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showResults.m
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showResults.m
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function[] = showResults(pathModifier)
%% Load parameters and results
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 results
resultsDir = [pwd , '\HHCoM_Results\'];
load([resultsDir , pathModifier])
% Plot settings
reset(0)
set(0 , 'defaultlinelinewidth' , 2)
%% ***************************** DEMOGRAPHY FIGURES **********************************************************************************************
%% Population size over time vs. UN/SSA data
% Ages 0-79
% All HIV-negatives
hivNeg = toInd(allcomb(1 : 2 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , 1 : age , 1 : risk));
% HIV-positives not on ART
hivNoART = toInd(allcomb(3 : 7 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , 1 : age , 1 : risk));
% HIV-positives on ART
art = toInd(allcomb(8 , 6 , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , 1 : age , 1 : risk));
genArray = {hivNeg , hivNoART , art};
totalPop0_79 = sum(popVec(:,genArray{1}),2) + sum(popVec(:,genArray{2}),2) + sum(popVec(:,genArray{3}),2);
% Ages 0-69 (future projections only up to age 69)
% All HIV-negatives
hivNeg = toInd(allcomb(1 : 2 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , 1 : 14 , 1 : risk));
% HIV-positives not on ART
hivNoART = toInd(allcomb(3 : 7 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , 1 : 14 , 1 : risk));
% HIV-positives on ART
art = toInd(allcomb(8 , 6 , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , 1 : 14 , 1 : risk));
genArray = {hivNeg , hivNoART , art};
totalPop0_69 = sum(popVec(:,genArray{1}),2) + sum(popVec(:,genArray{2}),2) + sum(popVec(:,genArray{3}),2);
% Load calibration data from Excel (years, values)
file = [pwd , '/Config/Population_validation_targets.xlsx'];
historicalPop0_69 = zeros(5,2);
futurePop0_69 = zeros(16,2);
historicalPop0_69(:,1) = xlsread(file , 'Demographics' , 'B91:F91'); % years
historicalPop0_69(:,2) = xlsread(file , 'Demographics' , 'B130:F130') .* 1000; % estimates
futurePop0_69(:,1) = xlsread(file , 'Demographics' , 'C144:R144'); % years
futurePop0_69(:,2) = xlsread(file , 'Demographics' , 'C146:R146') .* 1000; % projections
% Calibration error bars
mean = totPopSize_dObs(: , 2);
sdev = (totPopSize_dObs(: , 3).^(1/2));
figure()
plot(tVec , totalPop0_79 , 'b--');
hold all;
errorbar(totPopSize_dObs(: , 1) , mean , sdev , 'ks')
hold all;
plot(tVec , totalPop0_69 , 'b-');
hold all;
plot(historicalPop0_69(:,1) , historicalPop0_69(:,2) , 'ro');
hold all;
plot(futurePop0_69(:,1) , futurePop0_69(:,2) , 'mo');
title('KZN Population Size Ages 0-69')
xlabel('Year'); ylabel('Individuals')
xlim([1950 2120]);
legend('Model prediction, ages 0-79' , 'Calibration SD, ages 0-79' , ...
'Model prediction, ages 0-69' , 'KZN historical estimates (SSA), ages 0-69' , ...
'KZN future projections (UN & SSA), ages 0-69');
hold off;
%% Population size by broad age groups over time vs. SSA data
% Load calibration data from Excel
file = [pwd , '/Config/Population_validation_targets.xlsx'];
years = xlsread(file , 'Demographics' , 'B91:F91'); % years
kzn_popByage_yrs(: , :) = xlsread(file , 'Demographics' , 'M92:Q107').*1000; % males and females by age in 1996-2019
ageGroup = {'10-14' , '15-24' , '25-34' , '35-49' , '50-74'};
popPropYrs = zeros(length(tVec) , 6);
popPropYrs_obs = zeros(5 , 5);
ageVec = {3 , [4:5] , [6:7] , [8:10] , [11:15]};
for y = 1 : length(years)
yearCurr = years(y);
for aInd = 1 : length(ageVec)
a = ageVec{aInd};
popAge = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , a , 1 : risk));
popTot = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , 3 : 15 , 1 : risk));
popPropYrs(:,aInd) = sum(popVec(: , popAge),2) ./ sum(popVec(: , popTot),2);
popPropYrs_obs(y,aInd) = sum(kzn_popByage_yrs(a , y)) / sumall(kzn_popByage_yrs(3:15 , y));
end
end
figure;
set(gca,'ColorOrderIndex',1)
plot(tVec , popPropYrs);
hold on;
set(gca,'ColorOrderIndex',1)
%set(gca, 'ColorOrder', circshift(get(gca, 'ColorOrder'), numel(h)))
plot(years , popPropYrs_obs , 'o');
ylim([0.1 0.3]);
xlim([1995 2020]);
ylabel('Population proportion'); xlabel('Year'); title('KZN age distribution in broad groups');
legend('10-14, Model' , '15-24' , '25-34' , '35-49' , '50-74' , ...
'10-14, Observed' , '15-24' , '25-34' , '35-49' , '50-74' , 'Location' , 'EastOutside');
%legend('Model 2019' , 'SSA KZN observed data 2019');
%legend('Model 1919' , 'SSA KZN observed data 2019' , 'Model 1960' , ...
% 'SSA KZN observed data 2019' ,'Model 1990' , 'SSA KZN observed data 2019' ,'Model 2019' , 'SSA KZN observed data 2019');
%% Population size by 5-year age groups over time vs. SSA data
% Load calibration data from Excel
file = [pwd , '/Config/Population_validation_targets.xlsx'];
years = xlsread(file , 'Demographics' , 'B91:F91'); % years
kzn_popByage_yrs(: , :) = xlsread(file , 'Demographics' , 'M92:Q107').*1000; % males and females by age in 1996-2019
% Calibration error bars
mean = [popAgeDist_dObs(1:16 , 2) , popAgeDist_dObs(17:32 , 2) , popAgeDist_dObs(33:48 , 2)]';
sdev = ([popAgeDist_dObs(1:16 , 3) , popAgeDist_dObs(17:32 , 3) , popAgeDist_dObs(33:48 , 3)]'.^(1/2));
popPropYrs = zeros(5,age);
popPropYrs_obs = zeros(5,age);
for y = 1 : length(years)
yearCurr = years(y);
for a = 1 : age
popAge = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , a , 1 : risk));
popTot = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , 1 : age , 1 : risk));
popPropYrs(y,a) = sum(popVec(((yearCurr - startYear) * stepsPerYear +1) , popAge),2) ./ sum(popVec(((yearCurr - startYear) * stepsPerYear +1) , popTot),2);
popPropYrs_obs(y,a) = sum(kzn_popByage_yrs(a , y)) / sumall(kzn_popByage_yrs(1 : end , y));
end
end
figure;
subplot(1,3,1);
plot(years , popPropYrs(: , 1:7));
set(gca,'ColorOrderIndex',1)
%set(gca, 'ColorOrder', circshift(get(gca, 'ColorOrder'), numel(h)))
hold on;
plot(years , popPropYrs_obs(: , 1:7) , 'o');
hold on;
calibYrs = [unique(popAgeDist_dObs(: , 1)) , unique(popAgeDist_dObs(: , 1)) , unique(popAgeDist_dObs(: , 1)) , ...
unique(popAgeDist_dObs(: , 1)) , unique(popAgeDist_dObs(: , 1)) , unique(popAgeDist_dObs(: , 1)) , unique(popAgeDist_dObs(: , 1)) ,];
errorbar(calibYrs , mean(: , 1:7) , sdev(: , 1:7) , 'ks')
ylim([0.05 0.15]);
ylabel('Population proportion by age'); xlabel('Year');
legend('0-4, Model' , '5-9' , '10-14' , '15-19' , '20-24' , '25-29' , '30-34' , ...
'0-4, Observed KZN (SSA)' , '5-9' , '10-14' , '15-19' , '20-24' , '25-29' , '30-34' , ...
'Calibration SD' , 'Location' , 'EastOutside');
subplot(1,3,2);
plot(years , popPropYrs(: , 8:14));
set(gca,'ColorOrderIndex',1)
hold on;
plot(years , popPropYrs_obs(: , 8:14) , 'o');
hold on;
calibYrs = [unique(popAgeDist_dObs(: , 1)) , unique(popAgeDist_dObs(: , 1)) , unique(popAgeDist_dObs(: , 1)) , ...
unique(popAgeDist_dObs(: , 1)) , unique(popAgeDist_dObs(: , 1)) , unique(popAgeDist_dObs(: , 1)) , unique(popAgeDist_dObs(: , 1)) ,];
errorbar(calibYrs , mean(: , 8:14) , sdev(: , 8:14) , 'ks')
ylim([0.0 0.1]);
ylabel('Population proportion by age'); xlabel('Year');
legend('35-39, Model' , '40-44' , '45-49' , '50-54' , '55-59' , '60-64' , '65-69' , ...
'35-39, Observed KZN (SSA)' , '40-44' , '45-49' , '50-54' , '55-59' , '60-64' , '65-69' , ...
'Calibration SD' , 'Location' , 'EastOutside');
subplot(1,3,3);
plot(years , popPropYrs(: , 15:16));
set(gca,'ColorOrderIndex',1)
hold on;
plot(years , popPropYrs_obs(: , 15:16) , 'o');
hold on;
calibYrs = [unique(popAgeDist_dObs(: , 1)) , unique(popAgeDist_dObs(: , 1))];
errorbar(calibYrs , mean(: , 15:16) , sdev(: , 15:16) , 'ks')
ylim([0.0 0.02]);
ylabel('Population proportion by age'); xlabel('Year'); %title('KZN age distribution in 5-year groups');
legend('70-74, Model' , '75-79' , '70-74, Observed KZN (SSA)' , '75-79' , ...
'Calibration SD' , 'Location' , 'EastOutside');
%% Fertility over time vs. UN data
% Load validation data from Excel (years, values)
file = [pwd , '/Config/Population_validation_targets.xlsx'];
fertilityVal = xlsread(file , 'Demographics' , 'B4:G33');
disp('Remember to update fertility multipliers if calibrating!!!!');
fertDeclineProp = [0.37 ; 0.75];
fertility2 = fertility .* fertDeclineProp(1,1);
fertility3 = fertility2 .* fertDeclineProp(2,1);
fertility4 = fertility3 .* 1.0; %0.60
fertilityVec = [];
for y = 1 : stepsPerYear : length(tVec)
year = tVec(y);
fertilityAnl = fertility;
if year > 1960 && year <= 2000
dt = (year - 1960) * stepsPerYear;
dFert = (fertility2 - fertility) ...
./ ((2000 - 1960) * stepsPerYear);
fertilityAnl = fertility + dFert .* dt;
elseif year > 2000 && year <= 2010
fertilityAnl = fertility2;
elseif year > 2010 && year <=2020
dt = (year - 2010) * stepsPerYear;
dFert = (fertility3 - fertility2) ...
./ ((2020 - 2010) * stepsPerYear);
fertilityAnl = fertility2 + dFert .* dt;
elseif year > 2020 && year <= 2035
dt = (year - 2020) * stepsPerYear;
dFert = (fertility4 - fertility3) ...
./ ((2035 - 2020) * stepsPerYear);
fertilityAnl = fertility3 + dFert .* dt;
elseif year > 2035
fertilityAnl = fertility4;
end
diseaseVec = {[1:2,8] , 3 , 4 , 5 , 6 , 7};
aSum = 0;
for a = 4 : 10
allDinds = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : 3 , 1 : intervens , 2 , a , 1 : risk));
allTot = sumall(popVec(y,allDinds));
for d = 1 : length(diseaseVec)
subDinds = toInd(allcomb(d , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : 3 , 1 : intervens , 2 , a , 1 : risk));
dProp = sumall(popVec(y,subDinds)) / allTot;
aSum = aSum + fertilityAnl(a,d)*dProp*5;
end
end
fertilityVec = [fertilityVec; [year aSum]];
end
figure;
plot(fertilityVec(:,1) , fertilityVec(:,2) , '-');
hold all;
plot(fertilityVal(:,1) , fertilityVal(:,2) , 'o');
hold all;
plot(fertilityVal(:,1) , fertilityVal(:,3) , 'o');
hold all;
plot(fertilityVal(:,1) , fertilityVal(:,4) , 'o');
hold all;
plot(fertilityVal(:,1) , fertilityVal(:,5) , 'o');
hold all;
plot(fertilityVal(:,1) , fertilityVal(:,6) , 'o');
title('Total fertility rate');
legend('Model prediction' , 'SA estimates & projections (UN)' , 'Lower 95' , ...
'Lower 80' , 'Upper 80' , 'Upper 95');
ylim([0 8]);
xlabel('Year'); ylabel('Total fertility rate');
%% ***************************** HIV AND HIV TREATMENT FIGURES ******************************************************************************
%% HIV prevalence by age over time vs. Africa Centre data
hivAge = zeros(length(tVec) , age);
ageGroup = {'15 - 19' , '20 -24' , '25 - 29' ,...
'30 -34' , '35 - 39' , '40 - 44' , '45 - 49' , '50 - 54' , '55 - 59' , ...
'60 - 64' , '65 - 69' , '70 - 74'};
% 2010-2016 AC data
hivPrevF_val = [9.29 9.02 10.45 9.33 10.37 11.00 9.35
31.41 31.68 30.64 33.95 34.56 34.12 33.42
53.27 51.72 50.80 51.33 51.94 53.98 52.41
59.18 61.35 58.66 64.90 62.57 64.71 63.09
53.97 54.08 58.77 65.12 65.28 64.66 66.95
42.69 43.27 45.29 49.16 54.25 56.37 61.28
32.34 34.30 39.18 41.47 48.21 49.57 50.23
];
hivPrevM_val = [1.60 1.85 2.75 3.46 2.87 3.95 4.50
9.56 8.02 9.87 9.65 11.86 7.19 8.02
28.99 21.92 24.88 29.84 35.40 27.65 27.31
46.47 44.51 39.49 47.22 46.35 41.64 42.08
52.03 44.30 49.61 63.33 51.41 52.05 51.35
41.73 41.53 51.55 51.64 59.40 52.69 51.18
36.64 37.12 33.01 40.00 40.54 44.52 52.17
];
hivM(: , 1) = hivPrevM_dObs(: , 2) .* 100; % mean
hivM(: , 2) = (hivPrevM_dObs(: , 3).^(1/2)) .* 100; % calibration SD
hivF(: , 1) = hivPrevF_dObs(: , 2) .* 100; % mean
hivF(: , 2) = (hivPrevF_dObs(: , 3).^(1/2)) .* 100; % calibration SD
prevYears = unique(hivPrevF_dObs(: , 1));
prevYears2 = [2010 : 2016];
gen = {'Male' , 'Female'};
for g = 1 : gender
hivPrevs = hivM;
hivPrevs2 = hivPrevM_val;
if g == 2
hivPrevs = hivF;
hivPrevs2 = hivPrevF_val;
end
figure()
aVec = {16:20,21:25,26:30,31:35,36:40,41:45,46:50}; %,51:55,56:60,61:65,66:70,71:75};
for a = 4 : 10
%a = aVec{aInd};
hivAgeInds = [toInd(allcomb(3 : 7 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , 1 : endpoints , ...
1 : intervens , g , a , 1 : risk)); toInd(allcomb(8 , 6 , 1 : hpvVaxStates , 1 : hpvNonVaxStates , 1 : endpoints , ...
1 : intervens , g , a , 1 : risk))];
ageInds = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , 1 : endpoints , ...
1 : intervens , g , a , 1 : risk));
hivAge(: , a-3) = sum(popVec(: , hivAgeInds) , 2);
hivAgeRel = bsxfun(@rdivide , hivAge(: , a-3) , sum(popVec(: , ageInds) , 2)) * 100;
subplot(3 , 3 , a-3) %subplot(4 , 3 , aInd)
if a <= 11
plot(tVec' , hivAgeRel , prevYears , hivPrevs(((a-3) : 7 : end) , 1) ,'o'); % , ...
%prevYears2 , hivPrevs2((aInd) : 7 : end) , 'bo');
hold on;
errorbar(prevYears , hivPrevs(((a-3) : 7 : end) , 1) , hivPrevs(((a-3) : 7 : end) , 2) , 'ks')
else
plot(tVec' , hivAgeRel);
end
xlabel('Year'); ylabel('Prevalence (%)'); title([gen{g} , 's ages ' , ageGroup{a-3}]) % , ' HIV Prevalence'])
xlim([1980 2019])
end
legend('Model' , 'Observed KZN: AHRI DHHS' , 'Observed KZN: Calibration SD' , 'Observed KZN: AHRI DHHS')
end
%% HIV prevalence by gender over time vs. Africa Centre data
figure;
prevYears = unique(hivPrevF_dObs(: , 1));
hivRaw(:,:,1) = hivPrevM_dObs(: , 4:5);
hivRaw(:,:,2) = hivPrevF_dObs(: , 4:5);
hivData(: , : , 1) = zeros(length(prevYears) , 1);
hivData(: , : , 2) = zeros(length(prevYears) , 1);
for i = 1 : length(prevYears)
for g = 1 : gender
hivData(i,1,g) = (sumall(hivRaw(((i-1)*7+1):(i*7) , 1 , g)) ./ sumall(hivRaw(((i-1)*7+1):(i*7) , 2 , g))) .* 100;
end
end
gen = {'Male' , 'Female'};
for g = 1 : gender
hivInds = [toInd(allcomb(3 : 7 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , 1 : endpoints , ...
1 : intervens , g , 4 : 10 , 1 : risk)); toInd(allcomb(8 , 6 , 1 : hpvVaxStates , 1 : hpvNonVaxStates , 1 : endpoints , ...
1 : intervens , g , 4 : 10 , 1 : risk))];
totInds = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , 1 : endpoints , ...
1 : intervens , g , 4 : 10 , 1 : risk));
hivPop = sum(popVec(: , hivInds) , 2);
hivPopPrev = bsxfun(@rdivide , hivPop , sum(popVec(: , totInds) , 2)) * 100;
subplot(1,2,g)
plot(tVec' , hivPopPrev);
hold all;
plot(prevYears , hivData(:,:,g) , 'ro');
xlabel('Year'); ylabel('Prevalence (%)'); title(gen{g});
xlim([1980 2020]); ylim([0 40]);
legend('Model' , 'AHRI DHHS (Calibration)')
end
%% Relative HIV prevalence for untreated and persons on ART and virally supressed
figure()
artInds = toInd(allcomb(8 , 6 , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , 4 : 10 , 1 : risk));
artPop = sum(popVec(: , artInds) , 2);
hivInds = toInd(allcomb(3 : 7 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates, ...
1 : endpoints , 1 : intervens , 1 : gender , 4 : 10 , 1 : risk));
hivPop = sum(popVec(: , hivInds) , 2);
popInds = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 : gender , 4 : 10 , 1 : risk));
popTot = sum(popVec(: , popInds) , 2);
hiv_art = [100 * hivPop ./ popTot , 100 * artPop ./ popTot];
area(tVec , hiv_art); %art ./ sum(popVec , 2) , tVec , hiv ./ sum(popVec , 2))
xlabel('Year')
ylabel('Prevalence, ages 15-49 (%)')
title('HIV Prevalence by ART treatment status')
legend('Untreated', 'On ART + VS' , 'Location' , 'NorthWest')
xlim([1980 2020]);
%% Proportion of total HIV+ population on ART and VS (denominator: CD4-eligible and ineligible)
figure()
artIndsF = toInd(allcomb(8 , 6 , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 2 , 3 : age , 1 : risk));
hivAllIndsF = toInd(allcomb(3 : 7 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates, ...
1 : endpoints , 1 : intervens , 2 , 3 : age , 1 : risk));
artIndsM = toInd(allcomb(8 , 6 , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 , 3 : age , 1 : risk));
hivAllIndsM = toInd(allcomb(3 : 7 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates, ...
1 : endpoints , 1 : intervens , 1 , 3 : age , 1 : risk));
plot(tVec , 100 * sum(popVec(: , artIndsF) , 2) ./ (sum(popVec(: , hivAllIndsF) , 2) + sum(popVec(: , artIndsF) , 2)) , ...
tVec , 100 * sum(popVec(: , artIndsM) , 2) ./ (sum(popVec(: , hivAllIndsM) , 2) + sum(popVec(: , artIndsM) , 2)) , ...
(artYr + 1) , maxRateF .* 100 , 'o' , ...
(artYr + 1) , maxRateM .* 100 , 'o')
xlim([2000 2030]);
xlabel('Year')
ylabel('Percent of HIV-positive population')
title('Percent of HIV-positives on ART and virally supressed')
legend('Model KZN: Females' , ...
'Model KZN: Males' , ...
'Observed KZN: Females' , ...
'Observed KZN: Males')
%% On ART and VS by age
aVec = {1:5,6:1011:15,16:20,21:25,26:30,31:35,36:40,41:45,46:50,51:55,56:60,61:65,66:70,71:75,76:80}; %{10:15,16:25,26:35,36:50,51:75};
ageGroup = {'0-4','5-9' ,'10-14' , '15-19' , '20-24' , '25-29' ,...
'30-34' , '35-39' , '40-44' , '45-49' , '50-54' , '55-59' , ...
'60-64' , '65-69' , '70-74' , '75-79'};
aMatrix = zeros(1 , age); %length(aVec));
for a = 1 : age %length(aVec)
%a = aVec{aInd};
artInds = toInd(allcomb(8 , 6 , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 2 , a , 1 : risk));
artPop = sum(popVec(end , artInds) , 2); %end-605
hivInds = toInd(allcomb(3 : 8 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates, ...
1 : endpoints , 1 : intervens , 2 , a , 1 : risk));
hivPop = sum(popVec(end , hivInds) , 2);
hiv_art = [100 * artPop ./ hivPop];
aMatrix(1 , a) = hiv_art;
end
figure;
hold all;
plot([1:age] , aMatrix(1,:) , '-')
hold all;
ylabel('Percent females on ART + VS in 2020 by age');
ylim([0 100])
set(gca , 'xtick' , 1 : length(ageGroup) , 'xtickLabel' , ageGroup);
grid on;
%legend('Without ART dropout' , 'With ART dropout');
% legend('Without ART dropout' , 'With ART dropout: 6.19%' , 'With ART
% dropout: 11.8%' , 'With ART dropout: 11.8%, HIV mort on ART');1
%% Proportion HIV-negative males circumcised over time by age group
figure()
circInds = toInd(allcomb(2 , 1 , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 , 4 , 1 : risk));
circPop = sum(popVec(: , circInds) , 2);
hivNegInds = toInd(allcomb(1 : 2 , 1 , 1 : hpvVaxStates , 1 : hpvNonVaxStates, ...
1 : endpoints , 1 : intervens , 1 , 4 , 1 : risk));
hivNegPop = sum(popVec(: , hivNegInds) , 2);
circProp = 100 * circPop ./ hivNegPop;
plot(tVec , circProp);
xlabel('Year')
ylabel('Proportion of HIV-Negative Males ages 15-19 Circumcised (%)')
title('Circumcision Indicator')
xlim([1980 2020]);
%% Proportion HIV-negative males circumcised by broad age groups over time
circPropYr_obs = vmmcYr;
circProp_obs = vmmcRate' .* 100;
circProp_obs = [0.0 0.0 0.0 0.0 0.0 0.0 0.0; circProp_obs];
ageVec = {1 , 4 , 5 , [6:10] , [11:age]}; % Ages: (15-19), (20-24), (25-49), (50+)
circProp = zeros(length(tVec) , length(ageVec));
figure()
for aInd = 1 : length(ageVec)
a = ageVec{aInd};
circInds = toInd(allcomb(2 , 1 , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 1 , a , 1 : risk));
circPop = sum(popVec(: , circInds) , 2);
hivNegInds = toInd(allcomb(1 : 2 , 1 , 1 : hpvVaxStates , 1 : hpvNonVaxStates, ...
1 : endpoints , 1 : intervens , 1 , a , 1 : risk));
hivNegPop = sum(popVec(: , hivNegInds) , 2);
circProp(: , aInd) = 100 * circPop ./ hivNegPop;
end
plot(tVec , circProp);
set(gca,'ColorOrderIndex',1)
hold on;
plot(circPropYr_obs , circProp_obs , 'o');
xlabel('Year')
ylabel('Proportion of HIV-Negative Males Circumcised by Broad Age Groups (%)')
title('Circumcision Indicator')
xlim([1960 2030]);
grid on;
legend('0-4, Model' , '15-19' , '20-24' , '25-49' , '50+' , ...
'0-4, Observed' , '15-19' , '20-24' , '25-49' , '50+' , 'Location' , 'NorthWest');
%% ********************************** HPV FIGURES **********************************************************************************************
%% HPV Prevalence by age in 2002 vs. McDonald 2014 data
ageGroup = {'17 - 19' , '20 -24' , '25 - 29' ,...
'30 -34' , '35 - 39' , '40 - 44' , '45 - 49' , '50 - 54' , '55 - 59' ,...
'60 - 64' , '65 - 69' , '70 - 74' , '75 - 79'};
hpv2002 = zeros(9 , 1);
hpvHIV2002 = hpv2002;
hpvNeg2002 = hpv2002;
aVec = {18:20,21:25,26:30,31:35,36:40,41:45,46:50,51:55,56:60,61:65,66:70,71:75,76:80};
%for aInd = 1 : 13
for a = 4 : 12
%a = aVec{aInd};
hpvInds = unique([toInd(allcomb(1 : disease , 1 : viral , 2 : 5 , [1 : 5 , 7] , ...
1 , 1 : intervens , 2 , a , 1 : risk)); toInd(allcomb(1 : disease , 1 : viral , ...
[1 : 5 , 7] , 2 : 5 , 1 , 1 : intervens , 2 , a , 1 : risk))]);
ageInds = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 2 , a , 1 : risk));
hpv2002(a - 3 , 1) = sum(popVec((2002 - startYear) * stepsPerYear , hpvInds))...
./ sum(popVec((2002 - startYear) * stepsPerYear , ageInds)) * 100;
% HIV+
hpvInds = unique([toInd(allcomb(3 : 8 , 1 : viral , 2 : 5 , [1 : 5 , 7] , ...
1 , 1 : intervens , 2 , a , 1 : risk)); toInd(allcomb(3 : 8 , 1 : viral , ...
[1 : 5 , 7] , 2 : 5 , 1 , 1 : intervens , 2 , a , 1 : risk))]);
ageInds = toInd(allcomb(3 : 8 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 2 , a , 1 : risk));
hpvHIV2002(a - 3 , 1) = sum(popVec((2002 - startYear) * stepsPerYear , hpvInds))...
./ sum(popVec((2002 - startYear) * stepsPerYear , ageInds)) * 100;
% HIV-
hpvInds = unique([toInd(allcomb(1 : 2 , 1 : viral , 2 : 5 , [1 : 5 , 7] , ...
1 , 1 : intervens , 2 , a , 1 : risk)); toInd(allcomb(1 : 2 , 1 : viral , ...
[1 : 5 , 7] , 2 : 5 , 1 , 1 : intervens , 2 , a , 1 : risk))]);
ageInds = toInd(allcomb(1 : 2 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 2 , a , 1 : risk));
hpvNeg2002(a - 3 , 1) = sum(popVec((2002 - startYear) * stepsPerYear , hpvInds))...
./ sum(popVec((2002 - startYear) * stepsPerYear , ageInds)) * 100;
end
% McDonald 2014 (not sure where these bounds came from)
hpvHivObs(: , 1) = hpv_hiv_dObs(: , 2); % mean
hpvHivObs(: , 2) = [0.63 0.54 0.54 0.47 0.42 0.34 0.32 0.35 0.16]'; % lb
hpvHivObs(: , 3) = [0.87 0.67 0.66 0.62 0.51 0.50 0.55 0.72 0.53]'; % ub
hpvNegObs(: , 1) = hpv_hivNeg_dObs(: , 2); % mean
hpvNegObs(: , 2) = [0.53 0.34 0.21 0.17 0.18 0.16 0.11 0.14 0.12]'; % lb
hpvNegObs(: , 3) = [0.67 0.41 0.27 0.23 0.21 0.20 0.15 0.19 0.18]'; % ub
hpvHivObs = hpvHivObs * 100;
hpvNegObs = hpvNegObs * 100;
% Calibration error bars
mean = hpv_hiv_dObs(: , 2) .* 100;
sdev = (hpv_hiv_dObs(: , 3).^(1/2)) .* 100;
meanNeg = hpv_hivNeg_dObs(: , 2) .* 100;
sdevNeg = (hpv_hivNeg_dObs(: , 3).^(1/2)) .* 100;
figure()
% plot(1 : length(hpv2002) , hpv2002 , 'o-')
% hold all;
plot(1 : length(hpvHIV2002) , hpvHIV2002 , 'o-');
hold all;
plot(1 : length(hpvNeg2002) , hpvNeg2002 , 'o-')
hold all;
set(gca , 'xtickLabel' , ageGroup);
% general
% yPosError = abs(hrHpvObs(: , 3) - hrHpvObs(: , 1));
% yNegError = abs(hrHpvObs(: , 2) - hrHpvObs(: , 1));
% errorbar(1 : length(hrHpvObs) , hrHpvObs(: , 1) , yNegError , yPosError , 'rs')
% HIV+
yPosError = abs(hpvHivObs(: , 3) - hpvHivObs(: , 1));
yNegError = abs(hpvHivObs(: , 2) - hpvHivObs(: , 1));
errorbar(1 : length(hpvHivObs) , hpvHivObs(: , 1) , yNegError , yPosError , 'bs')
hold all;
errorbar(1 : length(mean) , mean , sdev , 'ks')
%HIV-
yPosError = abs(hpvNegObs(: , 3) - hpvNegObs(: , 1));
yNegError = abs(hpvNegObs(: , 2) - hpvNegObs(: , 1));
errorbar(1 : length(hpvNegObs) , hpvNegObs(: , 1) , yNegError , yPosError , 'rs')
hold all;
errorbar(1 : length(mean) , meanNeg , sdevNeg , 'ks')
set(gca , 'xtick' , 1 : length(hpvNegObs) , 'xtickLabel' , ageGroup);
legend('HIV-Positive (year 2002)' , 'HIV-Negative (year 2002)' , ...
'Observed HIV-Positive (McDonald, 2014): 2SD' , 'Observed HIV-Positive: Calibration SD' , ...
'Observed HIV-Negative (McDonald, 2014): 2SD' , 'Observed HIV-Negative: Calibration SD')
xlabel('Age Group'); ylabel('hrHPV Prevalence (%)'); ylim([0 100]);
%title('Age Specific hrHPV Prevalence in 2002')
%% HPV prevalence by age and HIV status in 2008 vs. Mbulawa data
yearPrev = 2008;
ageGroup = {'15-24' , '25-34' , '35-44' , '45-64'};
ageVec = {[4:5],[6:7],[8:9],[10:13]};
hpv_hivM2008 = zeros(length(ageVec) , 2);
for aV = 1 : length(ageVec)
a = ageVec{aV};
hpvHivPosInd = unique([toInd(allcomb(3 : 8 , 1 : viral , 2 , [1 : 2 , 7] , ...
1 , 1 : intervens , 1 , a , 1 : risk)); toInd(allcomb(3 : 8 , 1 : viral , ...
[1 : 2 , 7] , 2 , 1 , 1 : intervens , 1 , a , 1 : risk))]);
popHivInd = toInd(allcomb(3 : 8 , 1 : viral , 1 : hpvVaxStates , ...
1 : hpvNonVaxStates , 1 : endpoints , 1 : intervens , 1 , a , 1 : risk));
hpv_hivM2008(aV , 1) = (sum(popVec((yearPrev - startYear) * stepsPerYear +1 , hpvHivPosInd)) ...
./ sum(popVec((yearPrev - startYear) * stepsPerYear +1 , popHivInd))) * 100;
hpvHivNegInd = unique([toInd(allcomb(1 : 2 , 1 : viral , 2 , [1 : 2 , 7] , 1 , ...
1 : intervens , 1 , a , 1 : risk)); toInd(allcomb(1 : 2 , 1 : viral , ...
[1 : 2 , 7] , 2 , 1 , 1 : intervens , 1 , a , 1 : risk))]);
popNegInd = toInd(allcomb(1 : 2 , 1 : viral , 1 : hpvVaxStates , ...
1 : hpvNonVaxStates , 1 : endpoints , 1 : intervens , 1 , a , 1 : risk));
hpv_hivM2008(aV , 2) = (sum(popVec((yearPrev - startYear) * stepsPerYear +1 , hpvHivNegInd)) ...
./ sum(popVec((yearPrev - startYear) * stepsPerYear +1 , popNegInd))) * 100;
end
% Calibration error bars
mean = hpv_hivM2008_dObs(: , 2) .* 100;
sdev = (hpv_hivM2008_dObs(: , 3).^(1/2)) .* 100;
meanNeg = hpv_hivMNeg2008_dObs(: , 2) .* 100;
sdevNeg = (hpv_hivMNeg2008_dObs(: , 3).^(1/2)) .* 100;
figure()
subplot(2,1,1)
plot([1 : length(ageVec)] , hpv_hivM2008(: , 1)' , 'b-');
hold all;
plot([1 : length(ageVec)] , hpv_hivM2008_dObs(: , 2)' .* 100 , 'bo');
hold all;
errorbar(1 : length(mean) , mean , sdev , 'ks')
set(gca , 'xtick' , [1 : length(ageVec)] , 'xtickLabel' , ageGroup);
legend('HIV-Positive Males (year 2008)' , 'Observed HIV-Positive Males: Mbulawa 2008' , ...
'Observed HIV-Positive Males: Calibration SD');
xlabel('Age Group'); ylabel('hrHPV Prevalence (%)'); ylim([0 100]);
subplot(2,1,2)
plot([1 : length(ageVec)] , hpv_hivM2008(: , 2)' , 'r-')
hold all;
plot([1 : length(ageVec)] , hpv_hivMNeg2008_dObs(: , 2)' .* 100 , 'ro');
hold all;
errorbar(1 : length(mean) , meanNeg , sdevNeg , 'ks')
set(gca , 'xtick' , [1 : length(ageVec)] , 'xtickLabel' , ageGroup);
legend('HIV-Negative Males (year 2008)' , 'Observed HIV-Negative Males: Mbulawa 2008' , ...
'Observed HIV-Negative Males: Calibration SD');
xlabel('Age Group'); ylabel('hrHPV Prevalence (%)'); ylim([0 100]);
%% ********************************** CIN FIGURES *********************************************************************************************
%% CIN2/3 prevalence for All HR HPV types combined by HIV status and age in 2002 vs. McDonald 2014 data
cinPos2002 = zeros(10 , 1);
cinNeg2002 = cinPos2002;
ageGroup = {'17-19' , '20-24' , '25-29' ,...
'30-34' , '35-39' , '40-44' , '45-49' , '50-54' , '55-59' , ...
'60-64' , '65-69' , '70-74' , '75-79'};
%aVec = {18:20,21:25,26:30,31:35,36:40,41:45,46:50,51:55,56:60,61:65,66:70,71:75,76:80};
for a = 4 : 13 %note, age group 4 is 17-19 in the data
%a = aVec{aInd};
% HIV-positive (on and not on ART)
cinInds = unique([toInd(allcomb(3 : 8 , 1 : viral , 4 : 5 , [1 : 5 , 7] , ...
1 , 1 : intervens , 2 , a , 1 : risk)); toInd(allcomb(3 : 8 , 1 : viral , ...
[1 : 5 , 7] , 4 : 5 , 1 , 1 : intervens , 2 , a , 1 : risk))]);
ageInds = toInd(allcomb(3 : 8 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 2 , a , 1 : risk));
cinPos2002(a - 3) = (sum(popVec((2002 - startYear) * stepsPerYear , cinInds)))...
./ sum(popVec((2002 - startYear) * stepsPerYear , ageInds)) * 100;
% HIV-negative
cinNegInds = unique([toInd(allcomb(1 : 2 , 1 : viral , 4 : 5 , [1 : 5 , 7] , ...
1 , 1 : intervens , 2 , a , 1 : risk)); toInd(allcomb(1 : 2 , 1 : viral , ...
[1 : 5 , 7] , 4 : 5 , 1 , 1 : intervens , 2 , a , 1 : risk))]);
ageNegInds = toInd(allcomb(1 : 2 , 1 : viral , 1 : hpvVaxStates , 1 : hpvNonVaxStates , ...
1 : endpoints , 1 : intervens , 2 , a , 1 : risk));
cinNeg2002(a - 3) = (sum(popVec((2002 - startYear) * stepsPerYear , cinNegInds)))...
./ (sum(popVec((2002 - startYear) * stepsPerYear , ageNegInds))) * 100;
end
% McDonald 2014, HIV-positive (not sure where these bounds came from)
cinPosAct(: , 1) = cinPos2002_dObs(: , 2); % mean
cinPosAct(: , 2) = [0.03 0.02 0.09 0.10 0.05 0.02 0.02 0.00 0.00 0.00]'; % lb
cinPosAct(: , 3) = [0.22 0.08 0.17 0.21 0.11 0.09 0.14 0.17 0.22 0.22]'; % ub
cinPosAct = cinPosAct .* 100; % convert to %
yPosError = abs(cinPosAct(: , 3) - cinPosAct(: , 1));
yNegError = abs(cinPosAct(: , 2) - cinPosAct(: , 1));
% Calibration error bars
mean = cinPos2002_dObs(: , 2) .* 100;
sdev = (cinPos2002_dObs(: , 3).^(1/2)) .* 100;
figure();
subplot(2 , 1 , 1);
plot(1 : length(cinPos2002) , cinPos2002 ,'o-')
hold on;
errorbar(1 : length(cinPosAct) , cinPosAct(: , 1) , yNegError , yPosError , 'rs')
hold on;
errorbar(1 : length(mean) , mean , sdev , 'ks')
legend('HR HPV CIN 2/3' , 'Observed (McDonald, 2014), 2SD' , 'Calibration SD')
set(gca , 'xtick' , 1 : length(ageGroup) , 'xtickLabel' , ageGroup);
xlabel('Age Group'); ylabel('Prevalence (%)')
title('Age Specific CIN 2/3 Prevalence Among HIV+ in 2002')
ylim([0 25])
% McDonald 2014, HIV-negative (not sure where these bounds came from)
cinNegAct(: , 1) = cinNeg2002_dObs(: , 2); % mean
cinNegAct(: , 2) = [0.00 0.02 0.01 0.02 0.02 0.02 0.02 0.01 0.00 0.00]'; % lb
cinNegAct(: , 3) = [0.03 0.04 0.03 0.05 0.04 0.04 0.04 0.03 0.03 0.03]'; % ub
cinNegAct = cinNegAct .* 100; % convert to %
yPosError = abs(cinNegAct(: , 3) - cinNegAct(: , 1));
yNegError = abs(cinNegAct(: , 2) - cinNegAct(: , 1));
% Calibration error bars
mean = cinNeg2002_dObs(: , 2) .* 100;
sdev = (cinNeg2002_dObs(: , 3).^(1/2)) .* 100;
subplot(2 , 1 , 2)
plot(1 : length(cinNeg2002) , cinNeg2002 , 'o-')
hold on;
errorbar(1 : length(cinNegAct) , cinNegAct(: , 1) , yNegError , yPosError , 'rs')
hold on;
errorbar(1 : length(mean) , mean , sdev , 'ks')
legend('HR HPV CIN 2/3' , 'Observed (McDonald, 2014), 2SD' , 'Calibration SD')
set(gca , 'xtick' , 1 : length(ageGroup) , 'xtickLabel' , ageGroup);
xlabel('Age Group'); ylabel('Prevalence (%)')
title('Age Specific CIN 2/3 Prevalence Among HIV- in 2002')
ylim([0 25])
%% ****************************** CERVICAL CANCER FIGURES ****************************************************************************************
%% Cervical cancer incidence in 2012 by age vs. NCR KZN data and other sources
%ccIncYears = [2017 , 2003 , 1994 , 2012];
ccIncYears = [2012];
ccAgeRel = zeros(age , length(ccIncYears));
ccAgeNegRel = ccAgeRel;
ccAgePosRel = zeros(age , 5 , length(ccIncYears));
ccArtRel = ccAgeRel;
ccNegPosArt = zeros(age , 3 , length(ccIncYears));
fScale = 10^5;
ageGroup = {'0-4' , '5-9' , '10-14' , '15-19' , '20-24' , '25-29' ,...
'30-34' , '35-39' , '40-44' , '45-49' , '50-54' , '55-59' , ...
'60-64' , '65-69' , '70-74' , '75-79'};
annlz = @(x) sum(reshape(x , stepsPerYear , size(x , 1) / stepsPerYear));
ccYrs = ((ccIncYears - startYear) * stepsPerYear : ...
(ccIncYears + 1 - startYear) * stepsPerYear);
%aVec = {1:5,6:10,11:15,16:20,21:25,26:30,31:35,36:40,41:45,46:50,51:55,56:60,61:65,66:70,71:75,76:80};
%for aInd = 1 : 16
for a = 1 : age
%a = aVec{aInd};
for y = 1 : length(ccIncYears)
% Year
yr_start = (ccIncYears(y) - 1 - startYear) .* stepsPerYear;
yr_end = (ccIncYears(y) - startYear) .* stepsPerYear - 1;
% Total population
ageInds = toInd(allcomb(1 : disease , 1 : viral , [1 : 5 , 7] , [1 : 5 , 7] , 1 , ...
1 : intervens , 2 , a , 1 : risk));
ccAgeRel(a , y) = annlz(sum(sum(sum(newCC(yr_start : yr_end , ...
1 : disease , a , :) , 2) , 3) , 4)) ...
./ (annlz(sum(popVec(yr_start : yr_end , ageInds) , 2)) ...
./ stepsPerYear) * fScale;
% HIV Negative
ageNegInds = toInd(allcomb(1 : 2 , 1 : viral , [1 : 5 , 7] , [1 : 5 , 7] , 1 , ...
1 : intervens , 2 , a , 1 : risk));
ccAgeNegRel(a , y) = annlz(sum(sum(sum(newCC(yr_start : yr_end...
, 1 : 2 , a , :) , 2) , 3) , 4)) ...
./ (annlz(sum(popVec(yr_start : yr_end , ageNegInds) , 2)) ...
./ stepsPerYear) * fScale;
% Acute and CD4 > 500
agePosInds = toInd(allcomb(3 : 4 , 1 : viral , [1 : 5 , 7] , [1 : 5 , 7] , 1 , ...
1 : intervens , 2 , a , 1 : risk));
ccAgePosRel(a , 1 , y) = annlz(sum(sum(sum(newCC(yr_start : yr_end ...
, 3 : 4 , a , :), 2) , 3) , 4)) ...
./ (annlz(sum(popVec(yr_start : yr_end , agePosInds) , 2)) ...
./ stepsPerYear) * fScale;
% HIV Positive CD4 500-350 -> CD4 < 200
for d = 5 : 7
agePosInds = toInd(allcomb(d , 1 : viral , [1 : 5 , 7] , [1 : 5 , 7] , 1 , ...
1 : intervens , 2 , a , 1 : risk));
ccAgePosRel(a , d - 3 , y) = annlz(sum(sum(sum(newCC(yr_start : yr_end...
, d , a , :), 2) , 3) , 4)) ...
./ (annlz(sum(popVec(yr_start : yr_end , agePosInds) , 2)) ...
./ stepsPerYear) * fScale;
end
% All HIV+ no ART
ageAllPosInds = toInd(allcomb(3 : 7 , 1 : viral , [1 : 5 , 7] , [1 : 5 , 7] , 1 , ...
1 : intervens , 2 , a , 1 : risk));
ccAgePosRel(a , 5 , y) = annlz(sum(sum(sum(newCC(yr_start : yr_end ...
, 3 : 7 , a , :), 2) , 3) , 4)) ...
./ (annlz(sum(popVec(yr_start : yr_end , ageAllPosInds) , 2)) ...
./ stepsPerYear) * fScale;
% On ART
ageArtInds = toInd(allcomb(8 , 6 , [1 : 5 , 7] , [1 : 5 , 7] , 1 , ...
1 : intervens , 2 , a , 1 : risk));
ccArtRel(a , y) = annlz(sum(sum(sum(newCC(yr_start : yr_end ...
, 8 , a , :) , 2) , 3) , 4)) ...
./ (annlz(sum(popVec(yr_start : yr_end , ageArtInds) , 2)) ...
./ stepsPerYear) * fScale;
% Proportion of cervical cancers by HIV/ART status and age
% Total by age
ageTotal = annlz(sum(popVec(yr_start : yr_end , ageInds), 2 )) ./ stepsPerYear;
% HIV-
ccNegPosArt(a , 1 , y) = (annlz(sum(sum(sum(newCC(yr_start : yr_end , ...
1 , a , :), 2) , 3) , 4)) ...
./ ageTotal) .* fScale;
% HIV+
ccNegPosArt(a , 2 , y) = (annlz(sum(sum(sum(newCC(yr_start : yr_end , ...
3 : 7 , a , :) , 2) , 3) , 4)) ...
./ ageTotal) .* fScale;
% ART
ccNegPosArt(a , 3 , y) = (annlz(sum(sum(sum(newCC(yr_start : yr_end , ...
8 , a , :), 2) , 3) , 4)) ...
./ ageTotal) .* fScale;
end
end
globocan = [0.00
2.646467154
8.848389036
45.1937379
53.40682334
63.4
68.3
70.7
73
77.4
82.7
88.6
95.2];
combined_ub = [0.00
2.65
8.85
45.19
53.41
67.05
80.83
78.97
128.87
105.27
118.70
111.81
95.20];
combined_lb = [0.00
0.00
0.41
9.97
12.61
25.00
45.69
36.31
50.55
57.08
62.69
42.43
52.01];
% Calibration error bars
mean = ccInc2012_dObs(: , 2);
sdev = (ccInc2012_dObs(: , 3).^(1/2));
for y = 1 : length(ccIncYears)
ccIncYear = ccIncYears(y);
figure()
% Plot model outputs
plot(1 : size(ccAgeRel , 1) , ccAgeRel(: , y) , '-ko' , 1 : size(ccAgeNegRel(: , y) , 1) , ...
ccAgeNegRel(: , y) , '-kp' , 1 : size(ccAgePosRel , 1) , ccAgePosRel(: , 5 , y) , '-k+' , ...
1 : size(ccArtRel , 1) , ccArtRel(: , y) , '-k^');
hold on
% Plot observed data
plot(4 : age , combined_ub , 'r--' , 4 : age , combined_lb , 'r--' , 4 : age , ccInc2012_dObs(: , 2) , 'r-');
hold on;
errorbar(4 : age , mean , sdev , 'rs')
xlabel('Age Group'); ylabel('Incidence per 100,000');
set(gca , 'xtick' , 1 : length(ccAgeRel) , 'xtickLabel' , ageGroup);
ylim([0 350]);
title(['Cervical Cancer Incidence in ' num2str(ccIncYear)]);
legend('General' , 'HIV-' , 'HIV+' , 'ART' , 'Combined SA: upper bound' , 'Combined SA: lower bound' , ...
'Globocan SA (2012)' , 'Calibration SD')
% legend('General' , 'HIV-' , ' Acute and CD4 > 500' , 'CD4 500-350' , 'CD4 350-200' , ...
% 'CD4 < 200' , 'ART' , 'Globocan' , 'Upper Bound' , 'Lower Bound' , ...
% 'Location' , 'NorthEastOutside')
% figure()
% bar(1 : length(ccNegPosArt(: , : , y)) , ccNegPosArt(: , : , y), 'stacked')
% xlabel('Age Group'); ylabel('Incidence per 100,000')
% set(gca , 'xtick' , 1 : length(ccAgeRel) , 'xtickLabel' , ageGroup);
% title(['Cervical Cancer Incidence Distribution in ' , num2str(ccIncYear)])
% legend('HIV-' , 'HIV+' , 'ART')
end
%% CC incidence by HIV status over time (age-standardized)
inds = {':' , 1 : 2 , 3 : 7 , 8 , 3 : 8}; % HIV state inds
fac = 10 ^ 5;
plotTits1 = {'General' , 'HIV-negative' , 'HIV-positive no ART' , 'HIV-positive ART' , 'HIV all'};
linColor = {'k' , '[0.8500, 0.3250, 0.0980]' , '[0, 0.4470, 0.7410]' , '[0.9290, 0.6940, 0.1250]' , 'g'};
% worldStandard_WP2015 = [325428 (311262/5.0) 295693 287187 291738 299655 272348 ...
% 247167 240167 226750 201603 171975 150562 113118 82266 64484];
worldStandard_WP2015 = [325428 311262 295693 287187 291738 299655 272348 ...
247167 240167 226750 201603 171975 150562 113118 82266 64484 42237 ...
23477 9261 2155];
figure();
for i = 1 : length(inds)
ccIncRefVec = zeros(length(tVec(1 : stepsPerYear : end-1)),1)';
for aInd = 1 : age + 4
if aInd >= age
a = age;
else
a = aInd;
end
% General
allF = toInd(allcomb(1 : disease , 1 : viral , [1 : 5 , 7] , [1 : 5 , 7] , ...
1 , 1 : intervens , 2 , a , 1 : risk));
% All HIV-negative women
hivNeg = toInd(allcomb(1 : 2 , 1 : viral , [1 : 5 , 7] , [1 : 5 , 7] , ...
1 , 1 : intervens , 2 , a , 1 : risk));
% HIV-positive women not on ART
hivNoARTF = toInd(allcomb(3 : 7 , 1 : viral , [1 : 5 , 7] , [1 : 5 , 7] , ...
1 , 1 : intervens , 2 , a , 1 : risk));
% Women on ART
artF = toInd(allcomb(8 , 6 , [1 : 5 , 7] , [1 : 5 , 7] , ...
1 , 1 : intervens , 2 , a , 1 : risk));
% All HIV-positive women
hivAllF = toInd(allcomb(3 : 8 , 1 : viral , [1 : 5 , 7] , [1 : 5 , 7] , ...
1 , 1 : intervens , 2 , a , 1 : risk));
genArray = {allF , hivNeg , hivNoARTF , artF , hivAllF};
% Calculate incidence
if aInd <= age
ccIncRef = ...
(annlz(sum(sum(newCC(1:end-1 , inds{i} , a , :),2),4)) ./ ...
(annlz(sum(popVec(1:end-1 , genArray{i}) , 2) ./ stepsPerYear))* fac) ...
.* (worldStandard_WP2015(a));
if (i == 4) && (a < 3) && (max(annlz(sum(sum(newCC(1:end-1 , inds{i} , a , :),2),4))) == 0.0)
ccIncRef = zeros(length(tVec(1 : stepsPerYear : end-1)),1)';
end
elseif aInd > age
ccIncRef = ...
(annlz(sum(sum(newCC(1:end-1 , inds{i} , a , :),2),4)) ./ ...
(annlz(sum(popVec(1:end-1 , genArray{i}) , 2) ./ stepsPerYear)) * fac);
ccIncRef = [(ones(1,aInd-a).*ccIncRef(1,1)) , ccIncRef(1,1:end-(aInd-a))];
ccIncRef = ccIncRef .* worldStandard_WP2015(aInd);
end
ccIncRefVec = ccIncRefVec + ccIncRef;
end
ccInc = ccIncRefVec ./ (sum(worldStandard_WP2015(1:age+4)));
plot(tVec(1 : stepsPerYear : end-1) , ccInc ,'DisplayName' , plotTits1{i});
xlabel('Year'); ylabel('Cervical cancer incidence per 100K- age-standardized');
legend('-DynamicLegend');
grid on;
xlim([1925 2020]);
ylim([0 150]);
hold all;
end
%% ************************** HPV/CIN/CC TYPE DISTRIBUTION FIGURES *******************************************************************************
%% HPV type distribution by state over time (coinfections grouped as 9v-type HPV)
% HPV infected
hpvInds_vax = toInd(allcomb(1 : disease , 1 : viral , 2 , [1 : 2 , 7] , ...
1 , 1 : intervens , 2 , 1 : age , 1 : risk));
hpvInds_nonVax = toInd(allcomb(1 : disease , 1 : viral , [1 , 7] , 2 , ...
1 , 1 : intervens , 2 , 1 : age , 1 : risk));