Statistics and Its Interface

Volume 8 (2015)

Number 2

Special Issue on Modern Bayesian Statistics (Part II)

Guest Editor: Ming-Hui Chen (University of Connecticut)

Estimating the sizes of populations at risk of HIV infection from multiple data sources using a Bayesian hierarchical model

Pages: 125 – 136



Le Bao (Department of Statistics, Pennsylvania State University, U.S.A.)

Adrian E. Raftery (Departments of Statistics and Sociology, University of Washington, Seattle, Wash., U.S.A.)

Amala Reddy (UNAIDS, Regional Support Team for Asia and the Pacific, Thailand)


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 workers 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 local and national HIV key affected populations. The model incorporates multiple commonly used data sources including mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and expert opinion. The proposed model is used to estimate the numbers of people who inject drugs in Bangladesh.


capture-recapture, expert opinion, heterogeneity, HIV/AIDS epidemic, injecting drug user, key affected population, mapping data, Markov chain, Monte Carlo, key affected population, multiplier method

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