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
evidence—for 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.