Citation
Loo, Sara L.; Howerton, Emily; Contamin, Lucie; Smith, Claire P.; Borchering, Rebecca K.; Mullany, Luke C.; Bents, Samantha; Carcelen, Erica; Jung, Sung-mok; & Bogich, Tiffany, et al. (Preprint). The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering Long-Term Projections to Guide Policy. Epidemics.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 efforts 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 SMHwas 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.URL
https://doi.org/10.1016/j.epidem.2023.100738Reference Type
Journal ArticleYear Published
PreprintJournal Title
EpidemicsAuthor(s)
Loo, Sara L.Howerton, Emily
Contamin, Lucie
Smith, Claire P.
Borchering, Rebecca K.
Mullany, Luke C.
Bents, Samantha
Carcelen, Erica
Jung, Sung-mok
Bogich, Tiffany
van Panhuis, Willem G.
Kerr, Jessica
Espino, Jessi
Yan, Katie
Hochheiser, Harry
Runge, Michael C.
Shea, Katriona
Lessler, Justin
Viboud, Cécile
Truelove, Shaun