Individual-based epidemiological models support the study of fine-grained preventive measures, such as tailored vaccine allocation policies, in silico. As individual-based models are computationally intensive, it is pivotal to identify optimal strategies within a reasonable computational budget. Moreover, due to the high societal impact associated with the implementation of preventive strategies, uncertainty regarding decisions should be communicated to policy makers, which is naturally embedded in a Bayesian approach. We present a novel technique for evaluating vaccine allocation strategies using a multi-armed bandit framework in combination with a Bayesian anytime $m$-top exploration algorithm. $m$-top exploration allows the algorithm to learn $m$ policies for which it expects the highest utility, enabling experts to inspect this small set of alternative strategies, along with their quantified uncertainty. The anytime component provides policy advisors with flexibility regarding the computation time and the desired confidence, which is important as it is difficult to make this trade-off beforehand. We consider the Belgian COVID-19 epidemic using the individual-based model STRIDE, where we learn a set of vaccination policies that minimize the number of infections and hospitalisations. Through experiments we show that our method can efficiently identify the $m$-top policies, which is validated in a scenario where the ground truth is available. Finally, we explore how vaccination policies can best be organised under different contact reduction schemes. Through these experiments, we show that the top policies follow a clear trend regarding the prioritised age groups and assigned vaccine type, which provides insights for future vaccination campaigns.

翻译：以个人为基础的流行病学模型有助于研究精细预防性措施,例如专门设计的疫苗分配政策。随着个人模型的计算密集,在合理的计算预算范围内确定最佳战略至关重要。此外,由于执行预防性战略的社会影响很大,决策方面的不确定性应当传达给决策者,这自然地体现在巴伊西亚办法中。我们提出了一种新颖的技术,用于评价疫苗分配战略,利用多武装的土匪框架,与巴伊西亚人随时以美元计价的探险算法,同时使用多武装的土匪框架。 美元顶级勘探使算法能够学习其期望最高效用的百万美元政策,使专家能够检查这组小的替代战略及其量化的不确定性。随时提供政策顾问在计算时间和期望的信心方面具有灵活性,这一点很重要,因为事先实现这种贸易是困难的。我们用个人模型STRIDE对比利时的COVID-19流行病进行了评估,我们在那里学习了一套疫苗接种政策,最大限度地减少感染和住院人数。我们通过实验,能够检查这组检查这组的疫苗计划,我们最终能够从何种方法上展示一种最精确的疫苗试验。我们是如何在何种方法下确定一种最精确的接触。