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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Lyndon P. James & Joshua A. Salomon & Caroline O. Buckee & Nicolas A. Menzies, 2021. "The Use and Misuse of Mathematical Modeling for Infectious Disease Policymaking: Lessons for the COVID-19 Pandemic," Medical Decision Making, , vol. 41(4), pages 379-385, May.
    • Handle: RePEc:sae:medema:v:41:y:2021:i:4:p:379-385
    DOI: 10.1177/0272989X21990391
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    Citations

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    Cited by:

    1. Michael E. Darden & David Dowdy & Lauren Gardner & Barton H. Hamilton & Karen Kopecky & Melissa Marx & Nicholas W. Papageorge & Daniel Polsky & Kimberly A. Powers & Elizabeth A. Stuart & Matthew V. Za, 2022. "Modeling to inform economy‐wide pandemic policy: Bringing epidemiologists and economists together," Health Economics, John Wiley & Sons, Ltd., vol. 31(7), pages 1291-1295, July.
    2. Juan Guamán & Karen Portilla & Paúl Arias-Muñoz & Gabriel Jácome & Santiago Cabrera & Luis Álvarez & Bolívar Batallas & Hernán Cadena & Juan Carlos García, 2023. "Multivariate Forecasting Model for COVID-19 Spread Based on Possible Scenarios in Ecuador," Mathematics, MDPI, vol. 11(23), pages 1-13, November.
    3. Rocha Filho, T.M. & Moret, M.A. & Chow, C.C. & Phillips, J.C. & Cordeiro, A.J.A. & Scorza, F.A. & Almeida, A.-C.G. & Mendes, J.F.F., 2021. "A data-driven model for COVID-19 pandemic – Evolution of the attack rate and prognosis for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    4. Manuela Alcañiz & Marc Estévez & Miguel Santolino, 2023. ""Unveiling the underlying severity of multiple pandemic indicators"," IREA Working Papers 202312, University of Barcelona, Research Institute of Applied Economics, revised Oct 2023.
    5. Beate Jahn & Sarah Friedrich & Joachim Behnke & Joachim Engel & Ursula Garczarek & Ralf Münnich & Markus Pauly & Adalbert Wilhelm & Olaf Wolkenhauer & Markus Zwick & Uwe Siebert & Tim Friede, 2022. "On the role of data, statistics and decisions in a pandemic," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 349-382, September.
    6. Andres M. Kowalski & Mariela Portesi & Victoria Vampa & Marcelo Losada & Federico Holik, 2022. "Entropy-Based Informational Study of the COVID-19 Series of Data," Mathematics, MDPI, vol. 10(23), pages 1-16, December.

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