Concurrent Partnerships, Acute Infection and HIV Epidemic Dynamics Among Young Adults in Zimbabwe
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This paper explores the roles of acute infection and concurrent partnerships in HIV transmission dynamics among young adults in Zimbabwe using realistic representations of the partnership network and all published estimates of stage-specific infectivity. We use dynamic exponential random graph models to estimate partnership network parameters from an empirical study of sexual behavior and drive a stochastic simulation of HIV transmission through this dynamic network. Our simulated networks match observed frequencies and durations of short- and long-term partnerships, with concurrency patterns specific to gender and partnership type. Our findings suggest that, at current behavior levels, the epidemic cannot be sustained in this population without both concurrency and acute infection; removing either brings transmission below the threshold for persistence. With both present, we estimate 20–25% of transmissions stem from acute-stage infections, 30–50% from chronic-stage, and 30–45% from AIDS-stage. The impact of acute infection is strongly moderated by concurrency. Reducing this impact by reducing concurrency could potentially end the current HIV epidemic in Zimbabwe.
KeywordsHIV/AIDS Sexual networks Concurrent partnerships Acute infection Zimbabwe Mathematical modeling Stochastic simulation ERGM
The authors would like to thank the study participants as well as Mark Handcock, David Hunter, Pavel Krivitsky, Carter Butts, and the entire statnet development team. We are also grateful for the helpful comments from people who read earlier drafts, including Jim Shelton and Helen Epstein. SMG and SC were supported in part by the Puget Sound Partners for Global Health (Research and Technology Project Award 26145). NIH provided support for the data collection (U10-MH061544, R2I-AA014802), the methodology and software development (R01-HD041877 and R01-DA12831), and the analysis (K99-HD057553 and P30-AI27757).
- 1.Coombs RW, Speck CE, Hughes JP, Lee W, Sampoleo R, Ross SO, et al. Association between culturable human immunodeficiency virus type 1 (HIV-1) in semen and HIV-1 RNA levels in semen and blood: Evidence for compartmentalization of HIV-1 between semen and blood. J Infect Dis. 1998;177(2):320–30.PubMedCrossRefGoogle Scholar
- 5.Jacquez JA, Koopman J, Simon CP, Longini IMJ. Role of the primary infection in epidemics of HIV-infection in gay cohorts. J Acquir Immune Defic Syndr Hum Retrovirol. 1994;7(11):1169–84.Google Scholar
- 12.Hollingsworth TD, Anderson RM, Fraser C, editors. HIV-1 transmission, by stage of infection. Univ. Chicago Press; 2008.Google Scholar
- 14.Eaton JW, Hallett TB, Garnett GP. Concurrent sexual partnerships and primary HIV infection: a critical interaction. AIDS Behav. 2010. doi: 10.1007/s10461-010-9787-8.
- 22.Brenner BG, Roger M, Routy JP, Moisi D, Ntemgwa M, Matte C, et al., editors. High rates of forward transmission events after acute/early HIV-1 infection. Univ Chicago Press; 2007.Google Scholar
- 26.Sawers L, Stillwaggon E. Concurrent sexual partnerships do not explain the HIV epidemics in Africa: a systematic review of the evidence. J Int AIDS Soc. 2010;13:34.Google Scholar
- 27.NIMH Collaborative HIV/STD Prevention Trial Group. Methodological overview of a five-country community-level HIV/sexually transmitted disease prevention trial. AIDS. 2007;21 Suppl 2:S3–18.Google Scholar
- 28.Kasprzyk D, Montaño DE. Application of an integrated behavioral model to understand HIV prevention behavior of high-risk men in rural Zimbabwe. In: Ajzen I, Hornik R, editors. Prediction and change of health behavior: applying the reasoned action approach. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.; 2007.Google Scholar
- 29.UNAIDS Reference Group on Estimates Modelling and Projections. Consultation on concurrent sexual partnerships: recommendations from a meeting of the UNAIDS reference group on estimates, modelling and projections held in Nairobi, Kenya, April 20–21 2009. http://www.epidem.org/Publications/Concurrency%20meeting%20recommendations_Final.pdf2009.
- 30.Central Intelligence Agency. CIA World Factbook—Zimbabwe. 2009. https://www.cia.gov/library/publications/the-world-factbook/geos/zi.html Accessed 12 Dec 2009.
- 32.Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M. ERGM: a package to fit, simulate and diagnose exponential-family models for networks. 2.2-5 ed2010.Google Scholar
- 33.Burington B, Hughes JP, Whittington WL, Stoner B, Garnett G, Aral SO, et al. Estimating duration in partnership studies: issues, methods and examples. Sex Transm Infect. 2010;86(2):84–9.Google Scholar
- 35.Strauss D, Ikeda M. Pseudolikelihood estimation for social networks. J Am Stat Soc. 1990;85(409):204–12.Google Scholar
- 37.Handcock MS. Statistical models for social networks: degeneracy and inference. In: Breiger RL, Carley KM, Pattison P, editors. Dynamic social network modeling and analysis: workshop summary and papers. Washington: National Academies Press; 2003. p. 229–40.Google Scholar
- 42.Krivitsky PN. Statistical models for social network data and processes. Seattle: University of Washington; 2009.Google Scholar
- 43.Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M. Statnet: software tools for the statistical modeling of network data. 2.0 ed2003.Google Scholar
- 44.The World Bank. World development indicators. Washington: The World Bank; 2010.Google Scholar
- 46.Joint United Nations Programme on HIV/AIDS. AIDS epidemic update. Geneva: UNAIDS, World Health Organization; 2009.Google Scholar
- 48.May RM. Population biology of microparasite infections. In: Hallam TG, Levin SA, editors. Mathematical ecology: an introduction. Berlin: Springer-Verlag; 1986. p. 405–42.Google Scholar
- 50.Morris M, Epstein H, et al. Timing is everything: international variations in historical sexual partnership concurrency and HIV prevalence. PLoS One e14092. 2010. doi: 10.1371/journal.pone.0014092.