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A simulation case study to improve staffing decisions at mass immunization clinics for pandemic influenza

Author

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  • Michael F Beeler

    (University of Toronto, Toronto, ON, Canada)

  • Dionne M Aleman

    (University of Toronto, Toronto, ON, Canada)

  • Michael W Carter

    (University of Toronto, Toronto, ON, Canada)

Abstract

Mass immunization clinics (MICs) are an important component of pandemic influenza control strategies in many jurisdictions. Decisions about staffing levels at MICs affect several factors of concern to public health authorities: total vaccination volume, patient wait-times, operating costs, and intra-facility influenza transmission risk. We present a discrete-event simulation of an MIC to assess how strongly staffing changes affect these factors. The simulation is based on data from Canadian clinics responding to pandemic H1N1 in 2009. This study is the first to model flu transmission risk at an MIC, and the first to relate such risk to staffing decisions. We show that the marginal benefit of adding staff is greatly underestimated if indirect waiting costs and intra-facility infections are not considered.

Suggested Citation

  • Michael F Beeler & Dionne M Aleman & Michael W Carter, 2014. "A simulation case study to improve staffing decisions at mass immunization clinics for pandemic influenza," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(4), pages 497-511, April.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:4:p:497-511
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    Citations

    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Policy responses > Vaccination

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    Cited by:

    1. Ali Asgary & Svetozar Zarko Valtchev & Michael Chen & Mahdi M. Najafabadi & Jianhong Wu, 2020. "Artificial Intelligence Model of Drive-Through Vaccination Simulation," IJERPH, MDPI, vol. 18(1), pages 1-10, December.

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