The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy
Sara L. Loo,
Emily Howerton,
Lucie Contamin,
Claire P. Smith,
Rebecca K. Borchering,
Luke C. Mullany,
Samantha Bents,
Erica Carcelen,
Sung-mok Jung,
Tiffany Bogich,
Willem G. van Panhuis,
Jessica Kerr,
Jessi Espino,
Katie Yan,
Harry Hochheiser,
Michael C. Runge,
Katriona Shea,
Justin Lessler,
Cécile Viboud,
Shaun Truelove
Affiliations
Sara L. Loo
Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA; Corresponding author at: Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Emily Howerton
Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
Lucie Contamin
Public Health Dynamics Lab, University of Pittsburgh, Pittsburgh, PA, USA
Claire P. Smith
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Rebecca K. Borchering
Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
Luke C. Mullany
Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
Samantha Bents
Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
Erica Carcelen
Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA
Sung-mok Jung
UNC Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Tiffany Bogich
Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
Willem G. van Panhuis
Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
Jessica Kerr
Public Health Dynamics Lab, University of Pittsburgh, Pittsburgh, PA, USA
Jessi Espino
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
Katie Yan
Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
Harry Hochheiser
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
Michael C. Runge
Eastern Ecological Science Center at the Patuxent Research Refuge, US Geological Survey, Laurel, MD, USA
Katriona Shea
Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
Justin Lessler
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; UNC Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Cécile Viboud
Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
Shaun Truelove
Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA
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.
