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Abstract:
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It is the structure of networks –the restrictions on who contacts whom and when– that shapes the diffusion of pathogens in a population. The spread of disease and the impact of prevention interventions are sensitive not only to characteristics of individual behavior, but also to collective properties of contact networks. We present a Bayesian inference framework for integrating multiple data sources adapted to a staged, HIV-specific epidemic model. In this framework, we jointly model the contact network, transmission network, and epidemic process. Each component constrains and/or is constrained by some aspects of the other two, and can be informed by different data sources. Clinical data on disease progression and the timing of infections informs the epidemic process and the transmission tree. Pathogen genetic data strongly informs the transmission network. Behavioral surveys characterize individual behavior within the contact network, including the distribution of the number of partnerships and their duration. In the end, this framework will permit characterization of collective properties of the contact network in well-characterized risk populations.
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