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Validity and usefulness of COVID-19 models

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  • Sibel Eker

    (International Institute for Applied Systems Analysis (IIASA))

Abstract

Mathematical models have become central to the public and policy debate about the recent COVID-19 pandemic. On the one hand, they provide guidance to policy-makers about the development of the epidemic and healthcare demand overtime; on the other hand, they are heavily criticized for their lack of credibility. This commentary reflects on three such models from a validity and usefulness perspective. Specifically, it discusses the complexity, validation, and communication of models informing the government decisions in the UK, US and Austria, and concludes that, although these models are useful in many ways, they currently lack a thorough validation and a clear communication of their uncertainties. Therefore, prediction claims of these models should be taken cautiously, and their merits on scenario analysis should be the basis for decision-making. The lessons that can be learned from the COVID models in terms of the communication of uncertainties and assumptions can guide the use of quantitative models in other policy-making areas.

Suggested Citation

  • Sibel Eker, 2020. "Validity and usefulness of COVID-19 models," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-5, December.
    • Handle: RePEc:pal:palcom:v:7:y:2020:i:1:d:10.1057_s41599-020-00553-4
    DOI: 10.1057/s41599-020-00553-4
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    References listed on IDEAS

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    1. James S. Hodges, 1991. "Six (Or So) Things You Can Do with a Bad Model," Operations Research, INFORMS, vol. 39(3), pages 355-365, June.
    2. Sibel Eker & Elena Rovenskaya & Michael Obersteiner & Simon Langan, 2018. "Practice and perspectives in the validation of resource management models," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    3. Andrea Saltelli, 2019. "A short comment on statistical versus mathematical modelling," Nature Communications, Nature, vol. 10(1), pages 1-3, December.
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    Cited by:

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    2. Shirley Gee Hoon Tang & Muhamad Haziq Hasnul Hadi & Siti Rosilah Arsad & Pin Jern Ker & Santhi Ramanathan & Nayli Aliah Mohd Afandi & Madihah Mohd Afzal & Mei Wyin Yaw & Prajindra Sankar Krishnan & Ch, 2022. "Prerequisite for COVID-19 Prediction: A Review on Factors Affecting the Infection Rate," IJERPH, MDPI, vol. 19(20), pages 1-38, October.
    3. Noor Alkhateeb & Farag Sallabi & Saad Harous & Mamoun Awad, 2022. "A Study on Predicting the Outbreak of COVID-19 in the United Arab Emirates: A Monte Carlo Simulation Approach," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    4. Rastko Jovanović & Miloš Davidović & Ivan Lazović & Maja Jovanović & Milena Jovašević-Stojanović, 2021. "Modelling Voluntary General Population Vaccination Strategies during COVID-19 Outbreak: Influence of Disease Prevalence," IJERPH, MDPI, vol. 18(12), pages 1-18, June.
    5. Gabriel Recchia & Alexandra L J Freeman & David Spiegelhalter, 2021. "How well did experts and laypeople forecast the size of the COVID-19 pandemic?," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-16, May.
    6. Albert Zeyer, 2022. "Teaching Two-Eyed Seeing in Education for Sustainable Development: Inspirations from the Science|Environment|Health Pedagogy in Pandemic Times," Sustainability, MDPI, vol. 14(10), pages 1-12, May.
    7. Jessica Weinkle, 2022. "An evaluation of North Carolina science advice on COVID-19 pandemic response," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-16, December.

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