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OM Forum—COVID-19 Scratch Models to Support Local Decisions

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  • Edward H. Kaplan

    (Yale School of Management, Yale School of Public Health, Yale School of Engineering and Applied Science, Yale University, New Haven, Connecticut 06511)

Abstract

This article is based on modeling studies conducted in response to requests from Yale University, the Yale New Haven Hospital, and the State of Connecticut during the early weeks of the SARS-CoV-2 outbreak. Much of this work relied on scratch modeling, that is, models created from scratch in real time. Applications included recommending event crowd-size restrictions, hospital surge planning, timing decisions (when to stop and possibly restart university activities), and scenario analyses to assess the impacts of alternative interventions, among other problems. This paper documents the problems faced, models developed, and advice offered during real-time response to the COVID-19 crisis at the local level. Results include a simple formula for the maximum size of an event that ensures no infected persons are present with 99% probability; the determination that existing intensive care unit (ICU) capacity was insufficient for COVID-19 arrivals, which led to creating a large dedicated COVID-19–negative pressure ICU; and a new epidemic model that showed the infeasibility of the university hosting normal spring and summer events, that lockdown-like stay-at-home and social distancing restrictions without additional public health action would only delay transmission and enable a rebound after restrictions are lifted, and that aggressive community screening to rapidly detect and isolate infected persons could end the outbreak.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormsom:v:22:y:2020:i:4:p:645-655
    DOI: 10.1287/msom.2020.0891
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    Cited by:

    1. Zhiyuan Chen & Guangwen Kong, 2023. "Hospital admission, facility‐based isolation, and social distancing: An SEIR model with constrained medical resources," Production and Operations Management, Production and Operations Management Society, vol. 32(5), pages 1397-1414, May.
    2. 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.
    3. Xiaoyan Xu & Suresh P. Sethi & Sai‐Ho Chung & Tsan‐Ming Choi, 2023. "Reforming global supply chain management under pandemics: The GREAT‐3Rs framework," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 524-546, February.
    4. Li, Zhong-Ping & Chang, Aichih (Jasmine) & Zou, Zongbao, 2023. "Design mechanism to coordinate a hierarchical healthcare system: Patient subsidy vs. capacity investment," Omega, Elsevier, vol. 118(C).
    5. Ho‐Yin Mak & Tinglong Dai & Christopher S. Tang, 2022. "Managing two‐dose COVID‐19 vaccine rollouts with limited supply: Operations strategies for distributing time‐sensitive resources," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4424-4442, December.
    6. Choi, Tsan-Ming & Shi, Xiutian, 2022. "Reducing supply risks by supply guarantee deposit payments in the fashion industry in the “new normal after COVID-19”," Omega, Elsevier, vol. 109(C).
    7. Chang, Joseph T. & Kaplan, Edward H., 2023. "Modeling local coronavirus outbreaks," European Journal of Operational Research, Elsevier, vol. 304(1), pages 57-68.
    8. Hashemi, Hossein & Rajabi, Reza & Brashear-Alejandro, Thomas G., 2022. "COVID-19 research in management: An updated bibliometric analysis," Journal of Business Research, Elsevier, vol. 149(C), pages 795-810.
    9. Luyi Yang & Shiliang Cui & Zhongbin Wang, 2022. "Design of Covid‐19 testing queues," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2204-2221, May.
    10. Vahdani, Behnam & Mohammadi, Mehrdad & Thevenin, Simon & Meyer, Patrick & Dolgui, Alexandre, 2023. "Production-sharing of critical resources with dynamic demand under pandemic situation: The COVID-19 pandemic," Omega, Elsevier, vol. 120(C).
    11. Mohammad Ebrahim Arbabian & Hossein Rikhtehgar Berenji, 2023. "Inventory systems with uncertain supplier capacity: an application to covid-19 testing," Operations Management Research, Springer, vol. 16(1), pages 324-344, March.
    12. Lester Blackmon & Ross Chan & Omar Carbral & Geeta Chintapally & Sandip Dhara & Peter Felix & Aditi Jagdish & Srini Konakalla & Jasbir Labana & Jeff McIlvain & Jason Stone & Christopher S. Tang & Jaso, 2021. "Rapid Development of a Decision Support System to Alleviate Food Insecurity at the Los Angeles Regional Food Bank amid the COVID‐19 Pandemic," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3391-3407, October.
    13. Kang Kang & Sherwin Doroudi & Mohammad Delasay & Alexander Wickeham, 2023. "A queueing‐theoretic framework for evaluating transmission risks in service facilities during a pandemic," Production and Operations Management, Production and Operations Management Society, vol. 32(5), pages 1453-1470, May.
    14. Ram Bala & Kumar R. Sarangee & Shuhan He & Grace Jin, 2022. "Get Us PPE: A Self-Organizing Platform Ecosystem for Supply Chain Optimization during COVID-19," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
    15. Fouad El Ouardighi & Eugene Khmelnitsky & Suresh P. Sethi, 2022. "Epidemic control with endogenous treatment capability under popular discontent and social fatigue," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1734-1752, April.

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