Work

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

Year: 2015

Type: article

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.

Source: Statistics and Its Interface

DOI: 10.4310/sii.2015.v8.n2.a1

Authors: Le Bao, Adrian E. Raftery, Amala Reddy

Institutions: Pennsylvania State University, University of Washington, Joint United Nations Programme on HIV/AIDS

Language: English

Topic: Census and Population Estimation

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: Science Foundation Ireland, Ministry of Health and Family Welfare, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development