Estimating the proportion of tuberculosis recent transmission via simulation

Abstract

Tuberculosis (TB) is an infectious disease that can progress rapidly after infection or enter a period of latency that can last many years before reactivation. Accurate estimation of the proportion of TB disease representing recent versus remote (long ago) transmission is critical to disease-control policymaking (e.g., high rates of recent transmission demand more aggressive diagnostics). Existing approaches to this problem through cluster analysis of TB strains in population-based studies of TB molecular epidemiology are crude and prone to bias. We propose an agent-based simulation of TB transmission in conjunction with molecular epidemiologic techniques that enables study of clustering dynamics in relation to disease incidence, diversity of circulating strains, sampling coverage, and study duration. We perform a sequence of simulation experiments with regard to different levels of each factor, and study the accuracy of estimates from the cluster-analysis method relative to the true proportion of incidence due to recent transmission.