  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.
