlabel('Year'); ylabel('AS HPV incidence per 100');
% legend('General, lr' , 'HIV-neg' , 'HIV-posAll' , 'HIV-posNoArt' , 'HIV-posArt' , ...
% 'General, mr' , 'HIV-neg' , 'HIV-posAll' , 'HIV-posNoArt' , 'HIV-posArt' , ...
% 'General, hr' , 'HIV-neg' , 'HIV-posAll' , 'HIV-posNoArt' , 'HIV-posArt');
% end
% end
% %% CC INCIDENCE - checking effect of method of calculating median
% % Check 1 - effect of age-standardizing the median/lb/ub vs.
% % age-standardizing all 25 simulation results and then calculating the
% % median/lb/ub
% % Check 2 - effect of calculating percent reduction in medians vs. the
% % median percent reduction across all 25 simulations
% dInd = 1;
% diseaseLabels = {'Pop(All) ICC' , 'HIV- (ICC)' , 'HIV+ (ICC)' , 'HIV+ no ART (ICC)' , 'HIV+ ART (ICC)'};
% % Load results, method 1
% fname = [pwd , '\HHCoM_Results\' , simVec{j} , '\' , ...
% 'UofW_Impact_CC_IncidenceRates-standardised-(2020-2120)_S' , fileVec2{j} , '.xlsx'];
% ccIncHivTime1_med = readmatrix(fname , 'Sheet' , diseaseLabels{1} , 'Range' , ['B4:CY4']);
% fname = [pwd , '\HHCoM_Results\' , simVec{j} , '\' , ...
% 'UofW_Impact_CC_IncidenceRates-standardised-(2020-2120)LUB_S' , fileVec2{j} , '.xlsx'];
% ccIncHivTime1_lb = readmatrix(fname , 'Sheet' , diseaseLabels{1} , 'Range' , ['B4:CY4']);
% fname = [pwd , '\HHCoM_Results\' , simVec{j} , '\' , ...
% 'UofW_Impact_CC_IncidenceRates-standardised-(2020-2120)HUB_S' , fileVec2{j} , '.xlsx'];
% ccIncHivTime1_ub = readmatrix(fname , 'Sheet' , diseaseLabels{1} , 'Range' , ['B4:CY4']);
% ccIncHivTime1 = [ccIncHivTime1_med' , ccIncHivTime1_lb' , ccIncHivTime1_ub'];
% % Load results, method 2
% fname = [pwd , '\HHCoM_Results\' , simVec{j} , '\' , 'ICC-checkAS_' , fileVec{j} , '.xlsx'];
% ccIncHivTime2 = readmatrix(fname , 'Sheet' , diseaseLabels{1} , 'Range' , ['B1:AC102']);
%
% % Create figure
% if (j == 1) && (dInd == 1)
% fig = figure;
% set(fig,'DefaultAxesFontSize' , 18);
% end
% hold all;
% subplot(1,3,1);
% % Plot results, method 1
% p = plot([2019:2120] , ccIncHivTime1(:,1)' , '-');
% hold all;
% x2 = [[2019:2120] , fliplr([2019:2120])];
% inBetween = [ccIncHivTime1(:,3)' , fliplr(ccIncHivTime1(:,2)')];
% colorP = get(p,'Color');
% h = fill(x2 , inBetween , colorP);
% h.FaceAlpha = 0.3;
% h.LineStyle = '--';
% set(h,'EdgeColor', colorP);
% % Plot results, method 2
% p2 = plot([2019:2120] , ccIncHivTime2(:,1)' , '-');
% hold all;
% x2 = [[2019:2120] , fliplr([2019:2120])];
% inBetween = [ccIncHivTime2(:,3)' , fliplr(ccIncHivTime2(:,2)')];
% colorP = get(p2,'Color');
% h = fill(x2 , inBetween , colorP);
% h.FaceAlpha = 0.2;
% h.LineStyle = '--';
% set(h,'EdgeColor', colorP);
% axis([2019 2120 0 120])
% grid on;
% xlabel('Year'); ylabel('AS ICC per 100K');
%
% if j > 1
% % PERCENT REDUCTION
% % Baseline
% % Load results, method 1
% fname = [pwd , '\HHCoM_Results\' , simVec{1} , '\' , ...
% 'UofW_Impact_CC_IncidenceRates-standardised-(2020-2120)_S' , fileVec2{1} , '.xlsx'];
% ccIncHivTime1_med = readmatrix(fname , 'Sheet' , diseaseLabels{1} , 'Range' , ['B4:CY4']);
% fname = [pwd , '\HHCoM_Results\' , simVec{1} , '\' , ...
% 'UofW_Impact_CC_IncidenceRates-standardised-(2020-2120)LUB_S' , fileVec2{1} , '.xlsx'];
% ccIncHivTime1_lb = readmatrix(fname , 'Sheet' , diseaseLabels{1} , 'Range' , ['B4:CY4']);
% fname = [pwd , '\HHCoM_Results\' , simVec{1} , '\' , ...
% 'UofW_Impact_CC_IncidenceRates-standardised-(2020-2120)HUB_S' , fileVec2{1} , '.xlsx'];
% ccIncHivTime1_ub = readmatrix(fname , 'Sheet' , diseaseLabels{1} , 'Range' , ['B4:CY4']);
% ccIncHivTime1_baseline = [ccIncHivTime1_med' , ccIncHivTime1_lb' , ccIncHivTime1_ub'];
% % Load results, method 2
% fname = [pwd , '\HHCoM_Results\' , simVec{1} , '\' , 'ICC-checkAS_' , fileVec{1} , '.xlsx'];
% ccIncHivTime2_baseline = readmatrix(fname , 'Sheet' , diseaseLabels{1} , 'Range' , ['B1:AC102']);
%
% hold all;
% subplot(1,3,2);
% % Plot results, method 1
% p3 = plot([2019:2120] , ((ccIncHivTime1(:,1)'-ccIncHivTime1_baseline(:,1)')./ccIncHivTime1_baseline(:,1)').*-100 , '-');
% hold all;
% x2 = [[2019:2120] , fliplr([2019:2120])];
% inBetween = [((ccIncHivTime1(:,3)'-ccIncHivTime1_baseline(:,3)')./ccIncHivTime1_baseline(:,3)').*-100 , ...
% fliplr(((ccIncHivTime1(:,2)'-ccIncHivTime1_baseline(:,2)')./ccIncHivTime1_baseline(:,2)').*-100)];
% colorP = get(p3,'Color');
% h = fill(x2 , inBetween , colorP);
% h.FaceAlpha = 0.3;
% h.LineStyle = '--';
% set(h,'EdgeColor', colorP);
% % Plot results, method 3
% p5 = plot([2019:2120] , ((ccIncHivTime2(:,1)'-ccIncHivTime2_baseline(:,1)')./ccIncHivTime2_baseline(:,1)').*-100 , '-');
% hold all;
% x2 = [[2019:2120] , fliplr([2019:2120])];
% inBetween = [((ccIncHivTime2(:,3)'-ccIncHivTime2_baseline(:,3)')./ccIncHivTime2_baseline(:,3)').*-100 , ...
% fliplr(((ccIncHivTime2(:,2)'-ccIncHivTime2_baseline(:,2)')./ccIncHivTime2_baseline(:,2)').*-100)];
% colorP = get(p5,'Color');
% h = fill(x2 , inBetween , colorP);
% h.FaceAlpha = 0.2;
% h.LineStyle = '--';
% set(h,'EdgeColor', colorP);
% axis([2019 2120 0 100])
% grid on;
% xlabel('Year'); ylabel('Percent reduction');
% legend('Orig method, med' , 'range' , ...
% 'Inter method, med' , 'range');
%
% hold all;
% subplot(1,3,3);
% % Plot results, method 1
% p3 = plot([2019:2120] , ((ccIncHivTime1(:,1)'-ccIncHivTime1_baseline(:,1)')./ccIncHivTime1_baseline(:,1)').*-100 , '-');
% hold all;
% x2 = [[2019:2120] , fliplr([2019:2120])];
% inBetween = [((ccIncHivTime1(:,3)'-ccIncHivTime1_baseline(:,3)')./ccIncHivTime1_baseline(:,3)').*-100 , ...
% fliplr(((ccIncHivTime1(:,2)'-ccIncHivTime1_baseline(:,2)')./ccIncHivTime1_baseline(:,2)').*-100)];
% colorP = get(p3,'Color');
% h = fill(x2 , inBetween , colorP);
% h.FaceAlpha = 0.3;
% h.LineStyle = '--';
% set(h,'EdgeColor', colorP);
% % Plot results, method 2
% p4 = plot([2019:2120] , median(((ccIncHivTime2(:,4:28)'-ccIncHivTime2_baseline(:,4:28)')./ccIncHivTime2_baseline(:,4:28)'),1).*-100 , '-');
% hold all;
% x2 = [[2019:2120] , fliplr([2019:2120])];
% inBetween = [prctile(((ccIncHivTime2(:,4:28)'-ccIncHivTime2_baseline(:,4:28)')./ccIncHivTime2_baseline(:,4:28)'),95,1).*-100 , ...
% fliplr(prctile(((ccIncHivTime2(:,4:28)'-ccIncHivTime2_baseline(:,4:28)')./ccIncHivTime2_baseline(:,4:28)'),5,1).*-100)];
% colorP = get(p4,'Color');
% h = fill(x2 , inBetween , colorP);
% h.FaceAlpha = 0.2;
% h.LineStyle = '--';
% set(h,'EdgeColor', colorP);
% axis([2019 2120 0 100])
% grid on;
% xlabel('Year'); ylabel('Percent reduction');
% legend('Orig method, med' , 'range' , ...
% 'New method, med' , 'range');
%
% fname = [pwd , '\HHCoM_Results\' , simVec{j} , '\' , ...
% 'PercentReduct-ASICC_S' , fileVec2{j} , '.xlsx'];
% writematrix([[2019 : 2120]' , ...
% squeeze(median(((ccIncHivTime2_baseline(:,4:28)'-ccIncHivTime2(:,4:28)')./ccIncHivTime2_baseline(:,4:28)'),1)') , ...
% squeeze(prctile(((ccIncHivTime2_baseline(:,4:28)'-ccIncHivTime2(:,4:28)')./ccIncHivTime2_baseline(:,4:28)'),5,1)') , ...
% squeeze(prctile(((ccIncHivTime2_baseline(:,4:28)'-ccIncHivTime2(:,4:28)')./ccIncHivTime2_baseline(:,4:28)'),95,1)')] , ...
% fname);
% end
end
%%
if j == nResults
% subplot(1,3,1);
hold all;
plot ([2019:2120] , ones(1 , length([2019:2120])).*4.0 , 'k:')
hold all;
plot ([2019:2120] , ones(1 , length([2019:2120])).*10.0 , 'k--')
legend(fileTits{:} , ... %'General' , 'HIV-negative' , 'HIV-positive, all' , 'HIV-positive, untreated' , 'HIV-positive, on ART' , ...
'Elimination: <4/100K' , 'Benchmark: <10/100K'); %
%'P1-SCE0 (10sims) - adjusted KZN' , 'P1-SCE1 (10sims) - adjusted KZN' , 'P1-SCE2 (10sims) - adjusted KZN' , ...
end