Adaptive Resources Allocation CUSUM for Binomial Count Data Monitoring with Application to COVID-19 Hotspot Detection

TL;DR

This paper introduces an adaptive resource allocation method combining bandit algorithms and change-point detection to efficiently identify COVID-19 hotspots with limited data, outperforming benchmarks in detection speed and accuracy.

Contribution

The paper proposes a novel adaptive resource allocation approach integrating MAB, Bayesian updates, UCB, and CUSUM for hotspot detection, demonstrating improved performance over existing methods.

Findings

Lower detection delay compared to benchmarks

Higher detection precision in simulations

Effective hotspot detection in real COVID-19 data

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.