Ensemble forecast modeling for the design of COVID-19 vaccine efficacy trials
Ensemble forecast modeling for the design of COVID-19 vaccine efficacy trials
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DOI：
10.1016/j.vaccine.2020.09.031
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发表时间：
2020-10-27
期刊：
影响因子：
5.5
通讯作者：
Longini, Ira M., Jr.
中科院分区：
文献类型：
--
作者：
Dean, Natalie E.;Piontti, Ana Pastore Y.;Longini, Ira M., Jr.
为了快速评估COVID-19候选疫苗的安全性和有效性，优先考虑在预期疾病发病率高的地区进行疫苗试验，可以加快终点累积并缩短试验持续时间。数学和统计预测模型可以为选址过程提供信息，整合现有数据源，并促进不同地点的比较。我们建议使用集合预测建模--结合独立建模小组的预测--来指导研究人员确定COVID-19疫苗有效性试验的合适地点。我们描述了一个适当的结构，这个过程中，包括最低要求，建议的输出，并显示结果的用户友好的工具。重要的是，我们建议在整个试验过程中定期重复这一过程，以告知在现有研究中心招募新受试者的决定，这些研究中心的发病率正在下降，而不是增加全新的研究中心。这些类型的数据驱动模型可以支持实施针对疫情环境的灵活有效性试验。(C)2020爱思唯尔有限公司版权所有。
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. (C) 2020 Elsevier Ltd. All rights reserved.