Space–time surveillance of count data subject to linear trends

This paper proposes a new space–time cumulative sum (CUSUM) approach for detecting changes in spatially distributed Poisson count data subject to linear drifts. We develop expressions for the likelihood ratio test monitoring statistics and the change point estimators. The effectiveness of the propos...

Full description

Saved in:
Bibliographic Details
Published inQuality and reliability engineering international Vol. 37; no. 1; pp. 145 - 164
Main Authors Vanli, O. Arda, Alawad, Nour
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 01.02.2021
Subjects
Change detection
change point estimation
cumulative sum
Health surveillance
Likelihood ratio
Monitoring
Public health
scan methods
space–time monitoring
Statistical tests
Surveillance
Trends
Online AccessGet full text

Cover

More Information
Summary:This paper proposes a new space–time cumulative sum (CUSUM) approach for detecting changes in spatially distributed Poisson count data subject to linear drifts. We develop expressions for the likelihood ratio test monitoring statistics and the change point estimators. The effectiveness of the proposed monitoring approach in detecting and identifying trend‐type shifts is studied by simulation under various shift scenarios in regional counts. It is shown that designing the space–time monitoring approach specifically for linear trends can enhance the change point estimation accuracy significantly. A case study for male thyroid cancer outbreak detection is presented to illustrate the application of the proposed methodology in public health surveillance.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0748-8017
1099-1638
DOI:10.1002/qre.2727