AIDS and Behavior

, Volume 16, Issue 2, pp 312–322 | Cite as

Concurrent Partnerships, Acute Infection and HIV Epidemic Dynamics Among Young Adults in Zimbabwe

  • Steven M. Goodreau
  • Susan Cassels
  • Danuta Kasprzyk
  • Daniel E. Montaño
  • April Greek
  • Martina Morris
Original Paper


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.


HIV/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).

Supplementary material

10461_2010_9858_MOESM1_ESM.doc (166 kb)
Supplementary material 1 (DOC 166 kb)


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Steven M. Goodreau
    • 1
  • Susan Cassels
    • 2
  • Danuta Kasprzyk
    • 3
  • Daniel E. Montaño
    • 3
  • April Greek
    • 3
  • Martina Morris
    • 4
  1. 1.Department of AnthropologyUniversity of WashingtonSeattleUSA
  2. 2.Department of EpidemiologyUniversity of WashingtonSeattleUSA
  3. 3.Battelle Centers for Public Health Research and EvaluationSeattleUSA
  4. 4.Departments of Statistics and SociologyUniversity of WashingtonSeattleUSA

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