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vaxCEAms.m
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vaxCEAms.m
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% function vaxCEA()
%% load results
paramDir = [pwd , '\Params\'];
load([paramDir, 'general'])
nSims = size(dir([pwd , '\HHCoM_Results\Vaccine\*.mat']) , 1);
curr = load([pwd , '\HHCoM_Results\toNow.mat']); % Population up to 2018
% helper functions
annlz = @(x) sum(reshape(x , stepsPerYear , size(x , 1) / stepsPerYear)); % sums 1 year worth of values
annAvg = @(x) sum(reshape(x , stepsPerYear , size(x , 1) / stepsPerYear)) ./ stepsPerYear; % finds average value of a quantity within a given year
% time
c = fix(clock); % get time
currYear = c(1); % get the current year from time
vaxResult = cell(nSims , 1);
parfor n = 1 : nSims
% load results from vaccine run into cell array
vaxResult{n} = load([pwd , '\HHCoM_Results\Vaccine\vaxSimResult' ,...
num2str(n), '.mat']);
% concatenate vectors/matrices of population up to 2018 to population
% matrices for years past 2018
vaxResult{n}.popVec = [curr.popVec(1 : end , :) ; vaxResult{n}.popVec];
vaxResult{n}.newHpv= [curr.newHpv(1 : end , : , : , : , :) ; vaxResult{n}.newHpv];
vaxResult{n}.newImmHpv= [curr.newImmHpv(1 : end , : , : , : , :) ; vaxResult{n}.newImmHpv];
% vaxResult{n}.ccDeath = [curr.ccDeath(1 : end - 1 , : , : , :) ; vaxResult{n}.ccDeath];
vaxResult{n}.newCC = [curr.newCC(1 : end , : , : , :) ; vaxResult{n}.newCC];
vaxResult{n}.newHiv = [curr.newHiv(1 : end , : , : , :) ; vaxResult{n}.newHiv];
vaxResult{n}.tVec = [curr.tVec(1 : end) , vaxResult{n}.tVec];
% vaxResult{n}.ccTreated = [curr.ccTreated(1 : end - 1) , vaxResult{n}.ccTreated];
end
% Find no vaccine scenario
noVaxInd = -1;
for n = 1 : nSims
if vaxResult{n}.vaxEff == 0
noVaxInd = n;
end
end
noV = vaxResult{noVaxInd};
tVec = noV.tVec;
tVecYr = tVec(1 : stepsPerYear : end);
%%
reset(0)
set(0 , 'defaultlinelinewidth' , 2)
%% Calculate life years saved
yrIntStart = 2018;
for n = 1 : length(vaxResult)
vaxResult{n}.ly = zeros((length(tVec) - length(curr.tVec)) , 1);
vaxResult{n}.daly = zeros((length(tVec) - length(curr.tVec)) , 1);
end
noV.ly = zeros((length(tVec) - length(curr.tVec)) , 1);
noV.daly = zeros((length(tVec) - length(curr.tVec)) , 1);
%% CC Costs
ccCost = [2617 , 8533 , 8570]; % local, regional, distant
ccDalyWeight = 1 - [0.288 , 0.288 , 0.288]; % corresponds to local, regional, distant CC
for i = 1 : (length(tVec) - length(curr.tVec))
% If y = current year, count benefits and CC treatment costs for women aged
% >= y - B, where B = last year eligible for inclusion
% Since 5 year age groups are being used, at each year y, count benefits
% for women in age group (round((y-B)/5)) and above.
a = min(max(round((tVec(i + length(curr.tVec) - 1) - yrIntStart) / 5) , 1) , age);
ageCounted = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvTypes , 1 : hpvStates , ...
1 : periods , 2 , a : age , 1 : risk));
for n = 1 : length(vaxResult)
% CC Indices
localInds = toInd(allcomb(1 : disease , 1 : viral , 2 : hpvTypes , 5 , 1 : periods , ...
1 : gender , a : age , 1 : risk));
regionalInds = toInd(allcomb(1 : disease , 1 : viral , 2 : hpvTypes , 6 , 1 : periods , ...
1 : gender , a : age , 1 : risk));
distantInds = toInd(allcomb(1 : disease , 1 : viral , 2 : hpvTypes , 7 , 1 : periods , ...
1 : gender , a : age , 1 : risk));
% Count life years
vaxResult{n}.ly(i) = sum(vaxResult{n}.popVec(i + length(curr.tVec) - 1 , ageCounted) , 2);
% Count DALYs
% Adjust life years for CC by region according to disability
% weights
% calculate CC DALYs for each time step
cc_dalys = ...
sum(vaxResult{n}.popVec(i + length(curr.tVec) - 1 , localInds) , 2) .* ccDalyWeight(1) ...
+ sum(vaxResult{n}.popVec(i + length(curr.tVec) - 1 , regionalInds) , 2) .* ccDalyWeight(2)...
+ sum(vaxResult{n}.popVec(i + length(curr.tVec) - 1 , distantInds) , 2) .* ccDalyWeight(3);
% DALYs obtained by subtracting full life years corresponding to CC
% then adding DALYs corresponding to CC (done since LY already
% calculated above)
vaxResult{n}.daly(i) = vaxResult{n}.ly(i) ...
- (sum(vaxResult{n}.popVec(i + length(curr.tVec) - 1 , localInds) , 2) ... % subtract full LY corresponding to CC
+ sum(vaxResult{n}.popVec(i + length(curr.tVec) - 1 , regionalInds) , 2) ...
+ sum(vaxResult{n}.popVec(i + length(curr.tVec) - 1 , distantInds) , 2)) ...
+ cc_dalys; % Add CC DALYs
% Cervical cancer costs
vaxResult{n}.ccCosts(i) = ...
sum(sum(sum(vaxResult{n}.ccTreated(i , : , : , a : age , 1) , 2) , 3) , 4) .* ccCost(1) + ...
sum(sum(sum(vaxResult{n}.ccTreated(i , : , : , a : age , 2) , 2) , 3) , 4) .* ccCost(2) + ...
sum(sum(sum(vaxResult{n}.ccTreated(i , : , : , a : age , 3) , 2) , 3) , 4) .* ccCost(3);
end
% no vaccine scenario
% Count life years
noV.ly(i) = sum(noV.popVec(i + length(curr.tVec) - 1 , ageCounted) , 2);
cc_dalys = ...
sum(noV.popVec(i + length(curr.tVec) - 1 , localInds) , 2) .* ccDalyWeight(1) ...
+ sum(noV.popVec(i + length(curr.tVec) - 1 , regionalInds) , 2) .* ccDalyWeight(2)...
+ sum(noV.popVec(i + length(curr.tVec) - 1 , distantInds) , 2) .* ccDalyWeight(3);
% DALYs obtained by subtracting full life years corresponding to CC
% then adding DALYs corresponding to CC (done since LY already
% calculated above)
noV.daly(i) = noV.ly(i) ...
- (sum(noV.popVec(i + length(curr.tVec) - 1 , localInds) , 2) ... % subtract full LY corresponding to CC
+ sum(noV.popVec(i + length(curr.tVec) - 1 , regionalInds) , 2) ...
+ sum(noV.popVec(i + length(curr.tVec) - 1 , distantInds) , 2)) ...
+ cc_dalys; % Add CC DALYs
% Cervical cancer costs
noV.ccCosts(i) = sum(sum(sum(noV.ccTreated(i, : , : , a : age , 1) , 2) , 3) , 4) .* ccCost(1) + ...
sum(sum(sum(noV.ccTreated(i , : , : , a : age , 2) , 2) , 3) , 4) .* ccCost(2) + ...
sum(sum(sum(noV.ccTreated(i , : , : , a : age , 3) , 2) , 3) , 4) .* ccCost(3);
end
%%
for n = 1 : length(vaxResult)
vaxResult{n}.lys = vaxResult{n}.ly - noV.ly;
end
figure()
for n = 1 : length(vaxResult)
plot(tVec(length(curr.tVec) + 1 : end) , vaxResult{n}.lys , ...
'DisplayName' , ['Vaccine Efficacy: ' , ...
num2str(round(vaxResult{n}.vaxEff * 100)) ,'%, ' , ...
'Vaccine Coverage: ' , num2str(round(vaxResult{n}.vaxRate * 100)) ,'%'])
hold on
legend('-DynamicLegend' , 'Location' , 'NorthwestOutside')
end
figure()
for n = 1 : length(vaxResult)
plot(tVec(length(curr.tVec) + 1 : end) , sum(vaxResult{n}.popVec(length(curr.tVec) + 1 : end , :) , 2)...
-sum(noV.popVec(length(curr.tVec) + 1 : end , :),2), ...
'DisplayName' , ['Vaccine Efficacy: ' , ...
num2str(round(vaxResult{n}.vaxEff * 100)) ,'%, ' , ...
'Vaccine Coverage: ' , num2str(round(vaxResult{n}.vaxRate * 100)) ,'%'])
hold on
legend('-DynamicLegend' , 'Location' , 'NorthwestOutside')
end
for n = 1 : length(vaxResult)
vaxResult{n}.dalyPlus = vaxResult{n}.daly - noV.daly;
end
%% Calculate annual costs
% HIV Costs (Not used in ICER calculation. Included for completeness)
hospCost = [117 , 56 , 38 , 38]; % <200 , 200-350, >350 , on ART
artCost = 260;
% CC Costs (Incurred once per person at time of cervical cancer detection)
ccCost = [2617 , 8533 ,8570]; % local, regional, distant
% HIV Indices (Not used)
% above350Inds = toInd(allcomb(4 , 1 : viral , 1 : hpvTypes , 1 : hpvStates , 1 : periods , ...
% 1 : gender , 1 : age , 1 : risk));
% cd200_350Inds = toInd(allcomb(5 , 1 : viral , 1 : hpvTypes , 1 : hpvStates , 1 : periods , ...
% 1 : gender , 1 : age , 1 : risk));
% under200Inds = toInd(allcomb(6 , 1 : viral , 1 : hpvTypes , 1 : hpvStates , 1 : periods , ...
% 1 : gender , 1 : age , 1 : risk));
% artInds = toInd(allcomb(10 , 6 , 1 : hpvTypes , 1 : hpvStates , 1 : periods , ...
% 1 : gender , 1 : age , 1 : risk));
% Vaccination
discountRate = 0.03; % discount rate of 3% per annum
cost2v = 27; % cost of 2 doses of bivalent vaccine
import java.util.LinkedList
sims2v = LinkedList();
full9v = LinkedList();
full2v = LinkedList();
maxRate9vSim = -1;
maxRate2vSim = -1;
maxRate9v = -1;
maxRate2v = -1;
for n = 1 : length(vaxResult)
if vaxResult{n}.vaxEff <= 0.7 && vaxResult{n}.vaxEff > 0 % Vaccine efficacy <= 70% corresponds to 2v
sims2v.add(n); % save index of 2v scenarios
vaxResult{n}.vax2vCost = annlz(vaxResult{n}.vaxd) * cost2v;
vaxResult{n}.vax2vCostNPV = pvvar(vaxResult{n}.vax2vCost , discountRate); % NPV of vaccination cost
end
if vaxResult{n}.vaxEff == 0.90
full9v.add(n);
if vaxResult{n}.vaxRate > maxRate9v
maxRate9vSim = n;
maxRate9v = vaxResult{n}.vaxRate;
end
elseif vaxResult{n}.vaxEff == 0.70
full2v.add(n);
if vaxResult{n}.vaxRate > maxRate2v
maxRate2vSim = n;
maxRate2v = vaxResult{n}.vaxRate;
end
end
end
%% Find price threshold for 9v (CC costs only)
% 3 thresholds: 0.5x GDP , 1x GDP , 500 USD per LYS (BMGF)
ceThreshold = 6161; % USD per LYS
ceThresholds = [0.5 * ceThreshold , ceThreshold , 500];
% Using Life years
% High coverage scenario (9v vs 2v)
for i = 1 : length(ceThresholds)
priceGuess = 100; % Enter a price guess for 9v to seed the search process
% ce9v is an anonymous function that finds the vaccine price that
% places the 9v vaccine right at the cost-effectiveness threshold
% specified by ceThresholds(i)
ce9v = @(x) abs(pvvar(annlz(vaxResult{maxRate9vSim}.vaxd) * x - annlz(vaxResult{maxRate2vSim}.vaxd) .* cost2v ... % difference in vaccine cost for 9v vs 2v
+ annlz(vaxResult{maxRate9vSim}.ccCosts') - annlz(vaxResult{maxRate2vSim}.ccCosts') , discountRate) ... % difference in CC cost for 9v vs 2v scenario
/ pvvar(annAvg(vaxResult{maxRate9vSim}.lys) - annAvg(vaxResult{maxRate2vSim}.lys) , discountRate) - ceThresholds(i)); % difference in LYS for 9v vs 2v scenario
priceThreshold_9v = fminsearch(ce9v , priceGuess);
fprintf(['\n 9v vs 2v: Considering only CC costs, with a cost-effectiveness \n' , ...
' threshold of ' , num2str(ceThresholds(i)) , ' USD per LYS, ' ,...
' the unit cost of 9v vaccine must be less than or equal to \n ' , ...
num2str(round(priceThreshold_9v , 2)),' USD. \n'])
end
disp(' ')
% Using DALYs
% High coverage scenario (9v vs 2v)
for i = 1 : length(ceThresholds)
priceGuess = 100; % Enter a price guess for 9v to seed the search process
% ce9v is an anonymous function that finds the vaccine price that
% places the 9v vaccine right at the cost-effectiveness threshold
% specified by ceThresholds(i)
ce9v = @(x) abs(pvvar(annlz(vaxResult{maxRate9vSim}.vaxd) * x - annlz(vaxResult{maxRate2vSim}.vaxd) .* cost2v ... % difference in vaccine cost for 9v vs 2v
+ annlz(vaxResult{maxRate9vSim}.ccCosts') - annlz(vaxResult{maxRate2vSim}.ccCosts') , discountRate) ... % difference in CC cost for 9v vs 2v scenario
/ pvvar(annAvg(vaxResult{maxRate9vSim}.daly) - annAvg(vaxResult{maxRate2vSim}.daly) , discountRate) - ceThresholds(i)); % difference in DALYs for 9v vs 2v scenario
priceThreshold_9v = fminsearch(ce9v , priceGuess);
fprintf(['\n 9v vs 2v: Considering only CC costs, with a cost-effectiveness \n' , ...
' threshold of ' , num2str(ceThresholds(i)) , ' USD per DALY, ' ,...
' the unit cost of 9v vaccine must be less than or equal to \n ' , ...
num2str(round(priceThreshold_9v , 2)),' USD.\n'])
end
%% YLS
% figure()
% plot(tVec(1 : stepsPerYear : end) , annlz(c90_9vFull.vaxd))
% title('Vaccinated with 9v'); xlabel('Year'); ylabel('Number vaccinated')
%% CC incidence reduction
inds = {':' , [2 : 6 , 10] , [2 : 6] , 1 , 10};
files = {'CEA CC_General_Hpv_VaxCover' , 'CEA CC_HivAll_Hpv_VaxCover' , ...
'CEA CC_HivNoART_Hpv_VaxCover' , 'CEA CC_HivNeg_Hpv_VaxCover' ,...
'CEA CC_ART_HPV_VaxCover'};
plotTits = {'General' , 'HIV-Positive' , 'HIV-Positive (No ART)' , ....
'HIV-Negative' , 'HIV-Positive on ART'};
fac = 10 ^ 5;
% noV = zeros(1 , (length(tVec) - stepsPerYear + 1) / stepsPerYear);
% % c70_2vPartial_Inc = noV_Hpv;
% % c90_9vFullInc = noV_HpvAge;
% % c90_2vFullInc = noV_HpvAge;
% % v90_2vFullInc = noV_HpvAge;
figure()
for i = 1 : length(inds)
% general
allF = [toInd(allcomb(1 : disease , 1 : viral , 1 : hpvTypes , 1 : 4 , ...
1 : periods , 2 , 4 : age , 1 : risk)); ...
toInd(allcomb(1 : disease , 1 : viral , 1 : hpvTypes , 9 : 10 , ...
1 : periods , 2 , 4 : age , 1 : risk))];
% All HIV-positive women
allHivF = [toInd(allcomb(2 : 6 , 1 : viral , 1 : hpvTypes , 1 : 4 , ...
1 : periods , 2 , 4 : age , 1 : risk)); ...
toInd(allcomb(2 : 6 , 1 : viral , 1 : hpvTypes , 9 : 10 , ...
1 : periods , 2 , 4 : age , 1 : risk));...
toInd(allcomb(10 , 6 , 1 : hpvTypes , 1 : 4 , ...
1 : periods , 2 , 4 : age , 1 : risk)); ...
toInd(allcomb(10 , 6 , 1 : hpvTypes , 9 : 10 , ...
1 : periods , 2 , 4 : age , 1 : risk))];
% HIV-positive women not on ART
hivNoARTF = [toInd(allcomb(2 : 6 , 1 : viral , 1 : hpvTypes , 1 : 4 , ...
1 : periods , 2 , 4 : age , 1 : risk)); ...
toInd(allcomb(2 : 6 , 1 : viral , 1 : hpvTypes , 9 : 10 , ...
1 : periods , 2 , 4 : age , 1 : risk))];
% HIV-positive women CD4<200
under200Inds = [toInd(allcomb(6 , 1 : viral , 1 : hpvTypes , 1 : 4 , ...
1 : periods , 2 , 4 : age , 1 : risk)); ...
toInd(allcomb(6 , 1 : viral , 1 : hpvTypes , 9 : 10 , ...
1 : periods , 2 , 4 : age , 1 : risk))];
% HIV-positive women CD4 200-350
cd200_350Inds = [toInd(allcomb(5 , 1 : viral , 1 : hpvTypes , 1 : 4 , ...
1 : periods , 2 , 4 : age , 1 : risk)); ...
toInd(allcomb(5 , 1 : viral , 1 : hpvTypes , 9 : 10 , ...
1 : periods , 2 , 4 : age , 1 : risk))];
% HIV-positive women CD4 350-500
cd350_500Inds = [toInd(allcomb(4 , 1 : viral , 1 : hpvTypes , 1 : 4 , ...
1 : periods , 2 , 4 : age , 1 : risk)); ...
toInd(allcomb(4 , 1 : viral , 1 : hpvTypes , 9 : 10 , ...
1 : periods , 2 , 4 : age , 1 : risk))];
% HIV-positive women CD4 >500 and acute
above500Inds = [toInd(allcomb(2:3 , 1 : viral , 1 : hpvTypes , 1 : 4 , ...
1 : periods , 2 , 4 : age , 1 : risk)); ...
toInd(allcomb(2 : 3 , 1 : viral , 1 : hpvTypes , 9 : 10 , ...
1 : periods , 2 , 4 : age , 1 : risk))];
% All HIV-negative women
hivNeg = [toInd(allcomb(1 , 1 : viral , 1 : hpvTypes , 1 : 4 , 1 : periods , ...
2 , 4 : age , 1 : risk)); ...
toInd(allcomb(1 , 1 : viral , 1 : hpvTypes , 9 : 10 , 1 : periods , ...
2 , 4 : age , 1 : risk))];
% Women on ART
artF = [toInd(allcomb(10 , 6 , 1 : hpvTypes , 1 : 4 , ...
1 : periods , 2 , 4 : age , 1 : risk)); ...
toInd(allcomb(10 , 6 , 1 : hpvTypes , 9 : 10 , ...
1 : periods , 2 , 4 : age , 1 : risk))];
genArray = {allF , allHivF , hivNoARTF ,under200Inds, cd200_350Inds, cd350_500Inds, above500Inds, hivNeg , artF};
noV.ccInc = ...
annlz(sum(sum(sum(noV.newCC(: , inds{i} , : , 4 : age),2),3),4)) ./ ...
(annlz(sum(noV.popVec(: , genArray{i}) , 2) ./ stepsPerYear))* fac;
figure()
plot(tVec(1 : stepsPerYear : end) , noV.ccInc ,'DisplayName' , ...
['Efficacy: ' , num2str(round(vaxResult{n}.vaxEff * 100)) '% ,', ...
'Coverage: ' , num2str(round(vaxResult{n}.vaxRate * 100)) , '%'])
legend('-DynamicLegend')
hold on
for n = 1 : length(vaxResult)
vaxResult{n}.ccInc = ...
annlz(sum(sum(sum(vaxResult{n}.newCC(: , inds{i} , : , 4 : age),2),3),4)) ./ ...
(annlz(sum(vaxResult{n}.popVec(: , genArray{i}) , 2) ./ stepsPerYear)) * fac;
plot(tVec(1 : stepsPerYear : end) , vaxResult{n}.ccInc , 'DisplayName' , ...
['Efficacy: ' , num2str(round(vaxResult{n}.vaxEff * 100)) '% ,', ...
'Coverage: ' , num2str(round(vaxResult{n}.vaxRate * 100)) , '%'])
fname = ['Efficacy' , num2str(round(vaxResult{n}.vaxEff * 100)) , ...
'Coverage' , num2str(round(vaxResult{n}.vaxRate * 100)) , '.xlsx'];
hold on
if exist(fname , 'file') == 2
M = xlsread(fname);
M = catpad(2 , [tVec(1 : stepsPerYear : end)' , vaxResult{n}.ccInc'] , M);
xlswrite(fname , M , 'CC Incidence')
% dlmwrite(fname , [tVec(1 : stepsPerYear : end)' , vaxResult{n}.ccInc'] , ...
% '-append' , 'delimiter' , ',' , 'coffset' , 1)
else
xlswrite(fname , [tVec(1 : stepsPerYear : end)' , ...
vaxResult{n}.ccInc'] , 'CC Incidence')
end
end
title([plotTits{i} , ' Cervical Cancer Incidence'])
xlabel('Year'); ylabel('Incidence per 100,000')
hold off
% Reduction
figure()
for n = 1 : length(vaxResult)
vaxResult{n}.ccRed = (vaxResult{n}.ccInc - noV.ccInc) ./ noV.ccInc * 100;
plot(tVec(1 : stepsPerYear : end) , vaxResult{n}.ccRed , 'DisplayName' , ...
['Efficacy ' , num2str(round(vaxResult{n}.vaxEff * 100)) '% ,', ...
'Coverage ' , num2str(round(vaxResult{n}.vaxRate * 100)) , '%'])
legend('-DynamicLegend')
hold on
fname = ['Efficacy' , num2str(round(vaxResult{n}.vaxEff * 100)) , ...
'Coverage' , num2str(round(vaxResult{n}.vaxRate * 100)) , '.xlsx'];
if exist(fname , 'file') == 2
M = xlsread(fname);
M = catpad(2 , [tVec(1 : stepsPerYear : end)' , vaxResult{n}.ccRed'] , M);
xlswrite(fname , M , 'CC Reduction')
% dlmwrite(fname , [tVec(1 : stepsPerYear : end)' , vaxResult{n}.ccRed'] , ...
% '-append' , 'delimiter' , ',' , 'coffset' , 1)
else
xlswrite(fname , [tVec(1 : stepsPerYear : end)' , vaxResult{n}.ccRed']...
, 'CC Reduction')
end
title([plotTits{i} , ' Cervical Cancer Incidence Reduction'])
xlabel('Year'); ylabel('Reduction (%)')
end
hold off
end
% %%
%% CC Mortality
%% CC mortality reduction
inds = {':' , [2 : 6 , 10] , [2 : 6] , 1 , 10};
files = {'CEA CC_General_Hpv_VaxCover' , 'CEA CC_HivAll_Hpv_VaxCover' , ...
'CEA CC_HivNoART_Hpv_VaxCover' , 'CEA CC_HivNeg_Hpv_VaxCover' ,...
'CEA CC_ART_HPV_VaxCover'};
plotTits = {'General' , 'HIV-Positive' , 'HIV-Positive (No ART)' , ....
'HIV-Negative' , 'HIV-Positive on ART'};
fac = 10 ^ 5;
% noV = zeros(1 , (length(tVec) - stepsPerYear + 1) / stepsPerYear);
% % c70_2vPartial_Inc = noV_Hpv;
% % c90_9vFullInc = noV_HpvAge;
% % c90_2vFullInc = noV_HpvAge;
% % v90_2vFullInc = noV_HpvAge;
figure()
for i = 1 : length(inds)
% general
allF = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvTypes , 1 : hpvStates , ...
1 : periods , 2 , 4 : age , 1 : risk));
% All HIV-positive women
allHivF = [toInd(allcomb(2 : 6 , 1 : viral , 1 : hpvTypes , 1 : hpvStates , ...
1 : periods , 2 , 4 : age , 1 : risk)); ...
toInd(allcomb(10 , 6 , 1 : hpvTypes , 1 : hpvStates , ...
1 : periods , 2 , 4 : age , 1 : risk))];
% HIV-positive women not on ART
hivNoARTF = toInd(allcomb(2 : 6 , 1 : viral , 1 : hpvTypes , 1 : hpvStates , ...
1 : periods , 2 , 4 : age , 1 : risk));
% All HIV-negative women
hivNeg = toInd(allcomb(1 , 1 : viral , 1 : hpvTypes , 1 : hpvStates , 1 : periods , ...
2 , 4 : age , 1 : risk));
% Women on ART
artF = toInd(allcomb(10 , 6 , 1 : hpvTypes , 1 : hpvStates , ...
1 : periods , 2 , 4 : age , 1 : risk));
genArray = {allF , allHivF , hivNoARTF , hivNeg , artF};
noV.ccMort = ...
annlz(sum(sum(sum(noV.ccDeath(: , inds{i} , : , 4 : age),2),3),4)) ./ ...
(annlz(sum(noV.popVec(length(curr.tVec) + 1 : end , genArray{i}) , 2) ./ stepsPerYear))* fac;
figure()
plot(tVec(length(curr.tVec) + 1 : stepsPerYear : end) , noV.ccMort ,'DisplayName' , ...
['Efficacy: ' , num2str(round(vaxResult{n}.vaxEff * 100)) '% ,', ...
'Coverage: ' , num2str(round(vaxResult{n}.vaxRate * 100)) , '%'])
legend('-DynamicLegend')
hold on
for n = 1 : length(vaxResult)
vaxResult{n}.ccMort = ...
annlz(sum(sum(sum(vaxResult{n}.ccDeath(: , inds{i} , : , 4 : age),2),3),4)) ./ ...
(annlz(sum(vaxResult{n}.popVec(length(curr.tVec) + 1 : end , genArray{i}) , 2) ./ stepsPerYear)) * fac;
plot(tVec(length(curr.tVec) + 1 : stepsPerYear : end) , vaxResult{n}.ccMort , 'DisplayName' , ...
['Efficacy: ' , num2str(round(vaxResult{n}.vaxEff * 100)) '% ,', ...
'Coverage: ' , num2str(round(vaxResult{n}.vaxRate * 100)) , '%'])
hold on
fname = ['Efficacy' , num2str(round(vaxResult{n}.vaxEff * 100)) , ...
'Coverage' , num2str(round(vaxResult{n}.vaxRate * 100)) , '.xlsx'];
if exist(fname , 'file') == 2
M = xlsread(fname);
M = catpad(2 , [tVec(length(curr.tVec) + 1 : stepsPerYear : end)' , vaxResult{n}.ccMort'] , M);
xlswrite(fname , M , 'CC Mortality')
% dlmwrite(fname , [tVec(length(curr.tVec) + 1 : stepsPerYear : end)' , vaxResult{n}.ccMort'] , ...
% '-append' , 'delimiter' , ',' , 'coffset' , 1)
else
xlswrite(fname , [tVec(length(curr.tVec) + 1 : stepsPerYear : end)' , vaxResult{n}.ccMort'] , 'CC Mortality')
end
end
title([plotTits{i} , ' Cervical Cancer Mortality'])
xlabel('Year'); ylabel('Mortality per 100,000')
hold off
% Reduction
figure()
for n = 1 : length(vaxResult)
vaxResult{n}.ccMortRed = (vaxResult{n}.ccMort - noV.ccMort) ./ noV.ccMort * 100;
plot(tVec(length(curr.tVec) + 1 : stepsPerYear : end) , vaxResult{n}.ccMortRed , 'DisplayName' , ...
['Efficacy: ' , num2str(round(vaxResult{n}.vaxEff * 100)) '% ,', ...
'Coverage: ' , num2str(round(vaxResult{n}.vaxRate * 100)) , '%'])
legend('-DynamicLegend')
hold on
title([plotTits{i} , ' Cervical Cancer Mortality Reduction'])
xlabel('Year'); ylabel('Reduction (%)')
fname = ['Efficacy' , num2str(round(vaxResult{n}.vaxEff * 100)) , ...
'Coverage' , num2str(round(vaxResult{n}.vaxRate * 100)) , '.xlsx'];
if exist(fname , 'file') == 2
M = xlsread(fname);
M = catpad(2 , [tVec(length(curr.tVec) + 1 : stepsPerYear : end)' , vaxResult{n}.ccMortRed'] , M);
xlswrite(fname , M , 'CC Mortality Reduction')
% dlmwrite(fname , [tVec(length(curr.tVec) + 1 : stepsPerYear : end)' , vaxResult{n}.ccMortRed'] , ...
% '-append' , 'delimiter' , ',' ,'coffset' , 1)
else
xlswrite(fname , [tVec(length(curr.tVec) + 1 : stepsPerYear : end)' , vaxResult{n}.ccMortRed'] , ...
'CC Mortality Reduction')
end
end
hold off
end
%% Population Size
figure()
for n = 1 : length(vaxResult)
plot(tVec , sum(vaxResult{n}.popVec , 2) , 'DisplayName' , ...
['Efficacy: ' , num2str(round(vaxResult{n}.vaxEff * 100)) '% ,', ...
'Coverage: ' , num2str(round(vaxResult{n}.vaxRate * 100)) , '%'])
legend('-DynamicLegend')
hold on
end
title('Population Size')
xlabel('Year'); ylabel('Individuals')
hold off
%%
figure()
for g = 1 : 2
artInds = toInd(allcomb(10 , 6 , 1 : hpvTypes , 1 : hpvStates , ...
1 : periods , g , 4 : 10 , 1 : risk));
artPop = sum(noV.popVec(: , artInds) , 2);
hivInds = toInd(allcomb(2 : 6 , 1 : viral , 1 : hpvTypes , 1 : hpvStates, ...
1 : periods , g , 4 : 10 , 1 : risk));
hivPop = sum(noV.popVec(: , hivInds) , 2);
plot(tVec , 100 * artPop ./ (hivPop + artPop))
hold on
end
xlabel('Year')
ylabel('Proportion of HIV Population')
title('Proportion on ART')
legend('Model (Male)' , 'Model (Female)')
%
% %%
% figure()
% for g = 1 : 2
% artInds = toInd(allcomb(10 , 6 , 1 : hpvTypes , 1 : hpvStates , ...
% 1 : periods , g , 4 : 10 , 1 : risk));
% artPop = sum(noV.popVec(: , artInds) , 2);
% hivInds = toInd(allcomb(2 : 6 , 1 : viral , 1 : hpvTypes , 1 : hpvStates, ...
% 1 : periods , g , 4 : 10 , 1 : risk));
% allInds = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvTypes , 1 : hpvStates, ...
% 1 : periods , g , 4 : 10 , 1 : risk));
% hivPop = sum(noV.popVec(: , hivInds) , 2);
% allPop = sum(noV.popVec(: , allInds) , 2);
% plot(tVec , 100 * (hivPop + artPop) ./ allPop)
% hold on
% end
% xlabel('Year')
% ylabel('Prevalence')
% title('HIV Prevalence')
% %%
% hold on
% for g = 1 : 2
% artInds = toInd(allcomb(10 , 6 , 1 : hpvTypes , 1 : hpvStates , ...
% 1 : periods , g , 4 : 10 , 1 : risk));
% artPop = sum(c90_2vFull.popVec(: , artInds) , 2);
% hivInds = toInd(allcomb(2 : 6 , 1 : viral , 1 : hpvTypes , 1 : hpvStates, ...
% 1 : periods , g , 4 : 10 , 1 : risk));
% allInds = toInd(allcomb(1 : disease , 1 : viral , 1 : hpvTypes , 1 : hpvStates, ...
% 1 : periods , g , 4 : 10 , 1 : risk));
% hivPop = sum(c90_2vFull.popVec(: , hivInds) , 2);
% allPop = sum(c90_2vFull.popVec(: , allInds) , 2);
% plot(tVec , 100 * (hivPop + artPop) ./ allPop)
% hold on
% end
% legend('Male' , 'Female' , 'Male Vax' , 'Female Vax')
%
% %%
% figure()
% for g = 1 : 2
% hivSusInds = [toInd(allcomb(1 , 1 , 1 : hpvTypes , 1 : hpvStates , ...
% 1 : periods , g , 4 : 10 , 1 : risk)); ...
% toInd(allcomb(7 : 9 , 1 , 1 : hpvTypes , 1 : hpvStates , ...
% 1 : periods , g , 4 : 10 , 1 : risk))];
% hivSus = annlz(sum(c90_2vFull.popVec(: , hivSusInds) , 2)) ./ stepsPerYear;
% plot(tVec(1 : stepsPerYear : end) , ...
% annlz(sum(sum(c90_2vFull.newHiv(: , g , 4 : 10 , :) ...
% , 3) , 4)) ./ hivSus * 100)
% hold on
% end
%
% xlabel('Year'); ylabel('Rate Per 100'); title('HIV Incidence')
% hold on
%
% for g = 1 : 2
% hivSusInds = [toInd(allcomb(1 , 1 , 1 : hpvTypes , 1 : hpvStates , ...
% 1 : periods , g , 4 : 10 , 1 : risk)); ...
% toInd(allcomb(7 : 9 , 1 , 1 : hpvTypes , 1 : hpvStates , ...
% 1 : periods , g , 4 : 10 , 1 : risk))];
% hivSusNo = annlz(sum(noV.popVec(: , hivSusInds) , 2)) ./ stepsPerYear;
% plot(tVec(1 : stepsPerYear : end) , ...
% annlz(sum(sum(noV.newHiv(: , g , 4 : 10 , :) ...
% , 3) , 4)) ./ hivSusNo * 100 )
% end
% legend('Male' , 'Female' , 'Male No Vax' , 'Female No vax')