Understanding and Assessing Quantitative Modelling Research

Publication Summary

This document provides an overview of how to critically assess a research article which uses quantitative, data-driven mathematical modelling to examine infectious disease transmission. Included is a Quick Reference Guide which aligns with the process of quantitative model development and the format of research articles and is meant to assist in a critical review of the research.

Additional resources on mathematical modelling for public health, including the Comprehensive Glossary for Infectious Disease Modelling, can be found in Appendix: Supporting Resources. This document does not describe qualitativemodelling methods or how to interpret the results of qualitative modelling studies. It also does not attempt to rank mathematical models or research articles. It supports a critical review of modelling research studies by providing an overview of how model analyses and outcomes
can be useful for public health.

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.