Empirical Bayes approach to estimating the number of HIV-infected individuals in hidden and elusive populations.

Abstract

In this paper we estimate the numbers of intravenous drug users (IVDUs) and commercial sex workers (CSWs) in Thailand infected with human immunodeficiency virus (HIV) who have not developed acquired immunodeficiency syndrome (AIDS) directly from the semi-annual HIV serosurveillance data of Thailand from June 1993 to June 1995. We propose a 'generalized removal model for open populations' for estimating HIV-infected population size within a hidden, elusive, and perhaps high-risk population group, for all sampling time when capture probabilities vary with time. We apply empirical Bayes methodology to the generalized removal model for open populations by using the Gibbs sampler, a Markov chain Monte Carlo method. No assumption on the size of the hidden population in question is needed to implement this procedure. The statistical method proposed here requires very little computing and only a minimum of two sets of serosurvey data to obtain an estimate, thereby providing a simple and viable option in epidemiological studies when either powerful computing facilities or abundant sampling data are lacking.