IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v24y2021i2d10.1007_s10729-020-09526-0.html
   My bibliography  Save this article

Repeat SARS-CoV-2 testing models for residential college populations

Author

Listed:
  • Joseph T. Chang

    (Yale University)

  • Forrest W. Crawford

    (Yale School of Public Health)

  • Edward H. Kaplan

    (Yale School of Management, Yale School of Public Health, Yale School of Engineering and Applied Science)

Abstract

Residential colleges are considering re-opening under uncertain futures regarding the COVID-19 pandemic. We consider repeat SARS-CoV-2 testing models for the purpose of containing outbreaks in the residential campus community. The goal of repeat testing is to detect and isolate new infections rapidly to block transmission that would otherwise occur both on and off campus. The models allow for specification of aspects including scheduled on-campus resident screening at a given frequency, test sensitivity that can depend on the time since infection, imported infections from off campus throughout the school term, and a lag from testing until student isolation due to laboratory turnaround and student relocation delay. For early- (late-) transmission of SARS-CoV-2 by age of infection, we find that weekly screening cannot reliably contain outbreaks with reproductive numbers above 1.4 (1.6) if more than one imported exposure per 10,000 students occurs daily. Screening every three days can contain outbreaks providing the reproductive number remains below 1.75 (2.3) if transmission happens earlier (later) with time from infection, but at the cost of increased false positive rates requiring more isolation quarters for students testing positive. Testing frequently while minimizing the delay from testing until isolation for those found positive are the most controllable levers for preventing large residential college outbreaks. A web app that implements model calculations is available to facilitate exploration and consideration of a variety of scenarios.

Suggested Citation

  • Joseph T. Chang & Forrest W. Crawford & Edward H. Kaplan, 2021. "Repeat SARS-CoV-2 testing models for residential college populations," Health Care Management Science, Springer, vol. 24(2), pages 305-318, June.
  • Handle: RePEc:kap:hcarem:v:24:y:2021:i:2:d:10.1007_s10729-020-09526-0
    DOI: 10.1007/s10729-020-09526-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-020-09526-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10729-020-09526-0?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Edward H. Kaplan, 2020. "Containing 2019-nCoV (Wuhan) coronavirus," Health Care Management Science, Springer, vol. 23(3), pages 311-314, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Alec Morton & Ebru Bish & Itamar Megiddo & Weifen Zhuang & Roberto Aringhieri & Sally Brailsford & Sarang Deo & Na Geng & Julie Higle & David Hutton & Mart Janssen & Edward H Kaplan & Jianbin Li & Món, 2021. "Introduction to the special issue: Management Science in the Fight Against Covid-19," Health Care Management Science, Springer, vol. 24(2), pages 251-252, June.
    2. Holly Blake & Sarah Somerset & Ikra Mahmood & Neelam Mahmood & Jessica Corner & Jonathan K. Ball & Chris Denning, 2022. "A Qualitative Evaluation of the Barriers and Enablers for Implementation of an Asymptomatic SARS-CoV-2 Testing Service at the University of Nottingham: A Multi-Site Higher Education Setting in England," IJERPH, MDPI, vol. 19(20), pages 1-17, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    2. Gillis, Melissa & Urban, Ryley & Saif, Ahmed & Kamal, Noreen & Murphy, Matthew, 2021. "A simulation–optimization framework for optimizing response strategies to epidemics," Operations Research Perspectives, Elsevier, vol. 8(C).
    3. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2024. "Testing, Voluntary Social Distancing, and the Spread of an Infection," Operations Research, INFORMS, vol. 72(2), pages 533-548, March.
    4. Eugenio F. Sánchez-Úbeda & Pedro Sánchez-Martín & Macarena Torrego-Ellacuría & Ángel Del Rey-Mejías & Manuel F. Morales-Contreras & José-Luis Puerta, 2021. "Flexibility and Bed Margins of the Community of Madrid’s Hospitals during the First Wave of the SARS-CoV-2 Pandemic," IJERPH, MDPI, vol. 18(7), pages 1-22, March.
    5. Edward H. Kaplan & Dennis Wang & Mike Wang & Amyn A. Malik & Alessandro Zulli & Jordan Peccia, 2021. "Aligning SARS-CoV-2 indicators via an epidemic model: application to hospital admissions and RNA detection in sewage sludge," Health Care Management Science, Springer, vol. 24(2), pages 320-329, June.
    6. Anna Nagurney & Pritha Dutta, 2021. "A Multiclass, Multiproduct Covid-19 Convalescent Plasma Donor Equilibrium Model," SN Operations Research Forum, Springer, vol. 2(3), pages 1-30, September.
    7. Atul Pokharel & Robert Soulé & Avi Silberschatz, 2021. "A case for location based contact tracing," Health Care Management Science, Springer, vol. 24(2), pages 420-438, June.
    8. Matthias Klumpp & Dominic Loske & Silvio Bicciato, 2022. "COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(8), pages 1263-1285, November.
    9. Sarbast Moslem & Tiziana Campisi & Agnieszka Szmelter-Jarosz & Szabolcs Duleba & Kh Md Nahiduzzaman & Giovanni Tesoriere, 2020. "Best–Worst Method for Modelling Mobility Choice after COVID-19: Evidence from Italy," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    10. Chang, Joseph T. & Kaplan, Edward H., 2023. "Modeling local coronavirus outbreaks," European Journal of Operational Research, Elsevier, vol. 304(1), pages 57-68.

    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:kap:hcarem:v:24:y:2021:i:2:d:10.1007_s10729-020-09526-0. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.