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EPP 2009

EPP 2009. HIV Estimation and projection tools Session XIIA 2 nd Global HIV/AIDS Surveillance Meeting Bangkok 2-5.03.09. A bit of history. A three stage process Point estimate Workbook Epidemic Curve Workbook OR EPP Outcomes Spectrum. The Point Estimate- Workbook. . f 0. t 0. r.

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EPP 2009

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  1. EPP 2009 HIV Estimation and projection tools Session XIIA 2nd Global HIV/AIDS Surveillance Meeting Bangkok 2-5.03.09

  2. A bit of history • A three stage process • Point estimate • Workbook • Epidemic Curve • Workbook OR EPP • Outcomes • Spectrum

  3. The Point Estimate-Workbook

  4. f0 t0 r UNAIDS Reference Group model

  5. The Curve-Workbook

  6. EPP 2009

  7. EPP 2009 leads you througheach important step Each “tab” represents a step in the process Note new larger interface – more data shown, bigger graphs

  8. The EPP Define Epi page Create your own epidemic tree in panel on the right

  9. A bigger HIV data page Data is entered by sites for each sub-pop For each site give HIV prevalence & sample size

  10. EPP 2009’s first big change – ART Data Enters number on 1st and 2nd line ART nationally Divides that ART among the sub-populations

  11. Providing more input to fitting – Surveys Page Can enter up to 3 surveys for each sub-pop Or can choose not to include surveys in the fitting process

  12. The Curve-EPP

  13. Things to know about surveys in EPP 2009 • If you enter surveys, they will be used in fitting the epidemic • If you do not enter surveys in generalized epidemics, we will automatically calibrate • Fits to ANC data are adjusted downward • Adjustment based on an average of national survey prevalence to ANC prevalence in countries with national surveys • Urban and rural adjustments are different

  14. EPP’s job: fit the model to the data

  15. How does EPP 2009 fit data? Using a process calledIMISdeveloped by Le Bao & Adrian Raftery

  16. We first randomly generate many curves Curves come from random combinations of r, f0, t0 and phi High weight – fits the data closely. Take its values for r, f0, t0 and phi

  17. Then sample around highest weight curve Finds some new curves around the best fitting one, i.e. one with highest weight

  18. EPP 2009 repeats until lots of curves close to data An iterative process that may run up to 200 times and generate many 1000s of curve

  19. EPP 2009 picks the best one as the UA fit The one that fits the data best is chosen as the UA fit

  20. While fitting EPP 2009 also assesses the uncertainty in the fit

  21. Assessing uncertainty – Bayesian meldingDeveloped by Adrian Raftery, Leontine Alkema and Le Bao for EPP • Randomly generate lots of curves using IMIS procedure • Select a lot of (r, f0, phi and t0) values • Compare the curves with the data • Calculate “goodness” of fit and assign a weight • Likelihood function is used as a weight on the curve • High likelihood means a curve is a good fit and gets a high weight • Resample a smaller number of curves from the curves originally calculated • But, resample according to the weight assigned • The curves that fit better get picked more often • Keep the resampled curves, throw away the others • These curves provide an estimate of the uncertainty

  22. Some countries have the curves with high weights tightly bundled Botswana urban through 2003 – future of epidemic tightly constrained Botswana urban through 2002

  23. In other countries the data does not constrain possible curves much at all Uncertainty about the future is huge Senegal urban through 2003

  24. As more data becomes available projections should improve & uncertainty fall Botswana urban surveillance data through 2003

  25. Uncertainty decreases as more data becomes available Very uncertain Botswana urban using only data through 1995 – data still rising

  26. Uncertainty decreases as more data becomes available Uncertainty is getting smaller Botswana urban using only data through 2000 – points starting to level off

  27. Uncertainty decreases as more data becomes available Uncertainty is narrowing as epidemic levels off Botswana urban using all data through 2003 – data has leveled off

  28. This is the new Projection Page

  29. What happens if we include surveys? Surveys show up in red on the graph before fitting

  30. What happens if we include surveys? After fitting uncertainty bounds are narrower - Surveys assumed to be better estimates ANC data is downward scaled

  31. The EPP 2009 Calibration page 6 calibration options provided Display shows the result of each option One you choose will be used to change the outcomes on the Results page

  32. The EPP 2009 Pop change page Top row – UN Pop % urban 2nd row – your workset’s % urban Bottom – distribution of population among your sub-pops

  33. Results page – putting your projections together “Output results” - show outcomes - create Spectrum file

  34. Results page – putting your projections together “Show uncertainty” - Gives national uncertainty from combining projections

  35. Results page – putting your projections together “ART results” - Summarizes ART findings for national projection

  36. Results page – putting your projections together “Incidence distribution” - Shows how sub-pops contribute to national incidence

  37. Summary - Advanced features in EPP 2009 • Includes influence of ART on prevalence in fitting the epidemic • Uses an improved algorithm to generate better fits and more accurate uncertainties • Allows user to calibrate projections after fitting • Permits changing urban/rural populations • Displays contributions to incidence from sub-populations

  38. EPP 2009Working with concentrated epidemicsspecial features and approaches UNAIDS/WHO Working Group on Global HIV/AIDS & STI Surveillance

  39. Differences in EPP2009 for concentrated epidemics • EPP 2009 includes turnover for sub-populations • EPP 2009 cannot assess uncertainty for concentrated epidemics – • Instead it makes a best fit and • Also shows results for best fitting curves • Includes an Audit check page to verify values

  40. Having turnover forces changes to Define pop page Instead of demographics provide information on additional IDU mortality, duration in group and turnover A new panel is added for “Assign prevalence”

  41. Other differences in EPP 2009 concentrated No “Pop change” page Urban/rural change is not usually well described for at-risk populations

  42. Other differences in EPP 2009 concentrated The Audit check page Checks your population sizes against observed values elsewhere Looks at M/F ratios in AIDS cases

  43. The Results page – Concentrated form

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