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

Le Bao, Adrian E. Raftery, Amala Reddy

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)125-136
Number of pages12
JournalStatistics and its Interface
Volume8
Issue number2
DOIs
StatePublished - 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Applied Mathematics

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