The objective of this proposal is to link clinical medicine with mathematical modeling in order to design epidemic control strategies for HIV (based upon combination antiretroviral therapies (ARV) and risk reduction) that will maximize public health benefit. We will design epidemic control strategies for the HIV-infected gay community in San Francisco, where the current prevalence of HIV is 30%. We will accomplish our research objective by utilizing three main methodological approaches: developing and analyzing transmission models, fitting transmission models to data, and using novel statistical and phylogenetic analyses to analyze data sets. We have three specific aims: (i) to formulate and analyze new transmission models, (ii) to develop statistical models using existing time dependent sequence data to specify distributions of inputs for the transmission models, and (iii) to use the transmission models to design epidemic control strategies. To complete specific aim 1 we will: (a) formulate a series of new transmission models that can be used to evaluate and to predict the effects of ARV on the epidemic dynamics and the evolution of the HIV epidemic in the gay community in San Francisco, (b) use the models to predict the incidence and the prevalence of drug resistance, (c) incorporate data into the theoretical predictions, and (d) identify which drug-resistance-generating (DRG) mechanisms are the most important in contributing to the emergence and transmission of drug resistant HIV. We will evaluate the contribution of the DRG mechanisms on the epidemic of drug resistant HIV that are due to: the patient, the doctor, the viral strain, and the treatment regimen. To complete specific aim 2 we will: (a) develop a distribution of drug resistant phenotypes using Classification and Regression Trees (CART), and (b) estimate person-specific mutation rates as a function of DRG mechanisms using Bayesian phylogenetic reconstruction. To complete specific aim 3 we will: (a) design detailed epidemic control strategies based upon a variety of evaluation criteria using both medical interventions (ARV) and behavioral interventions (risk reduction), and (b) use the evaluation criteria to evaluate trade-offs between the medical and behavioral interventions.
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