Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty
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Published:2023-11-20
Issue:1
Volume:14
Page:
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ISSN:2041-1723
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Container-title:Nature Communications
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language:en
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Short-container-title:Nat Commun
Author:
Howerton EmilyORCID, Contamin LucieORCID, Mullany Luke C.ORCID, Qin MichelleORCID, Reich Nicholas G., Bents Samantha, Borchering Rebecca K.ORCID, Jung Sung-mokORCID, Loo Sara L.ORCID, Smith Claire P., Levander JohnORCID, Kerr Jessica, Espino J.ORCID, van Panhuis Willem G., Hochheiser HarryORCID, Galanti Marta, Yamana TeresaORCID, Pei SenORCID, Shaman JeffreyORCID, Rainwater-Lovett KaitlinORCID, Kinsey Matt, Tallaksen Kate, Wilson Shelby, Shin Lauren, Lemaitre Joseph C.ORCID, Kaminsky Joshua, Hulse Juan Dent, Lee Elizabeth C.ORCID, McKee Clifton D., Hill AlisonORCID, Karlen Dean, Chinazzi MatteoORCID, Davis Jessica T., Mu Kunpeng, Xiong Xinyue, Pastore y Piontti Ana, Vespignani AlessandroORCID, Rosenstrom Erik T.ORCID, Ivy Julie S., Mayorga Maria E., Swann Julie L., España Guido, Cavany Sean, Moore SeanORCID, Perkins AlexORCID, Hladish Thomas, Pillai Alexander, Ben Toh Kok, Longini Ira, Chen ShiORCID, Paul Rajib, Janies Daniel, Thill Jean-Claude, Bouchnita Anass, Bi KaimingORCID, Lachmann Michael, Fox Spencer J.ORCID, Meyers Lauren Ancel, Srivastava AjiteshORCID, Porebski PrzemyslawORCID, Venkatramanan SriniORCID, Adiga Aniruddha, Lewis BryanORCID, Klahn BrianORCID, Outten Joseph, Hurt BenjaminORCID, Chen Jiangzhuo, Mortveit Henning, Wilson AmandaORCID, Marathe Madhav, Hoops StefanORCID, Bhattacharya Parantapa, Machi Dustin, Cadwell Betsy L., Healy Jessica M.ORCID, Slayton Rachel B., Johansson Michael A.ORCID, Biggerstaff MatthewORCID, Truelove ShaunORCID, Runge Michael C.ORCID, Shea KatrionaORCID, Viboud CécileORCID, Lessler JustinORCID
Abstract
AbstractOur 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.
Funder
National Science Foundation The Eberly College of Science Barbara McClintock Science Achievement Graduate Scholarship in Biology is a graduate fellowship funded through the Pennsylvania State University. U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences Council of State and Territorial Epidemiologists Virginia Department of Health
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
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