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Selecting pharmacies for COVID-19 testing to ensure access

Author

Listed:
  • Simon Risanger

    (Norwegian University of Science and Technology)

  • Bismark Singh

    (Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • David Morton

    (Northwestern University)

  • Lauren Ancel Meyers

    (The University of Texas at Austin)

Abstract

Rapid diagnostic testing for COVID-19 is key to guiding social distancing orders and containing emerging disease clusters by contact tracing and isolation. However, communities throughout the US do not yet have adequate access to tests. Pharmacies are already engaged in testing, but there is capacity to greatly increase coverage. Using a facility location optimization model and willingness-to-travel estimates from US National Household Travel Survey data, we find that if COVID-19 testing became available in all US pharmacies, an estimated 94% of the US population would be willing to travel to obtain a test, if warranted. Whereas the largest chain provides high coverage in densely populated states, like Massachusetts, Rhode Island, New Jersey, and Connecticut, independent pharmacies would be required for sufficient coverage in Montana, South Dakota, and Wyoming. If only 1,000 ZIP code areas for pharmacies in the US are selected to provide testing, judicious selection, using our optimization model, provides estimated access to 29 million more people than selecting pharmacies simply based on population density.

Suggested Citation

  • Simon Risanger & Bismark Singh & David Morton & Lauren Ancel Meyers, 2021. "Selecting pharmacies for COVID-19 testing to ensure access," Health Care Management Science, Springer, vol. 24(2), pages 330-338, June.
    • Handle: RePEc:kap:hcarem:v:24:y:2021:i:2:d:10.1007_s10729-020-09538-w
    DOI: 10.1007/s10729-020-09538-w
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Pingting Zhu & Meiyan Qian & Qiwei Wu & Xinyi Liu, 2022. "Challenges Faced in Large-Scale Nucleic Acid Testing during the Sudden Outbreak of the B.1.617.2 (Delta)," IJERPH, MDPI, vol. 19(3), pages 1-16, January.
    3. Robin L. Dillon & Vicki M. Bier & Richard Sheffield John & Abdullah Althenayyan, 2023. "Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic," Decision Analysis, INFORMS, vol. 20(2), pages 109-132, June.
    4. Yashoda Devi & Sabyasachi Patra & Surya Prakash Singh, 2022. "A location-allocation model for influenza pandemic outbreaks: A case study in India," Operations Management Research, Springer, vol. 15(1), pages 487-502, June.
    5. 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.

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