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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Article number100738
StatePublished - Mar 2024

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Parasitology
  • Microbiology
  • Public Health, Environmental and Occupational Health
  • Infectious Diseases
  • Virology

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