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Mitigation Strategies for COVID-19: Lessons from the K-SEIR Model Calibrated to the Observable Data

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  • Alexander Lipton

    (The Jerusalem School of Business Administration, The Hebrew University of Jerusalem, Jerusalem 91120, Israel
    Connection Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA)

  • Marcos Lopez de Prado

    (Department of Operations Research and Information Engineering, Cornell University, New York, NY 14850, USA)

Abstract

This article develops a detailed epidemiological multi-factor model, the K-susceptible–exposed–infected–removed (K-SEIR) model, and several simpler sub-models as its building blocks. The general model enables us to account for all the relevant COVID-19 features, its disparate impact on different population groups, and interactions within and between the groups. It also includes the availability (or lack thereof) of spare hospital beds and intensive care units (ICU) to accommodate the pent-up demand due to the pandemic. We use the most recent hospitalization and mortality data to calibrate the model. Since our model is multi-factor, we can use it to simulate and analyze the consequences of the sheltering-in-place for each specific group and compare the lives saved and lost due to this measure. We show that in countries with well-developed healthcare systems and a population willing to abide by suitable containment and mitigation procedures, the sheltering in place of the entire community is excessive and harmful when considered holistically. At the same time, sealing nursing homes as thoroughly as possible to avoid high infection and mortality rates is an absolute necessity.

Suggested Citation

  • Alexander Lipton & Marcos Lopez de Prado, 2022. "Mitigation Strategies for COVID-19: Lessons from the K-SEIR Model Calibrated to the Observable Data," JRFM, MDPI, vol. 15(6), pages 1-38, June.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:6:p:248-:d:829733
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    References listed on IDEAS

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    3. Daron Acemoglu & Victor Chernozhukov & Ivàn Werning & Michael D. Whinston, 2020. "A Multi-Risk SIR Model with Optimally Targeted Lockdown," CeMMAP working papers CWP14/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Adams-Prassl, Abi & Boneva, Teodora & Golin, Marta & Rauh, Christopher, 2020. "Inequality in the impact of the coronavirus shock: Evidence from real time surveys," Journal of Public Economics, Elsevier, vol. 189(C).
    5. Condon, Bradly John & Sinha, Tapen, 2010. "Who is that masked person: The use of face masks on Mexico City public transportation during the Influenza A (H1N1) outbreak," Health Policy, Elsevier, vol. 95(1), pages 50-56, April.
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    Cited by:

    1. Matheus R. Grasselli & Alan Kirman & William Hynes, 2022. "Editorial: A Systemic Recovery," JRFM, MDPI, vol. 15(12), pages 1-7, December.

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