1. Modeling ‘test and treat’ for HIV in South Africa Jan AC Hontelez 1,2,3, Mark N Lurie 4, Till Bärnighausen 3,5, Roel Bakker 1 Rob Baltussen 2, Frank Tanser 3, Timothy B Hallett 6, Marie- Louise Newell 3, and Sake J de Vlas 1 1 Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands 2 Nijmegen International Center for Health System Analysis and Education (NICHE), Department of Primary and Community Care, Radboud University Nijmegen Medical Centre, Netherlands 3 Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Mtubatuba, South Africa 4 Department of Epidemiology and the International Health Institute, Warren Alpert Medical School, Brown University, Providence, RI, USA 5 Department of Global Health and Population, Harvard School of Public Health, Boston, USA 6 Imperial College, London, UK Contact: j.hontelez@erasmusmc.nl

2. Introduction  Universal test and treat (UTT) suggested to drive HIV into an elimination phase (incidence < 1 / 1,000 person-years) in South Africa  Many mathematical models predict impact of UTT, wide range of different results  Eaton et al: Models agree on potential of ART to reduce incidence, but disagree on the amount of reduction and overall impact  Objective: Examine the impact of different model structures and parameterizations on predicting the impact of UTT in South Africa using a highly controlled experiment

3. Study outline Model B Model A Age structured population (Model B1) Heterogeneity in transmission by HIV stage (Model B2) Heterogeneity in sexual behaviour (Model C1) HIV prevention interventions and STI co-factors (Model C2) Up-to-date ART effectiveness assumptions (Model C3) Model C Model D Current ART scale-up in South Africa (at ≤350 cells/µL) Universal HIV testing and immediate treatment for all, at 90% coverage, starting in 2012

4. Model A Model B Model A Age structured population (Model B1) Heterogeneity in transmission by HIV stage (Model B2) Heterogeneity in sexual behaviour (Model C1) HIV prevention interventions and STI co-factors (Model C2) Up-to-date ART effectiveness assumptions (Model C3) Model C Model D Current ART scale-up in South Africa (at ≤350 cells/µL)

5. Model A

6. Overall effectiveness

7. Model B Model A Age structured population (Model B1) Heterogeneity in transmission by HIV stage (Model B2) Heterogeneity in sexual behaviour (Model C1) HIV prevention interventions and STI co-factors (Model C2) Up-to-date ART effectiveness assumptions (Model C3) Model C Model D Current ART scale-up in South Africa (at ≤350 cells/µL)

8. Model B

9. Overall effectiveness

10. Model B Model A Age structured population (Model B1) Heterogeneity in transmission by HIV stage (Model B2) Heterogeneity in sexual behaviour (Model C1) HIV prevention interventions and STI co-factors (Model C2) Up-to-date ART effectiveness assumptions (Model C3) Model C Model D Current ART scale-up in South Africa (at ≤350 cells/µL)

11. Model C

12. Overall effectiveness

13. Model B Model A Age structured population (Model B1) Heterogeneity in transmission by HIV stage (Model B2) Heterogeneity in sexual behaviour (Model C1) HIV prevention interventions and STI co-factors (Model C2) Up-to-date ART effectiveness assumptions (Model C3) Model C Model D Current ART scale-up in South Africa (at ≤350 cells/µL)

14. Model D

15. Overall effectiveness

16. Conclusions and implications  We confirm results by Granich and colleagues that the HIV epidemic in South Africa can be driven into an elimination phase through expanded access to ART  Models differ substantially in predicted time till 0.1% incidence is achieved and impact of the intervention Most important components in driving differences between models:  Sexual networks  Prevalence density function versus prevention interventions  ART scale-up  Heterogeneity in HIV transmission

17. Conclusions and implications (2)  Predicted effectiveness of UTT declines as important underlying dynamics of the epidemic are taking into account Important implications for future modeling studies on the impact of treatment as prevention  Current treatment roll-out may already have such a substantial impact that the epidemic will reach the 0.1% incidence threshold if current scale-up is maintained and universal access achieved

18. Conclusions and implications (3)  Universal test and treat intervention is still cost effective, yet assumptions on programmatic effectiveness are rather optimistic.  Detailed incremental cost-effectiveness analyses with more realistic assumptions on programmatic effectiveness of treatment as prevention are needed  Detailed cost-effectiveness analyses to inform policy makers and guidelines should be performed with models that allow for sufficient levels of detail in modeling the underlying epidemic

19. Acknowledgements  Co-authors  Mark N Lurie  Till Bärnighausen  Roel Bakker  Rob Baltussen  Frank Tanser  Timothy B Hallett  Marie-Louise Newell  Sake J de Vlas  Funders:  Bill & Melinda Gates Foundation through grants from the HIV modelling consortium  NIH