TY - JOUR
T1 - The US COVID-19 and Influenza Scenario Modeling Hubs
T2 - Delivering long-term projections to guide policy
AU - Loo, Sara L.
AU - Howerton, Emily
AU - Contamin, Lucie
AU - Smith, Claire P.
AU - Borchering, Rebecca K.
AU - Mullany, Luke C.
AU - Bents, Samantha
AU - Carcelen, Erica
AU - Jung, Sung mok
AU - Bogich, Tiffany
AU - van Panhuis, Willem G.
AU - Kerr, Jessica
AU - Espino, Jessi
AU - Yan, Katie
AU - Hochheiser, Harry
AU - Runge, Michael C.
AU - Shea, Katriona
AU - Lessler, Justin
AU - Viboud, Cécile
AU - Truelove, Shaun
N1 - Publisher Copyright:
© 2024
PY - 2024/3
Y1 - 2024/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85182426874&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182426874&partnerID=8YFLogxK
U2 - 10.1016/j.epidem.2023.100738
DO - 10.1016/j.epidem.2023.100738
M3 - Article
C2 - 38184954
AN - SCOPUS:85182426874
SN - 1755-4365
VL - 46
JO - Epidemics
JF - Epidemics
M1 - 100738
ER -