An emerging paradigm in public health seeks to tailor multiple interventions for cost-effectiveness, using epidemic modeling to identify potential areas for interaction. However, the complexities of combination interventions require new methods for multiple reasons. We report on a modeling framework developed to estimate and simulate HIV transmission among men who have sex with men. We incorporate numerous forms of demographic, relational, behavioral, and biological heterogeneity, parameterized from large-scale surveys of MSM in the U.S. We rely on the ERGM framework for networks, with two novel extensions (Krivitsky 2009). Initial results suggest that 33% of infections occur within main partnerships, far less than the 68% estimated in a recent paper (Sullivan et al. 2009). Our estimate for the proportion of infections originating with diagnosed, untreated men is high (59%). We conclude by discussing implications of our early results, and upcoming applications to questions of combination HIV interventions for MSM.
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