Transmission modeling to infer tuberculosis incidence, prevalence, and mortality in settings with generalized HIV epidemics

Abstract 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 (ART) have driven dynamic TB epidemics, complicating estimation of burden, trends, and potential intervention impact. We therefore developed a novel age-structured TB transmission model incorporating evolving demographic and HIV/ART effects, and calibrated to TB prevalence and notification data from 12 African countries. We used Bayesian methods to include uncertainty for all TB model parameters, and estimated age-specific annual risks of TB infection (ARTI) and proportion of TB incidence from recent (re)infection (PR). We found ARTI of up to 16.0%/year in adults, but a mean PR 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.