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Ensemble Forecast Modeling for the Design of COVID-19 Vaccine Efficacy Trials

  • Dean, Natalie E.1
  • Pastore y Piontti, Ana2
  • Madewell, Zachary J.1
  • Cummings, Derek A.T3
  • Hitchings, Matthew D.T.3
  • Joshi, Keya4
  • Kahn, Rebecca4
  • Vespignani, Alessandro2
  • Elizabeth Halloran, M.5, 6
  • Longini, Ira M. Jr.1
  • 1 Department of Biostatistics, University of Florida, Gainesville, FL
  • 2 Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA
  • 3 Department of Biology, University of Florida, Gainesville, FL
  • 4 Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
  • 5 Fred Hutchinson Cancer Research Center, Seattle, WA
  • 6 Department of Biostatistics, University of Washington, Seattle, WA
Published Article
Elsevier Ltd.
Publication Date
Sep 15, 2020
DOI: 10.1016/j.vaccine.2020.09.031
PMID: 33012602
PMCID: PMC7492005
PubMed Central


To rapidly evaluate the safety and efficacy of COVID-19 vaccine candidates, prioritizing vaccine trial sites in areas with high expected disease incidence can speed endpoint accrual and shorten trial duration. Mathematical and statistical forecast models can inform the process of site selection, integrating available data sources and facilitating comparisons across locations. We recommend the use of ensemble forecast modeling – combining projections from independent modeling groups – to guide investigators identifying suitable sites for COVID-19 vaccine efficacy trials. We describe an appropriate structure for this process, including minimum requirements, suggested output, and a user-friendly tool for displaying results. Importantly, we advise that this process be repeated regularly throughout the trial, to inform decisions about enrolling new participants at existing sites with waning incidence versus adding entirely new sites. These types of data-driven models can support the implementation of flexible efficacy trials tailored to the outbreak setting.

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