Ensemble forecast modeling for the design of COVID-19 vaccine efficacy trials. Issue 46 (27th October 2020)

• Record Type: Journal Article
• Authors: Dean, Natalie E.; Pastore y Piontti, Ana; Madewell, Zachary J.; Cummings, Derek A.T; Hitchings, Matthew D.T.; Joshi, Keya; Kahn, Rebecca; Vespignani, Alessandro; Halloran, M. Elizabeth; Longini, Ira M.
• Is Part Of: Vaccine. Volume 38:Issue 46(2020)
• Page Start: 7213
• Page End: 7216
• Publication Date: 2020-10-27
• Subjects: Efficacy trial -- Trial planning -- Forecast model -- Ensemble modeling; Vaccines -- Periodicals; 615.372
• DOI: 10.1016/j.vaccine.2020.09.031
• Languages: English
• ISSNs: 0264-410X

Abstract

Abstract: 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.