A key component in controlling the spread of an epidemic is deciding where, when and to whom to apply an intervention. We develop a framework for using data to inform these decisions in realtime. We formalize a treatment allocation strategy as a sequence of functions, one per treatment period, that map up-to-date information on the spread of an infectious disease to a subset of locations where treatment should be allocated. An optimal allocation strategy optimizes some cumulative outcome, e.g. the number of uninfected locations, the geographic footprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategy for an emerging infectious disease is challenging because spatial proximity induces interference between locations, the number of possible allocations is exponential in the number of locations, and because disease dynamics and intervention effectiveness are unknown at out-break. We derive a Bayesian on-line estimator of the optimal allocation strategy that combines simulation-optimization with Thompson sampling. The estimator proposed performs favourably in simulation experiments. This work is motivated by and illustrated using data on the spread of white nose syndrome, which is a highly fatal infectious disease devastating bat populations in North America.

中文翻译：

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在线控制新兴传染病的最佳时空分配方法。

控制流行病传播的关键因素是确定在何处，何时以及向谁进行干预。我们开发了一个框架，用于使用数据实时告知这些决策。我们将治疗分配策略正式化为一系列功能，每个治疗周期使用一个功能，将传染病传播的最新信息映射到应该分配治疗的部分位置。最佳分配策略可优化某些累积结果，例如未感染的地点数，疾病的地理足迹或流行病的成本。估算新出现的传染病的最佳分配策略具有挑战性，因为空间邻近性会在位置之间引起干扰，可能分配的数量在位置数量中呈指数关系，而且由于疫情爆发时疾病动态和干预效果尚不清楚。我们推导了最佳分配策略的贝叶斯在线估计器，该估计器将模拟优化与汤普森采样相结合。提出的估计器在模拟实验中表现良好。这项工作是受有关白鼻子综合症传播的数据的激发和说明的，该现象是北美致命的传染性疾病，破坏了蝙蝠种群。