PMID- 37808611
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240210
IS  - 0266-4763 (Print)
IS  - 1360-0532 (Electronic)
IS  - 0266-4763 (Linking)
VI  - 50
IP  - 14
DP  - 2023
TI  - Adaptive resources allocation CUSUM for binomial count data monitoring with 
      application to COVID-19 hotspot detection.
PG  - 2889-2913
LID - 10.1080/02664763.2022.2117288 [doi]
AB  - 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.
CI  - (c) 2022 Informa UK Limited, trading as Taylor & Francis Group.
FAU - Hu, Jiuyun
AU  - Hu J
AD  - School of Computing and Augmented Intelligence, Arizona State University, Tempe, 
      AZ, USA.
FAU - Mei, Yajun
AU  - Mei Y
AUID- ORCID: 0000-0002-1015-990X
AD  - School of Industrial and Systems Engineering, Georgia Institute of Technology, 
      Atlanta, GA, USA.
FAU - Holte, Sarah
AU  - Holte S
AUID- ORCID: 0000-0001-7569-3700
AD  - Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 
      Seattle, WA, USA.
FAU - Yan, Hao
AU  - Yan H
AUID- ORCID: 0000-0002-4322-7323
AD  - School of Computing and Augmented Intelligence, Arizona State University, Tempe, 
      AZ, USA.
LA  - eng
GR  - P30 AI027757/AI/NIAID NIH HHS/United States
GR  - R21 AI157618/AI/NIAID NIH HHS/United States
PT  - Journal Article
DEP - 20220903
PL  - England
TA  - J Appl Stat
JT  - Journal of applied statistics
JID - 9883455
PMC - PMC10557554
OTO - NOTNLM
OT  - 62L15
OT  - 62P10
OT  - CUSUM statistics
OT  - Multi-arm bandit
OT  - adaptive resources allocation
OT  - change point detection
OT  - count data
COIS- No potential conflict of interest was reported by the author(s).
EDAT- 2022/09/03 00:00
MHDA- 2022/09/03 00:01
PMCR- 2023/09/03
CRDT- 2023/10/09 05:40
PHST- 2022/09/03 00:01 [medline]
PHST- 2022/09/03 00:00 [pubmed]
PHST- 2023/10/09 05:40 [entrez]
PHST- 2023/09/03 00:00 [pmc-release]
AID - 2117288 [pii]
AID - 10.1080/02664763.2022.2117288 [doi]
PST - epublish
SO  - J Appl Stat. 2022 Sep 3;50(14):2889-2913. doi: 10.1080/02664763.2022.2117288. 
      eCollection 2023.