RR EC TE D PR

Estimating the sizes of populations at risk of HIV infection from multiple data sources using a Bayesian hierarchical model Le Bao, Adrian E. Raftery∗, and Amala Reddy In most countries in the world outside of sub-Saharan Africa, HIV is largely concentrated in sub-populations whose behavior puts them at higher risk of contracting and transmitting HIV, such as people who inject drugs, sex work-ers and men who have sex with men. Estimating the size of these sub-populations is important for assessing overall HIV prevalence and designing effective interventions. We present a Bayesian hierarchical model for estimating the sizes of lo-cal and national HIV key affected populations. The model incorporates multiple commonly used data sources includ-ing mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and ex-pert opinion. The proposed model is used to estimate the numbers of people who inject drugs in Bangladesh