Antiretroviral treatment (ART) markedly reduces the risk of HIV transmission between stable HIV discordant heterosexual partners and increasing ART use can effect "population-level" reductions in HIV incidence. Whether or not HIV prevention interventions, such as ART "Treatment as Prevention" (TasP) strategies can significantly reduce epidemic growth rates remains a matter of considerable debate, though current community-randomized TasP trials will require vast infrastructure and financial resources. Alternative, less resource-prohibitive approaches are needed, that ideally would also provide real-time insight into HIV transmission dynamics and be easily modified to address unique regional and epidemiological factors. Prospective studies of prevention and treatment interventions that target epidemiologic "hot spots" of HIV transmission may provide an opportunity to efficiently interrupt HIV transmission chains. Effective prevention interventions are particularly relevant in populations of men who have sex with men (MSM) since their incidence rates are disproportionately high compared to other risk groups. We propose to use molecular epidemiology and computational modeling to estimate in real time the risk of onward HIV transmission in newly HIV diagnosed persons. We have shown that by evaluating HIV sequences that are generated in 'real- time'after a participant is identified from our San Diego Primary HIV infection Cohort (SD PIC), a partial transmission network can be inferred rapidly and reliably, and this network can be leveraged to better measure the efficacy of treatments and interventions, correlates of transmission risk, and estimate the size and features of the San Diego HIV epidemic. The proposed study will also address concerns related to patient confidentiality, specifically - unintended disclosure of HIV status or a putative transmission link. We will develop sophisticated quantitative methods to preserve privacy prior to future consideration of potential public health applications. Our primary hypothesis is that the efficacy of HIV prevention interventions, such as TasP (i.e., ART), can be measured within a well-characterized MSM epidemic, using network statistics to assess real time changes in HIV transmission dynamics within the population. This proposal will address the following Specific Aims: 1) To infer the San Diego HIV transmission network using molecular epidemiology and use agent-based simulations to estimate features of the underlying infected population and efficacy of potential interventions, 2) To assess the potential of molecular epidemiology and network statistics to measure the efficacy of ART as a network-based prevention intervention, by comparing HIV transmission rates in persons who initiate early ART compared to those who either delay or decline ART, and 3) To develop and deploy privacy preserving methods for computing transmission network statistics. These same methods could be easily replicated in a prospective fashion across diverse HIV epidemics and could help to prioritize interventions within the scope of available resources.

Public Health Relevance

It is critically important to determine whether antiretroviral therapy (ART) given to a subset of infected individuals within a population, rather than to the entire population, has the potential to control epidemic spread of HIV. We propose to use our 16-year extensive characterization of the San Diego HIV transmission network to evaluate the potential of ART to restrict or terminate HIV transmission clusters (i.e., persons connected to at least one other individual). We will develop critically needed methods to preserve patient privacy such that these methods could be easily replicated in a prospective fashion across diverse HIV epidemics and could help to prioritize interventions within the scope of available resources.

National Institute of Health (NIH)
Research Project (R01)
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AIDS Clinical Studies and Epidemiology Study Section (ACE)
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Pequegnat, Willo
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University of California San Diego
Internal Medicine/Medicine
Schools of Medicine
La Jolla
United States
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