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An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system

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Listed:
  • Daniel Chertok
  • Chad Konchak
  • Nirav Shah
  • Kamaljit Singh
  • Loretta Au
  • Jared Hammernik
  • Brian Murray
  • Anthony Solomonides
  • Ernest Wang
  • Lakshmi Halasyamani

Abstract

An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible.

Suggested Citation

  • Daniel Chertok & Chad Konchak & Nirav Shah & Kamaljit Singh & Loretta Au & Jared Hammernik & Brian Murray & Anthony Solomonides & Ernest Wang & Lakshmi Halasyamani, 2021. "An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-21, October.
    • Handle: RePEc:plo:pone00:0258710
    DOI: 10.1371/journal.pone.0258710
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    References listed on IDEAS

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    1. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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