We consider the problem of deciding how best to target and prioritize existing vaccines that may offer protection against new variants of an infectious disease. Sequential experiments are a promising approach; however, challenges due to delayed feedback and the overall ebb and flow of disease prevalence make available methods inapplicable for this task. We present a method, partial likelihood Thompson sampling, that can handle these challenges. Our method involves running Thompson sampling with belief updates determined by partial likelihood each time we observe an event. To test our approach, we ran a semi-synthetic experiment based on 200 days of COVID-19 infection data in the US.

翻译：我们考虑了决定如何最佳地针对和确定现有疫苗的优先次序的问题,这些疫苗可以提供保护,防止传染病的新的变种。 分阶段实验是一种很有希望的方法;然而,由于反馈延迟以及疾病流行的总体起伏和流动而带来的挑战,使这一任务无法适用。 我们提出了一个方法,即部分可能抽取Thompson样本,可以应对这些挑战。我们的方法是进行Thompson抽样,每观察一次事件,就提供部分可能性确定最新的信仰信息。为了测试我们的方法,我们在美国进行了基于200天COVID-19感染数据的半合成实验。