Ensemble forecast modeling for the design of COVID-19 vaccine efficacy trials

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorDean, Natalie E.-
Autor(es): dc.creatorPastore y Piontti, Ana-
Autor(es): dc.creatorMadewell, Zachary J.-
Autor(es): dc.creatorCummings, Derek A. T.-
Autor(es): dc.creatorHitchings, Matthew D. T.-
Autor(es): dc.creatorJoshi, Keya-
Autor(es): dc.creatorKahn, Rebecca-
Autor(es): dc.creatorVespignani, Alessandro-
Autor(es): dc.creatorHalloran, M. Elizabeth-
Autor(es): dc.creatorLongini, Ira M.-
Data de aceite: dc.date.accessioned2026-02-09T12:49:20Z-
Data de disponibilização: dc.date.available2026-02-09T12:49:20Z-
Data de envio: dc.date.issued2020-10-14-
Data de envio: dc.date.issued2020-10-14-
Data de envio: dc.date.issued2020-10-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/43405-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0264410X20311919-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1168933-
Descrição: dc.descriptionTo 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.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceVaccine-
Palavras-chave: dc.subjectEfficacy trial-
Palavras-chave: dc.subjectTrial planning-
Palavras-chave: dc.subjectForecast model-
Palavras-chave: dc.subjectEnsemble modeling-
Título: dc.titleEnsemble forecast modeling for the design of COVID-19 vaccine efficacy trials-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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