The Use and Misuse of Mathematical Modeling for Infectious Disease Policymaking: Lessons for the COVID-19 Pandemic

Lyndon P. James, Joshua A. Salomon, Caroline O. Buckee and Nicolas A. Menzies
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Lyndon P. James: Harvard University, Cambridge, MA, USA
Joshua A. Salomon: Center for Health Policy and Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA
Caroline O. Buckee: Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
Nicolas A. Menzies: Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA

Medical Decision Making, 2021, vol. 41, issue 4, 379-385

Abstract: Mathematical modeling has played a prominent and necessary role in the current coronavirus disease 2019 (COVID-19) pandemic, with an increasing number of models being developed to track and project the spread of the disease, as well as major decisions being made based on the results of these studies. A proliferation of models, often diverging widely in their projections, has been accompanied by criticism of the validity of modeled analyses and uncertainty as to when and to what extent results can be trusted. Drawing on examples from COVID-19 and other infectious diseases of global importance, we review key limitations of mathematical modeling as a tool for interpreting empirical data and informing individual and public decision making. We present several approaches that have been used to strengthen the validity of inferences drawn from these analyses, approaches that will enable better decision making in the current COVID-19 crisis and beyond.

Keywords: COVID-19; infectious diseases; mathematical modeling; uncertainty; validation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:41:y:2021:i:4:p:379-385

DOI: 10.1177/0272989X21990391

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