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Pandemic lockdown, isolation, and exit policies based on machine learning predictions

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  • Theodoros Evgeniou
  • Mathilde Fekom
  • Anton Ovchinnikov
  • Raphaël Porcher
  • Camille Pouchol
  • Nicolas Vayatis

Abstract

The widespread lockdowns imposed in many countries at the beginning of the COVID‐19 pandemic elevated the importance of research on pandemic management when medical solutions such as vaccines are unavailable. We present a framework that combines a standard epidemiological SEIR (susceptible–exposed–infected–removed) model with an equally standard machine learning classification model for clinical severity risk, defined as an individual's risk of needing intensive care unit (ICU) treatment if infected. Using COVID‐19–related data and estimates for France as of spring 2020, we then simulate isolation and exit policies. Our simulations show that policies considering clinical risk predictions could relax isolation restrictions for millions of the lowest risk population months earlier while consistently abiding by ICU capacity restrictions. Exit policies without risk predictions, meanwhile, would considerably exceed ICU capacity or require the isolation of a substantial portion of population for over a year in order to not overwhelm the medical system. Sensitivity analyses further decompose the impact of various elements of our models on the observed effects. Our work indicates that predictive modeling based on machine learning and artificial intelligence could bring significant value to managing pandemics. Such a strategy, however, requires governments to develop policies and invest in infrastructure to operationalize personalized isolation and exit policies based on risk predictions at scale. This includes health data policies to train predictive models and apply them to all residents, as well as policies for targeted resource allocation to maintain strict isolation for high‐risk individuals.

Suggested Citation

  • Theodoros Evgeniou & Mathilde Fekom & Anton Ovchinnikov & Raphaël Porcher & Camille Pouchol & Nicolas Vayatis, 2023. "Pandemic lockdown, isolation, and exit policies based on machine learning predictions," Production and Operations Management, Production and Operations Management Society, vol. 32(5), pages 1307-1322, May.
  • Handle: RePEc:bla:popmgt:v:32:y:2023:i:5:p:1307-1322
    DOI: 10.1111/poms.13726
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    References listed on IDEAS

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    1. Daron Acemoglu & Victor Chernozhukov & Iván Werning & Michael D. Whinston, 2021. "Optimal Targeted Lockdowns in a Multigroup SIR Model," American Economic Review: Insights, American Economic Association, vol. 3(4), pages 487-502, December.
    2. Edward H. Kaplan, 2020. "OM Forum—COVID-19 Scratch Models to Support Local Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 22(4), pages 645-655, July.
    3. Eduardo Levy Yeyati & Patricio Goldstein & Luca Sartorio, 2021. "Lockdown Fatigue: The Diminishing Effects of Quarantines on the Spread of COVID-19," CID Working Papers 391, Center for International Development at Harvard University.
    4. Patricio Goldstein & Eduardo Levy Yeyati & Luca Sartorio, 2021. "Lockdown fatigue: The diminishing effects of quarantines on the spread of COVID-19," Department of Economics Working Papers wp_gob_2021_01, Universidad Torcuato Di Tella.
    5. John R. Birge & Ozan Candogan & Yiding Feng, 2022. "Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures," Management Science, INFORMS, vol. 68(5), pages 3175-3195, May.
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    Cited by:

    1. Edward Anderson & Sushil Gupta & Nitin Joglekar & Martin Starr, 2023. "Managing pandemics: A POM perspective and directions for future research," Production and Operations Management, Production and Operations Management Society, vol. 32(5), pages 1295-1306, May.

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