Transmission Modeling to Infer Tuberculosis Incidence Prevalence and Mortality in Settings with Generalized HIV Epidemics

Tuberculosis (TB) killed more people globally than any other single pathogen over the past decade. Where surveillance is weak, estimating TB burden estimates uses modeling. In many African countries, increases in HIV prevalence and antiretroviral therapy have driven dynamic TB epidemics, complicating estimation of burden, trends, and potential intervention impact. We therefore develop a novel age-structured TB transmission model incorporating evolving demographic, HIV and antiretroviral therapy effects, and calibrate to TB prevalence and notification data from 12 African countries. We use Bayesian methods to include uncertainty for all TB model parameters, and estimate age-specific annual risks of TB infection, finding up to 16.0%/year in adults, and the proportion of TB incidence from recent (re)infection, finding a mean across countries of 34%. Rapid reduction of the unacceptably high burden of TB in high HIV prevalence settings will require interventions addressing progression as well as transmission. Accurately estimating the burden of tuberculosis is challenging due to incomplete registration systems and the relationship with HIV. Here, the authors develop a Bayesian modelling strategy accounting for these factors that estimates age- and country-specific annual risks of infection and the proportion resulting from recent infection.