HIV Modeling and Projection

Transcription

1 1 HIV Modeling and Projection Wiwat Peerapatanapokin The 21 st Thailand National Epidemiology Seminar Bangkok, Thailand 6 July 2011

2 2 HIV models used at present Simple spreadsheet model: UNAIDS Workbook Curve fitting model: EPP Complex process models: AEM (for Asia), ASSA (for South Africa) Impact model: Spectrum model (Spectrum is not an epidemic model)

3 3 The critical questions to answer Is your epidemic growing? If so, how fast, where, and in what groups? What will most effectively slow or stop this growth? What are the implications for future support, at a e t e p cat o s o utu e suppo t, care and treatment needs?

4 So what tools help answer these questions? Surveillance, behavioral & response data UNAIDS Workbook UNAIDS Estimation & Projection Package Spectrum Asian Epidemic Model 4

5 5 What determines the tools we can use? Availability aab yodaa of data HIV prevalence data (current and past) Size estimates for key populations Behavioral data Condom use Frequency of sex, needle sharing, injection

6 Estimation & projection tools When little data is available 6 UNAIDS workbook/spreadsheet approach Data needs: Sizes of key pops (high and low) Prevalence estimates t (high h and low) Had been used for most of Asia due to limited data availability

7 UNAIDS Workbook prevalence estimation 7

8 Estimation & projection tools When moderate levels of data available 8 Curve fitting models (EPP) Data needs: HIV prevalence trends Sizes of key ypopsp Applicable in a few countries with time series of data, but Gaps in key populations Changing surveillance systems Questions about representativeness of data

9 UNAIDS EPP

10 Estimation & projection tools When moderate levels of data available 10 Impact assessment tools - Spectrum Data needs: HIV incidence or prevalence trends HIV age structures t Demographics Applicable in most countries Can use trends from workbook, EPP, and AEM

11 Spectrum 11 Surveillance data EPP, Workbook, or AEM Adult HIV prevalence Spectrum UN Pop Division population estimates Number infected New infections AIDS deaths Orphans Treatment needs UNAIDS epidemic patterns

12 Spectrum allows various analyses 12

13 Estimation & projection tools When lots of data is available 13 Simulation models (Asian Epidemic Model) Data needs: Time trends in HIV Time trends in behaviors Sizes of key populations Information on responses Applicable in places with extensive data Requires critical analysis of the inputs Extracting trends is time consuming

14 Inside the Asian Epidemic Model The common HIV transmission pattern in Asia 14 FSW ISW Clients Low or no risk males MSM MSW IDUs AEM has compartments for each of these groups Low or no risk females

15 AEM takes behavioral inputs and translates them to epi outputs Sizes & behavioral trends in clients, sex workers, injecting drug users, men who have sex with men, population at large Injecting drug users Female sex workers Tim 15 Bro wn Beiji ng, Jun e AEM Calculation Engine Adult males Adult females Probabilities biliti of Observed transmission HIV trends and start years (white lines)

16 What do these models give us IF we have enough data? 16 UNAIDS Workbooks High-low estimates of numbers of infections & g short term trends

17 What do these models give us IF we have enough data? 17 Workbooks High-low estimates of numbers of infections EPP and Spectrum Numbers of current and new infections (prevalence and incidence) Short term projections (5 yrs) ART needs Information on where new infections arise

18 Number of HIV infections among female in Cambodia 18 80,000 70,000 Current HIV New HIV 67,604 72,069 74,469 75,446 60,000 New Death 60,914 nfections umber of i N 50,000 40,000 30,000 26,847 38,143 50,499 20,000 10, , ,647 11, , ,541 9,879 8,591 8,815 7,574 7,922 6,500 7,477 4,797 5,521 3,839 4,350 2,697 3,189 2,106 1, , Year

19 EPP can also highlight where new infections are coming from 19

20 What do these models give us IF we have enough data? 20 AEM Past history of epidemic where to focus Impacts of past & future behaviors on HIV/STI With additional analysis national/provincial response effectiveness Long term projections, if future behaviors assumed Comparisons of policy/program p alternatives

21 Numb ber of inf fection AEM can provide long term projections 1,400,000 1,200,000 1,000, , , , , Living w/hiv and AIDS Cumulative HIV New HIV

22 In an evolving epidemic, we must adapt responses as the epidemic i changes % 80% Extramarital Female from Husband 60% Male from wife Sex worker 40% Male from sex worker Male sex with male Injection Drug User 20% 0%

23 Evaluation of past prevention efforts and future course of the epidemic Infectio ns in m illions Red line represents what might have been if behaviors had not changed in Thailand Infections prevented Curre ent HIV With behavior change Without prevention

24 24 Conclusion Models are useful for estimating and projecting number of HIV/AIDS. However, countries and sub-national units need to: Know their data availability in selecting models, and Understand the advantages & limitations of each modeling tool before applying them.

25 Thank you 25

26 26 UNAIDS Workbook model summary Inputs Size of subpopulations HIV Prevalence among subpopulations Outputs Current number of HIV infection Advantage Very simple Good for low prevalence countries and countries with very limited data Limitation Get only ypoint estimate in one year Difficult to assess the trend of HIV epidemic

27 27 EPP model summary Inputs Size of subpopulations Series of HIV Prevalence among subpopulations Outputs Current number of HIV infection Trends of HIV infection among subpopulations Advantage Good for countries with series of HIV prevalence data Can be separated sub epidemic i by geographic area Get trends of prevalence among subpopulation and geographic area Limitation Difficult to assess the policy impact to HIV epidemic Can produce only short term projection (less than 5 years)

28 Spectrum model summary (This is an impact model, not epidemic model) 28 Inputs Epidemic trends from Workbook model, EPP or AEM Demographic data, male to female ratio, Progression, PMTCT data, ART data. Outputs Number of HIV, Number of treatment needs, deaths, new infection Children infection Advantage Provide impact estimations Simple to use Limitation Need epidemic trends from other epidemic model Limited policy analysis Not separate by sup-populationpopulation

29 29 AEM model summary Inputs Size of subpopulations Series of HIV Prevalence among subpopulations Behaviors among these subpopulations p Outputs New and Current number of HIV infection Trends of HIV infection among subpopulations HIV Impacts including AIDS cases, deaths, children infections, ART needs, etcs. Advantages Good for countries with series of HIV prevalence data and behavior data Get trends of prevalence among subpopulation Policy Analyses can be done to assess the impact of policy, programs, and interventions. Get long term projection Limitationsit ti Cannot be separated to subepidemic by geographic area in one dataset Need lots of data