Flexible-Elliptical Spatial Scan Method

Mohammad Meysami (), Joshua P. French and Ettie M. Lipner
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Mohammad Meysami: Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
Joshua P. French: Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA
Ettie M. Lipner: National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA

Mathematics, 2023, vol. 11, issue 17, 1-22

Abstract: The detection of disease clusters in spatial data analysis plays a crucial role in public health, while the circular scan method is widely utilized for this purpose, accurately identifying non-circular (irregular) clusters remains challenging and reduces detection accuracy. To overcome this limitation, various extensions have been proposed to effectively detect arbitrarily shaped clusters. In this paper, we combine the strengths of two well-known methods, the flexible and elliptic scan methods, which are specifically designed for detecting irregularly shaped clusters. We leverage the unique characteristics of these methods to create candidate zones capable of accurately detecting irregularly shaped clusters, along with a modified likelihood ratio test statistic. By inheriting the advantages of the flexible and elliptic methods, our proposed approach represents a practical addition to the existing repertoire of spatial data analysis techniques.

Keywords: spatial scan statistic; public health; disease cluster identification; candidate zones; likelihood ratio test statistics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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