Simulating the origin of HIV-1 group M in Kinshasa: why did the epidemic emerge when it did?

1. Simulating the origin of HIV-1 group M in Kinshasa:why did the epidemic emerge when it did? Viktor Müller1*, João Dinis de Sousa2, Philippe Lemey2 & Anne-Mieke Vandamme2 1Institute of Biology, EötvösLoránd University, Budapest, Hungary 2Laboratory for Clinical and Evolutionary Virology, Rega Institute for Medical Research, KatholiekeUniversiteit Leuven, Leuven, Belgium

2. The origin(s) of HIV • Multiple origins (cross-species jumps) • 4 successful groups: • HIV-1 groups M and O • HIV-2 groups A and B • Several unsuccessful groups

3. A unique time window for the origin of epidemic HIVs • All successful HIV lineages originated in the first half of the 20th century.

4. The problem of timing • Why not earlier? • Continuous exposure to bushmeat since prehistoric times • Urbanization, increased mobility, etc. • Parenteral transmission? Source: Nature 455, 605.

5. Why not later? HIV emergence Livingwild chimps Global syringe production Cities HIV-1-O HIV-1-M HIV-2-A and B 108 400k 107 2 million Treatments in Belgian Congo 106 # of people treated in millions Jenner. vacc. in millions 200k Kinshasa Brazzaville 105 300k 40k 104 1970 1930 1910 1950 Time

6. Hypothesis Epicentres of HIV emergence offered particularly permissive conditions for epidemic spread. • Factors affecting heterosexual spread today: • Contact network (partner exchange rate, concurrency) • Acute infection • Circumcision • Genital ulcer disease (GUD) • The same factors might have been important at the origin of HIV.

7. Case study: Kinshasa • Epicentre of HIV-1 group M • Data collection • Scientific literature (medical, virological, ethnographic, etc) • Original archive research

8. Case study: Kinshasa

9. Case study: Kinshasa • GUD peak in the early 20th century • Lower levels of circumcision than today

10. Simulation model • Individual based model • HIV status (age of infection) • GUD status (age of ulcer period) • Circumcision status • Population classes • Single men/women • Married men/women • “Femmes libres” • Commercial sex workers (CSW) • Fixed population • Dynamic network of contacts • Time step: 1 week, duration: 1 year. • Stochastic processes.

11. Simulation model: sexual network

12. Simulation model: sexual network • Stable links

13. Simulation model: sexual network • Stable links • Short-term links

14. Simulation model: sexual network • Stable links • Short-term links • CSW links • Parameters from current Yaoundé

15. Simulation model: sexual network WEEK 1

16. Simulation model: sexual network WEEK 2

17. Simulation model: sexual network WEEK 3

18. Simulation model: tracking HIV WEEK 1

19. Simulation model: tracking HIV WEEK 2

20. Simulation model: tracking HIV WEEK 3

21. Simulation model: time step • Dissolution and formation of sexual links • Generation of sexual contacts over links • Transmission of HIV over sexual contacts • Update GUD and HIV status, infection age

22. Simulation model: time step • Dissolution and formation of sexual links • Generation of sexual contacts over links • Transmission of HIV over sexual contacts • Update GUD and HIV status, infection age • Run sexual network till steady state

23. Simulation model: time step • Dissolution and formation of sexual links • Generation of sexual contacts over links • Transmission of HIV over sexual contacts • Update GUD and HIV status, infection age • Run sexual network till steady state • Introduce HIV into a single man.

24. Simulation model: parameters Factors affecting heterosexual spread: • Contact network (partner exchange rate, concurrency) • Genital ulcer disease (GUD) • Acute infection • Circumcision • Initial lack of adaptation

25. Kinshasa: historical scenarios • Pre-colonial village: small population, balanced sex ratio, no GUD. • 1919: growing population, distorted sex ratio, rampant GUD, uncircumcised subpopulations • 1929: further population growth • 1958: further population growth, circumcision universal, GUDs controlled, normalizing sex ratio.

26. Results#1: historical scenarios 1000 simulation runs for each scenario • 1919 and 1929 scenarios are particularly permissive for HIV spread

27. Sensitivity analysis The results were robust with respect to • Varying multiple network parameters • Varying baseline transmission probability • Varying the effect of circumcision • Allowing for chronic transmission

28. Results#2: which factors mattered? With respect to the 1919 default, we tested: • 10-fold reduced population size • Balanced sex ratio • No GUD • Universal circumcision

29. Results#2: which factors mattered? • GUD prevalence had by far the strongest effect

30. Conclusions & Discussion • The origin of epidemic HIVs was probably facilitated by rampant GUD epidemics in colonial cities. • The “window of opportunity” was probably closed by aggressive campaigns against GUDs from the mid 30’s. • Circumcision and population size/structure probably had indirect impact by affecting GUD prevalence. • Control of GUDs is essential to prevent emergence of new HIV groups. • (GUD-facilitated emergence is consistent with the origin of all epidemic HIV groups: HIV-1 group O in Douala (Cameroon), HIV-2 groups A and B in Abidjan (Côte d’Ivoire)).

31. Acknowledgements João Dinis de Sousa Anne-Mieke Vandamme Funding: Philippe Lemey