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A Simple Planning Problem for COVID-19 Lockdown

Author

Listed:
  • Fernando Alvarez

    (University of Chicago - Booth School of Business and NBER)

  • David Argente

    (Pennsylvania State University)

Abstract

We study the optimal lockdown policy for a planner who controls the fatalities of a pandemic while minimizing the output costs of the lockdown. The policy depends on the fraction of infected and susceptible in the population, prescribing a severe lockdown beginning two weeks after the outbreak, covering 60% of the population after a month, and gradually withdrawing to 20% of the population after 3 months. The intensity of the optimal lockdown depends on the gradient of the fatality rate with respect to the infected, and the availability of antibody testing that yields a welfare gain of 2% of GDP.

Suggested Citation

  • Fernando Alvarez & David Argente, 2020. "A Simple Planning Problem for COVID-19 Lockdown," Working Papers 2020-34, Becker Friedman Institute for Research In Economics.
    • Handle: RePEc:bfi:wpaper:2020-34
    as

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    References listed on IDEAS

    as
    1. Diamond, P. A. & Maskin, Eric, 1981. "An equilibrium analysis of search and breach of contract II. A non-steady state example," Journal of Economic Theory, Elsevier, vol. 25(2), pages 165-195, October.
    2. Facundo Piguillem & Liyan Shi, 2022. "Optimal Covid-19 Quarantine and Testing Policies [‘Optimal targeted lockdowns in a multigroup sir model’]," The Economic Journal, Royal Economic Society, vol. 132(647), pages 2534-2562.
    3. Robert J. Barro & José F. Ursua & Joanna Weng, 2020. "The Coronavirus and the Great Influenza Epidemic - Lessons from the "Spanish Flu" for the Coronavirus's Potential Effects on Mortality and Economic Activity," CESifo Working Paper Series 8166, CESifo.
    4. repec:aei:rpaper:1008560098 is not listed on IDEAS
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    More about this item

    Keywords

    Dynamic programming; epidemic control; lockdown; Quarantine;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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