论文标题
适用于二项式计数数据监视的自适应资源分配库，并应用于Covid-19 Hotspot检测
Adaptive Resources Allocation CUSUM for Binomial Count Data Monitoring with Application to COVID-19 Hotspot Detection
论文作者
论文摘要
在本文中，我们提出了一种有效的统计方法（称为“自适应资源分配Cusum”），以使用有限的采样资源来稳健有效地检测热点。我们的主要思想是结合多臂强盗（MAB）和更改点检测方法，以平衡对热点检测资源分配的探索和开发。此外，使用贝叶斯加权更新来更新感染率的后验分布。然后，将上限限制（UCB）用于资源分配和计划。最后，Cusum监视统计数据以检测变化点以及变化位置。为了进行绩效评估，我们将提出方法的性能与文献中的几种基准方法进行了比较，并表明所提出的算法能够达到较低的检测延迟和较高的检测精度。最后，在华盛顿州华盛顿州县级每日阳性19例案例的实际案例研究中，将此方法应用于热点检测，并以非常有限的分布样本证明了有效性。
In this paper, we present an efficient statistical method (denoted as "Adaptive Resources Allocation CUSUM") to robustly and efficiently detect the hotspot with limited sampling resources. Our main idea is to combine the multi-arm bandit (MAB) and change-point detection methods to balance the exploration and exploitation of resource allocation for hotspot detection. Further, a Bayesian weighted update is used to update the posterior distribution of the infection rate. Then, the upper confidence bound (UCB) is used for resource allocation and planning. Finally, CUSUM monitoring statistics to detect the change point as well as the change location. For performance evaluation, we compare the performance of the proposed method with several benchmark methods in the literature and showed the proposed algorithm is able to achieve a lower detection delay and higher detection precision. Finally, this method is applied to hotspot detection in a real case study of county-level daily positive COVID-19 cases in Washington State WA) and demonstrates the effectiveness with very limited distributed samples.
