Mathematical modelling is commonly used to evaluate infectious disease control policy, and is influential in shaping policy and budgets. Mathematical models necessarily make assumptions about disease natural history, and if these assumptions are not valid the results of these studies may be biased. We conducted a systematic review of published TB transmission models, to assess the validity of assumptions about progression to active disease following initial infection (PROSPERO ID CRD42016030009). We searched PubMed, Web of Science, Embase, Biosis, and Cochrane Library, and included studies from the earliest available date (1962) to August 31st 2017. We identified 312 studies that met inclusion criteria. Predicted TB incidence varied widely across studies for each risk factor investigated. For population groups with no individual risk factors, annual incidence varied by several orders of magnitude, and 20-year cumulative incidence ranged from close to 0% to 100%. A substantial fraction of modelled results were inconsistent with empirical evidencefor 10-year cumulative incidence 40% of modelled results were more than double or less than half the empirical estimates. These results demonstrate substantial disagreement between modelling studies on a central feature of TB natural history. Greater attention to reproducing known features of TB epidemiology would strengthen future TB modelling studies, and readers of modelling studies are recommended to assess how well those studies demonstrate their validity.
N Menzies, E Wolf, D Connors, M Bellerose, A Sbarra, T Cohen, A Hill, R Yaesoubi, K Galer, PJ White, I Abubakar, J Salomon. (2017). Progression from latent infection to active disease in dynamic TB transmission models: a systematic review of the validity of modelling assumptions. Lancet Infectious Diseases, 18(8)