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Estimation of HIV Prevalence and Covariance Within High-Risk Groups with Bayesian Hierarchical Modeling (304776)

Keywords: Bayesian hierarchical modeling, covariance estimation, semiparametric modeling, HIV

HIV/AIDS prevalence has decreased dramatically for most countries since the initial outbreak of the epidemic 30 years ago. However, certain populations, such as female sex workers, their clients, and intravenous drug users are rarely directly included in countries’ response to the HIV/AIDS epidemic, leading to disproportionately high prevalence of HIV/AIDS among these high risk groups. Moreover, data on these groups are often sparse due to their hard-to-reach nature. To improve estimation of HIV prevalence among high risk groups, we propose a semiparametric Bayesian hierarchical model, which will efficiently pool information together across the risk groups and allow for better estimation of HIV prevalence within risk groups with scarce data. We also estimate the covariance of prevalence across groups. This is a preliminary investigation and we will incorporate the final proposed model in Spectrum/EPP, a software that has been used by 163 countries to produce the 2015 estimates of HIV and HIV-related indicators.