Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty

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  • Howerton E.
    • Other Affiliation: The Pennsylvania State University
  • Contamin L.
    • Other Affiliation: University of Pittsburgh
  • Mullany L.C.
    • Other Affiliation: Johns Hopkins University Applied Physics Lab
  • Qin M.
    • Other Affiliation: Harvard University
  • Reich N.G.
    • Other Affiliation: University of Massachusetts Amherst
  • Bents S.
    • Other Affiliation: National Institutes of Health Fogarty International Center
  • Borchering R.K.
    • Other Affiliation: The Pennsylvania State University
  • Jung S.-M.
    • Affiliation: University of North Carolina at Chapel Hill
  • Loo S.L.
    • Other Affiliation: Johns Hopkins University
  • Smith C.P.
    • Other Affiliation: Johns Hopkins University
  • Levander J.
    • Other Affiliation: University of Pittsburgh
  • Kerr J.
    • Other Affiliation: University of Pittsburgh
  • Espino J.
    • Other Affiliation: University of Pittsburgh
  • van Panhuis W.G.
    • Other Affiliation: National Institute of Allergy and Infectious Diseases
  • Hochheiser H.
    • Other Affiliation: University of Pittsburgh
  • Galanti M.
    • Other Affiliation: Columbia University
  • Yamana T.
    • Other Affiliation: Columbia University
  • Pei S.
    • Other Affiliation: Columbia University
  • Shaman J.
    • Other Affiliation: Columbia University
  • Rainwater-Lovett K.
    • Other Affiliation: Johns Hopkins University Applied Physics Lab
  • Kinsey M.
    • Other Affiliation: Johns Hopkins University Applied Physics Lab
  • Tallaksen K.
    • Other Affiliation: Johns Hopkins University Applied Physics Lab
  • Wilson S.
    • Other Affiliation: Johns Hopkins University Applied Physics Lab
  • Shin L.
    • Other Affiliation: Johns Hopkins University Applied Physics Lab
  • Lemaitre J.C.
    • Affiliation: University of North Carolina at Chapel Hill
  • Kaminsky J.
    • Other Affiliation: Johns Hopkins University
  • Hulse J.D.
    • Other Affiliation: Johns Hopkins University
  • Lee E.C.
    • Other Affiliation: Johns Hopkins University
  • McKee C.D.
    • Other Affiliation: Johns Hopkins University
  • Hill A.
    • Other Affiliation: Johns Hopkins University
  • Karlen D.
    • Other Affiliation: University of Victoria
  • Chinazzi M.
    • Other Affiliation: Northeastern University
  • Davis J.T.
    • Other Affiliation: Northeastern University
  • Mu K.
    • Other Affiliation: Northeastern University
  • Xiong X.
    • Other Affiliation: Northeastern University
  • Pastore y Piontti A.
    • Other Affiliation: Northeastern University
  • Vespignani A.
    • Other Affiliation: Northeastern University
  • Rosenstrom E.T.
    • Other Affiliation: North Carolina State University
  • Ivy J.S.
    • Other Affiliation: North Carolina State University
  • Mayorga M.E.
    • Other Affiliation: North Carolina State University
  • Swann J.L.
    • Other Affiliation: North Carolina State University
  • España G.
    • Other Affiliation: University of Notre Dame
  • Cavany S.
    • Other Affiliation: University of Notre Dame
  • Moore S.
    • Other Affiliation: University of Notre Dame
  • Perkins A.
    • Other Affiliation: University of Notre Dame
  • Hladish T.
    • Other Affiliation: University of Florida
  • Pillai A.
    • Other Affiliation: University of Florida
  • Ben Toh K.
    • Other Affiliation: Northwestern University
  • Longini I., Jr.
    • Other Affiliation: University of Florida
  • Chen S.
    • Other Affiliation: University of North Carolina at Charlotte
  • Paul R.
    • Other Affiliation: University of North Carolina at Charlotte
  • Janies D.
    • Other Affiliation: University of North Carolina at Charlotte
  • Thill J.-C.
    • Other Affiliation: University of North Carolina at Charlotte
  • Bouchnita A.
    • Other Affiliation: University of Texas at El Paso
  • Bi K.
    • Other Affiliation: University of Texas at Austin
  • Lachmann M.
    • Other Affiliation: Santa Fe Institute
  • Fox S.J.
    • Other Affiliation: University of Georgia
  • Meyers L.A.
    • Other Affiliation: University of Texas at Austin
  • Srivastava A.
    • Other Affiliation: University of Southern California
  • Porebski P.
    • Other Affiliation: University of Virginia
  • Venkatramanan S.
    • Other Affiliation: University of Virginia
  • Adiga A.
    • Other Affiliation: University of Virginia
  • Lewis B.
    • Other Affiliation: University of Virginia
  • Klahn B.
    • Other Affiliation: University of Virginia
  • Outten J.
    • Other Affiliation: University of Virginia
  • Hurt B.
    • Other Affiliation: University of Virginia
  • Chen J.
    • Other Affiliation: University of Virginia
  • Mortveit H.
    • Other Affiliation: University of Virginia
  • Wilson A.
    • Other Affiliation: University of Virginia
  • Marathe M.
    • Other Affiliation: University of Virginia
  • Hoops S.
    • Other Affiliation: University of Virginia
  • Bhattacharya P.
    • Other Affiliation: University of Virginia
  • Machi D.
    • Other Affiliation: University of Virginia
  • Cadwell B.L.
    • Other Affiliation: Centers for Disease Control and Prevention
  • Healy J.M.
    • Other Affiliation: Centers for Disease Control and Prevention
  • Slayton R.B.
    • Other Affiliation: Centers for Disease Control and Prevention
  • Johansson M.A.
    • Other Affiliation: Centers for Disease Control and Prevention
  • Biggerstaff M.
    • Other Affiliation: Centers for Disease Control and Prevention
  • Truelove S.
    • Other Affiliation: Johns Hopkins University
  • Runge M.C.
    • Other Affiliation: U.S. Geological Survey Eastern Ecological Science Center
  • Shea K.
    • Other Affiliation: The Pennsylvania State University
  • Viboud C.
    • Other Affiliation: National Institutes of Health Fogarty International Center
  • Lessler J.
    • Affiliation: University of North Carolina at Chapel Hill
Abstract
  • Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.
Date of publication
DOI
Identifier
Resource type
  • Article
License
  • Attribution 4.0 International
Journal title
  • Nature Communications
Journal volume
  • 14
Journal issue
  • 1
Language
  • English
Version
  • Publisher
Funder
  • National Science Foundation, NSF, (2127976, DEB-2028301, DEB-2037885, DEB-2126278, DEB-2220903)
  • U.S. Department of Homeland Security, DHS
  • Johns Hopkins University, JHU
  • Centers for Disease Control and Prevention, CDC, (200-2016-91781)
  • Council of State and Territorial Epidemiologists, CSTE
  • U.S. Department of Health and Human Services, HHS
  • California Department of Public Health, CDPH
  • National Institutes of Health, NIH, (5R01AI102939, 75D30119C05935, CCF-1918656, CCF-2142997, OAC-1916805, OAC-2027541, R01GM109718, R01GM140564)
  • Amazon Web Services, AWS
  • TRACS, (UL1TR002489)
  • Eberly College of Science Barbara McClintock Science Achievement
  • Pennsylvania State University, PSU, (6U01IP001137, HHS/CDC 5U01IP0001137, NU38OT000297)
  • Defense Threat Reduction Agency, DTRA, (TG-BIO210084)
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF, (200021-172578)
  • National Institute of General Medical Sciences, NIGMS, (5U24GM132013)
  • U.S. Government
ISSN
  • 2041-1723
Publisher
  • Nature Research

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