The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy

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Creator
  • Loo S.L.
    • Other Affiliation: Johns Hopkins Bloomberg School of Public Health
  • Howerton E.
    • Other Affiliation: The Pennsylvania State University
  • Contamin L.
    • Other Affiliation: University of Pittsburgh
  • Smith C.P.
    • Other Affiliation: Johns Hopkins Bloomberg School of Public Health
  • Borchering R.K.
    • Other Affiliation: The Pennsylvania State University
  • Mullany L.C.
    • Other Affiliation: Johns Hopkins University Applied Physics Laboratory
  • Bents S.
    • Other Affiliation: National Institutes of Health
  • Carcelen E.
    • Other Affiliation: Johns Hopkins Bloomberg School of Public Health
  • Jung S.-M.
    • Affiliation: Carolina Population Center
  • Bogich T.
    • Other Affiliation: The Pennsylvania State University
  • van Panhuis W.G.
    • Other Affiliation: University of Pittsburgh
  • Kerr J.
    • Other Affiliation: University of Pittsburgh
  • Espino J.
    • Other Affiliation: University of Pittsburgh
  • Yan K.
    • Other Affiliation: The Pennsylvania State University
  • Hochheiser H.
    • Other Affiliation: University of Pittsburgh
  • Runge M.C.
    • Other Affiliation: US Geological Survey
  • Shea K.
    • Other Affiliation: The Pennsylvania State University
  • Lessler J.
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
    • Other Affiliation: Johns Hopkins Bloomberg School of Public Health
  • Viboud C.
    • Other Affiliation: National Institutes of Health
  • Truelove S.
    • Other Affiliation: Johns Hopkins Bloomberg School of Public Health
Abstract
  • Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022–23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.
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  • Article
Rights statement
  • In Copyright
License
  • Attribution-NonCommercial-NoDerivs 4.0 International
Journal title
  • Epidemics
Journal volume
  • 46
Language
  • English
Version
  • Publisher
Funder
  • National Institute of General Medical Sciences, NIGMS, (U24GM132013)
  • Council of State and Territorial Epidemiologists, CSTE
  • Eberly College of Science Barbara McClintock Science Achievement
  • Huck Institutes of the Life Sciences
  • Pennsylvania State University, PSU
  • National Science Foundation, NSF, (DEB-2028301, DEB-2037885, DEB-2126278, DEB-2220903)
  • U.S. Department of Health and Human Services, HHS, (200-2016-91781, N00024-13-D-6400)
  • Safety and Healthcare Epidemiology Prevention Research Development
  • Centers for Disease Control and Prevention, CDC
  • National Institutes of Health, NIH
ISSN
  • 1755-4365
Publisher
  • Elsevier B.V.

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