Coordinated strategy for a model-based decision support tool for coronavirus disease, Utah, USA

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Creator
  • Meredith, H.R
    • Other Affiliation: Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
  • Arehart, E
    • Other Affiliation: University of Utah, Salt Lake City, UT, United States
  • Grantz, K.H
    • Other Affiliation: Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
  • Beams, A
    • Other Affiliation: University of Utah, Salt Lake City, UT, United States
  • Sheets, T
    • Other Affiliation: University of Utah, Salt Lake City, UT, United States
  • Nelson, R.
  • Zhang, Y
    • Other Affiliation: University of Utah, Salt Lake City, UT, United States
  • Vinik, R.G
    • Other Affiliation: University of Utah, Salt Lake City, UT, United States
  • Barfuss, D
    • Other Affiliation: University of Utah, Salt Lake City, UT, United States
  • Pettit, J.C
    • Other Affiliation: University of Utah, Salt Lake City, UT, United States
  • McCaffrey, K
    • Other Affiliation: Utah Department of Health, Salt Lake City, United States
  • Dunn, A.C
    • Other Affiliation: Utah Department of Health, Salt Lake City, United States
  • Good, M
    • Other Affiliation: University of Utah, Salt Lake City, UT, United States
  • Frattaroli, S
    • Other Affiliation: Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
  • Samore, M.H
    • Other Affiliation: University of Utah, Salt Lake City, UT, United States
  • Lessler, J
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
    • Other Affiliation: Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
  • Lee, E.C
    • Other Affiliation: Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
  • Keegan, L.T
    • Other Affiliation: University of Utah, Salt Lake City, UT, United States
Abstract
  • The coronavirus disease pandemic has highlighted the key role epidemiologic models play in supporting public health decision-making. In particular, these models provide estimates of outbreak potential when data are scarce and decision-making is critical and urgent. We document the integrated modeling response used in the US state of Utah early in the coronavirus disease pandemic, which brought together a diverse set of technical experts and public health and healthcare officials and led to an evidence-based response to the pandemic. We describe how we adapted a standard epidemiologic model; harmonized the outputs across modeling groups; and maintained a constant dialogue with policymakers at multiple levels of government to produce timely, evidence-based, and coordinated public health recommendations and interventions during the first wave of the pandemic. This framework continues to support the state's response to ongoing outbreaks and can be applied in other settings to address unique public health challenges.
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DOI
Identifier
Resource type
  • Article
Journal title
  • Emerging Infectious Diseases
Journal volume
  • 27
Journal issue
  • 5
Page start
  • 1259
Page end
  • 1265
Language
  • English
Version
  • Publisher
Funder
  • Amazon Web Services, AWS
  • 26798
  • U.S. Department of Homeland Security, DHS
  • Centers for Disease Control and Prevention, CDC: 5U01CK000538-03, 5U01CK000585-02
ISSN
  • 1080-6040
Publisher
  • Centers for Disease Control and Prevention (CDC)

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