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.

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时间。数学和统计预测模型可以为选址过程提供信息，整合可用数据源并促进不同地点的比较。我们建议使用集成预测模型——结合独立建模组的预测——来指导研究人员确定 COVID-19 疫苗功效试验的合适地点。我们描述了此过程的适当结构，包括最低要求、建议的输出和用于显示结果的用户友好工具。重要的是，我们建议在整个试验过程中定期重复此过程，告知有关在发病率下降的现有站点招募新参与者与添加全新站点的决策。这些类型的数据驱动模型可以支持实施针对疫情环境量身定制的灵活疗效试验。