Mathematical Modeling of “Chronic” Infectious Diseases: Unpacking the Black Box

BACKGROUND: Mathematical models are increasingly used to understand the dynamics of infectious diseases, including “chronic” infections with long generation times. Such models include features that are obscure to most clinicians and decision-makers. METHODS: Using a model of a hypothetical active case-finding intervention for tuberculosis in India as an example, we illustrate the effects on model results of different choices for model structure, input parameters, and calibration process. RESULTS: Using the same underlying data, different transmission models produced different estimates of the projected intervention impact on tuberculosis incidence by 2030 with different corresponding uncertainty ranges. We illustrate the reasons for these differences and present a simple guide for clinicians and decision-makers to evaluate models of infectious diseases. CONCLUSIONS: Mathematical models of chronic infectious diseases must be understood to properly inform policy decisions. Improved communication between modelers and consumers is critical if model results are to improve the health of populations.