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Bayesian Estimation of Prevalence and At-Risk Group Size in Sexually
Transmitted Infection Epidemics

PI: Adrian Elmes Raftery
Sponsor: Bayesian Estimation of Prevalence and At-Risk Group Size in Sexually Transmitted Infection Epidemics
Project Period: -
Amount: $1,857,552.00

Abstract

The goal of this proposal is to develop new statistical methods for estimating prevalence and the size of at-risk groups in sexually transmitted infection epidemics. We also aim to estimate other policy-relevant quantities such as the number of orphans and children impacted, and treatment needs. We consider two types of epidemic: generalized epidemics, in which the disease is spread throughout the general population, and concentrated epidemics, in which the disease is largely confined to at-risk groups such as intravenous drug users, sex workers and men who have sex with men. Our goal is to develop methods appropriate for countries with sparse data, most of which are developing countries. For generalized epidemics, we propose a susceptible-infected model with a stochastic infection rate. We will develop a Bayesian approach to estimating the model from clinic data over time and sparse household surveys. We will extend the model to take account of changes in treatment availability, and to produce provincial as well as national estimates. For concentrated epidemics, we will first develop new integrated Bayesian methods for estimating the sizes of the main at-risk groups from fragmentary data, including mapping or hotspot data, behavioral surveillance data, program enrollment data and the overlaps between them. Much recent data comes from two relatively new network-based data collection methods, respondent-driven sampling (RDS) and the network scale-up method. We will develop methods for estimating unknown population size from multiple data sources, including RDS and network scale-up. We will then develop methods for estimating at-risk group size and prevalence over time, using a dynamic Bayesian model. We will produce publicly available software to implement our new methods and make them available to the research community and policy-makers.