Combining theoretical and empirical network approaches to HIV transmission

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DESCRIPTION (provided by applicant): Extensive empirical research has shown that the transmission of HIV and STIs is profoundly influenced by contact network structure and behavioral risks. Yet the diverse insights gleaned from these studies have not yet been integrated into a general framework for understanding the endemic and epidemic spread of STIs through heterogeneous populations. There are a number of mathematical approaches to modeling disease transmission through contact networks (for example, compartment models; actor-based models; network simulations; and exponential random graph models [also know as p* models]), but most make simplifying assumptions that are inconsistent with empirical observations. In particular, many models assume that contact and behavioral patterns remain constant for the duration of an outbreak. In this exploratory project, we propose (1) to develop broad hypotheses linking population structure and behavioral risk to STI transmission dynamics through extensive statistical analyses of data from eight empirical network studies, and (2) to test these hypotheses using powerful new mathematical models that explicitly consider the epidemiological impact of network fluidity. Our empirical data derive from eight completed network studies (six in Atlanta GA and one each in Colorado Springs CO and Flagstaff AZ), and one ongoing study in Atlanta, all of which used the same basic questionnaire. We will assemble and amalgamate these studies, and we estimate that 80% of the variables in each of these studies will be congruent. We will conduct multivariable analyses to determine which behavioral and population factors most strongly influence the transmission of HIV/STIs. Then, using mathematical models that incorporate the network structures, behavioral risks, and prevalence patterns calculated from our eight studies, we will quantitatively explore the implications of these influential factors, the efficacy of various empirical network study designs, and the differential transmission of diverse pathogens in the same network. PUBLIC HEALTH RELEVANCE: Field studies convincingly suggest that many factors simultaneously influence HIV/STI transmission, but such intuition is difficult to document. By viewing eight diverse communities through a common lens and adapting the latest generation of mathematical models to reflect the observed patterns, we aim to construct a theoretical framework that robustly relates network structure, behavioral risks, and epidemiological outcomes.