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. 2022 Sep 3;50(14):2889-2913.
doi: 10.1080/02664763.2022.2117288. eCollection 2023.

Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection

Jiuyun Hu  1 , Yajun Mei  2 , Sarah Holte  3 , Hao Yan  1
Affiliations

Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection

Jiuyun Hu et al. J Appl Stat. .

Abstract

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.

Keywords: 62L15; 62P10; CUSUM statistics; Multi-arm bandit; adaptive resources allocation; change point detection; count data.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
Figure 1.
Infection rate on Sep 13, 2020 in the United States.
Figure 2.
Figure 2.
Example of infection rates in WA.
Figure 3.
Figure 3.
Comparison of county-wise infection rate in WA on different dates. (a) Infection proportion on day 100: May 1 2020 and (b) Infection proportion on day 200: Aug 9 2020.
Figure 4.
Figure 4.
Flow chart for the algorithm.
Figure 5.
Figure 5.
Example of reward functions with different estimated infection rate.
Figure 6.
Figure 6.
Visualization of test distributions in control. (a) Test distributions density in control and (b) Boxplot of number of tests in control.
Figure 7.
Figure 7.
Median number of tests. (a) Median number of tests in control and (b) Median number of tests out of control.
Figure 8.
Figure 8.
Comparison of the test statistics.
Figure 9.
Figure 9.
Example of test distribution in WA. (a) Example of test distribution in control and (b) Example of test distribution out of control.
Figure A1.
Figure A1.
Boxplot of the testing kit distribution in one county before the change for different w.
Figure A2.
Figure A2.
Boxplot of the testing kit distribution in one county before the change for different a.
See this image and copyright information in PMC

References

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