Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection
Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection
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DOI：
10.1080/02664763.2022.2117288
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发表时间：
2022-08
影响因子：
1.5
通讯作者：
Jiuyun Hu;Y. Mei;S. Holte;Hao Yan
中科院分区：
文献类型：
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作者：
Jiuyun Hu;Y. Mei;S. Holte;Hao Yan
在本文中，我们提出了一种有效的统计方法（表示为“自适应资源分配矩阵”），以强大和有效地检测有限的采样资源的热点。我们的主要思想是结合联合收割机的多臂强盗（MAB）和变点检测方法，以平衡资源分配的探索和开发热点检测。此外，贝叶斯加权更新用于更新感染率的后验分布。然后，置信上限（UCB）用于资源分配和规划。最后，通过CNOUM监控统计数据来检测更改点以及更改位置。在性能评估方面，我们将该算法与文献中的几种基准方法进行了性能比较，结果表明该算法能够实现较低的检测延迟和较高的检测精度。最后，将该方法应用于华盛顿州县级每日COVID-19阳性病例的真实的案例研究中的热点检测，并在非常有限的分布式样本下证明了该方法的有效性。
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.