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Saturation effects and the concurrency hypothesis: Insights from an analytic model

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  • Joel C Miller
  • Anja C Slim

Abstract

Sexual partnerships that overlap in time (concurrent relationships) may play a significant role in the HIV epidemic, but the precise effect is unclear. We derive edge-based compartmental models of disease spread in idealized dynamic populations with and without concurrency to allow for an investigation of its effects. Our models assume that partnerships change in time and individuals enter and leave the at-risk population. Infected individuals transmit at a constant per-partnership rate to their susceptible partners. In our idealized populations we find regions of parameter space where the existence of concurrent partnerships leads to substantially faster growth and higher equilibrium levels, but also regions in which the existence of concurrent partnerships has very little impact on the growth or the equilibrium. Additionally we find mixed regimes in which concurrency significantly increases the early growth, but has little effect on the ultimate equilibrium level. Guided by model predictions, we discuss general conditions under which concurrent relationships would be expected to have large or small effects in real-world settings. Our observation that the impact of concurrency saturates suggests that concurrency-reducing interventions may be most effective in populations with low to moderate concurrency.

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  • Joel C Miller & Anja C Slim, 2017. "Saturation effects and the concurrency hypothesis: Insights from an analytic model," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-19, November.
    • Handle: RePEc:plo:pone00:0187938
    DOI: 10.1371/journal.pone.0187938
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    References listed on IDEAS

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    1. Leung, K.Y. & Kretzschmar, M.E.E. & Diekmann, O., 2012. "Dynamic concurrent partnership networks incorporating demography," Theoretical Population Biology, Elsevier, vol. 82(3), pages 229-239.
    2. Christel Kamp, 2010. "Untangling the Interplay between Epidemic Spread and Transmission Network Dynamics," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-9, November.
    3. Adams, J. & Moody, J. & Morris, M., 2013. "Sex, drugs, and race: How behaviors differentially contribute to the sexually transmitted infection risk network structure," American Journal of Public Health, American Public Health Association, vol. 103(2), pages 322-329.
    4. Fredrik Liljeros & Christofer R. Edling & Luís A. Nunes Amaral & H. Eugene Stanley & Yvonne Åberg, 2001. "The web of human sexual contacts," Nature, Nature, vol. 411(6840), pages 907-908, June.
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