During the early stages of the COVID-19 pandemic, at a time when there was limited knowledge and evidence, many modelling studies were published over a short period of time that presented very different results (1–15). This raised concerns about the quality of the models and results of these studies given the limited knowledge and evidence available at the time (16–18). This guide was written to help public health professionals critically assess infectious disease modelling research for application in public health. It includes considerations and guiding questions about how a disease is modelled and interpreted for real-world settings.
An appropriately structured mathematical model can simulate real-world population health scenarios. For public health planning, this creates possibilities to better understand the factors that can affect interventions and their outcomes, providing information for policy decisions and resource allocation. In the context of infectious disease spread, such factors include the diversity (heterogeneity) and contact patterns in populations; the type, intensity, and effect of interventions; and strategies for prevention, treatment, and elimination. However, mathematical models are limited by how well the causes (etiological), health burden (epidemiological), and clinical aspects of the disease are understood, what is known about interactions within the population of interest, and how these vary over time depending on demogrphic, geographic, and socioeconomic characteristics.
This guide provides a way to assess the rigour and utility of modelling studies without being mired in calculations. It is designed to help readers think critically about the conclusions made by the authors of a modelling study and how the research can be applied to public health action.