1. The Contribution of Early HIV Infection to HIV Spread in Lilongwe, Malawi: Implications for Transmission Prevention Strategies Kimberly Powers, 1 Azra Ghani, 2 William Miller, 1 Irving Hoffman, 1 Audrey Pettifor, 1 Gift Kamanga, 3 Francis Martinson, 3 Myron Cohen 1 1. University of North Carolina at Chapel Hill, 2. Imperial College London, 3. UNC Project Malawi

2. Early HIV Infection HIV transmission risk is ↑ ↑ ↑ ↑ during early HIV infection (EHI). Interventions targeting EHI could be very efficient in limiting epidemic spread. BUT EHI is brief and case detection is difficult. EHI contribution to epidemic spread varies and has implications for prevention strategies.

3. Role of EHI in Epidemic Spread Useful to elucidate role of EHI IF BIG EHI ROLE: Effects of CHI-only interventions may be limited. IF SMALL EHI ROLE: EHI detection & interventions may be harder to justify. SMALL

4. Role of EHI: Model Estimates Hollingsworth et al 2008 Hayes & White 2006* Pinkerton & Abramson 1996** Kretzschmar & Dietz 1998**† Xiridou et al 2004 Jacquez et al 1994 Abu-Raddad & Longini 2008† Salomon & Hogan 2008* Koopman et al 1997** Pinkerton 2007 Prabhu et al 2009 * Range of estimates reflects the proportion of all transmissions during an individual’s entire infectious period that occur during EHI. The extent to which this proportion corresponds with the proportion of all transmissions that occur during EHI at the population level will depend on the epidemic phase and the distribution of sexual contact patterns. ** Transmission probabilities were drawn from the population category shown, but the reported estimates result from a range of hypothetical sexual behavior parameters that do not necessarily reflect a specific population. † The range of estimates shown was extracted from the endemic-phase portion of graphs showing the time-course of the proportion due to EHI.

5. Role of EHI: Model Estimates Hollingsworth et al 2008 Hayes & White 2006* Pinkerton & Abramson 1996** Kretzschmar & Dietz 1998**† Xiridou et al 2004 Jacquez et al 1994 Abu-Raddad & Longini 2008† Salomon & Hogan 2008* Koopman et al 1997** Pinkerton 2007 Prabhu et al 2009 * Range of estimates reflects the proportion of all transmissions during an individual’s entire infectious period that occur during EHI. The extent to which this proportion corresponds with the proportion of all transmissions that occur during EHI at the population level will depend on the epidemic phase and the distribution of sexual contact patterns. ** Transmission probabilities were drawn from the population category shown, but the reported estimates result from a range of hypothetical sexual behavior parameters that do not necessarily reflect a specific population. † The range of estimates shown was extracted from the endemic-phase portion of graphs showing the time-course of the proportion due to EHI. Difficult to obtain data for informing models Effects of interventions during EHI unknown

6. Study Objectives Based on data from our ongoing work in Lilongwe, Malawi: – Estimate the proportion of HIV transmissions attributable to index cases with EHI – Predict the reduction in HIV prevalence achievable through detection and interventions during EHI

7. Methods Data-driven, deterministic model, with: – Heterosexual transmission within & outside steady pairs – Multiple infection stages – Two risk groups Sexual behavior parameters from detailed study of partnership patterns at Lilongwe STI Clinic Bayesian melding procedure to fit model to observed HIV prevalence (ANC data)

8. Stages of Infection EHIAsymptomatic Period Early AIDS AIDS →→→ ~ 1 to ~6 months* Average EHI transmission probability 26 times as high as during asymptomatic period* Changing transmission probabilities within EHI based on longitudinal viral load data from Lilongwe** * Hollingsworth et al, JID 2008.**Pilcher et al, AIDS 2007.

9. Lilongwe ANC Prevalence Data ANC data

10. Lilongwe ANC Prevalence Data Best-fitting model estimates 95% credible intervals ANC data

11. Predicted Contribution of EHI 38% 19% 58% Best fitting model estimates 95% credible intervals

12. Transmission-suppressing intervention Assumed generic intervention that ↓↓↓ infectivity in those receiving it – e.g., complete viral suppression, effective condom use

13. Transmission-suppressing intervention EHICHI EHICHI EHICHI (Approximates test-and-treat with annual tests) (No residual effect during CHI)

14. EHI-only Prevention Strategy Assuming transmission is almost completely suppressed in various proportions of EHI cases only (no residual effect): If suppression in 100% CHI Transmission suppressed in: 25% EHI cases 50% EHI cases 75% EHI cases 100% EHI cases No intervention

15. CHI-only Prevention Strategy Assuming transmission is almost completely suppressed in 75% of CHI cases only (beginning to end of CHI): Transmission suppressed in: 75% CHI + 0% EHI cases No intervention

16. 75% CHI coverage, 25% EHI coverage Assuming transmission is almost completely suppressed in 75% of CHI cases and 25% of EHI cases: Transmission suppressed in: 75% CHI + 0% EHI cases 75% CHI + 25% EHI cases No intervention

17. 75% CHI coverage, 50% EHI coverage Assuming transmission is almost completely suppressed in 75% of CHI cases and 50% of EHI cases: Transmission suppressed in: 75% CHI + 0% EHI cases 75% CHI + 50% EHI cases No intervention

18. 75% CHI coverage, 75% EHI coverage Assuming transmission is almost completely suppressed in 75% of CHI cases and 75% of EHI cases: Transmission suppressed in: 75% CHI + 0% EHI cases 75% CHI + 75% EHI cases No intervention

19. Limitations Models are simplified representations of reality. – Model was based on data from setting of interest – Model allowed transmission within and outside pairs – Model included multiple risk groups & infection stages Uncertainties surround input parameter values. – Model fit to ANC data to identify most likely input values – Sensitivity analyses around predicted EHI contribution

20. Conclusions EHI plays an important role in the HIV epidemic of Lilongwe, Malawi. A perfect intervention with 100% coverage throughout ALL of CHI may eliminate HIV. Anything less will require strategies during EHI. It is time to determine: – The best ways to identify EHI cases – The optimal prevention strategies during EHI

21. Acknowledgments UNC – Bill Miller – Mike Cohen – Irving Hoffman – Audrey Pettifor Imperial College London – Azra Ghani – Christophe Fraser – Tim Hallett – Rebecca Baggaley UNC Project Malawi – Gift Kamanga – Robert Jafali – Mina Hosseinipour – David Chilongozi – Francis Martinson Funding from – NIH – UNC CFAR