IDEAS home Printed from https://ideas.repec.org/a/inm/ormsom/v22y2020i4p645-655.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/msom.2020.0891
    Download Restriction: no
    File URL: https://libkey.io/10.1287/msom.2020.0891?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. Chang, Joseph T. & Kaplan, Edward H., 2023. "Modeling local coronavirus outbreaks," European Journal of Operational Research, Elsevier, vol. 304(1), pages 57-68.
    5. 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.
    6. 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.
    7. 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).
    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. 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.
    11. 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.
    12. 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.
    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. 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).
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormsom:v:22:y:2020:i:4:p:645-655. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.