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A spatial scan statistic for multiple clusters

A spatial scan statistic for multiple clusters
A spatial scan statistic for multiple clusters
Spatial scan statistics are commonly used for geographical disease surveillance and cluster detection. While there are multiple clusters coexisting in the study area, they become difficult to detect because of clusters' shadowing effect to each other. The recently proposed sequential method showed its better power for detecting the second weaker cluster, but did not improve the ability of detecting the first stronger cluster which is more important than the second one. We propose a new extension of the spatial scan statistic which could be used to detect multiple clusters. Through constructing two or more clusters in the alternative hypothesis, our proposed method accounts for other coexisting clusters in the detecting and evaluating process. The performance of the proposed method is compared to the sequential method through an intensive simulation study, in which our proposed method shows better power in terms of both rejecting the null hypothesis and accurately detecting the coexisting clusters. In the real study of hand-foot-mouth disease data in Pingdu city, a true cluster town is successfully detected by our proposed method, which cannot be evaluated to be statistically significant by the standard method due to another cluster's shadowing effect.
geographic disease surveillance, multiple clusters, spatial scan statistic, statistical power
0025-5564
135-142
Li, Xiao-Zhou
ced34730-83c9-4b8b-9241-74da17a62c9e
Wang, Jin-Feng
b8ccd997-188b-4d55-af4b-02f6189625ba
Yang, Wei-Zhong
35daea8e-8d21-43ec-bc93-f39c1c304316
Li, Zhong-Jie
c6c1bcc6-e23f-4b30-bc15-ce18abbab914
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Li, Xiao-Zhou
ced34730-83c9-4b8b-9241-74da17a62c9e
Wang, Jin-Feng
b8ccd997-188b-4d55-af4b-02f6189625ba
Yang, Wei-Zhong
35daea8e-8d21-43ec-bc93-f39c1c304316
Li, Zhong-Jie
c6c1bcc6-e23f-4b30-bc15-ce18abbab914
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001

Li, Xiao-Zhou, Wang, Jin-Feng, Yang, Wei-Zhong, Li, Zhong-Jie and Lai, Shengjie (2011) A spatial scan statistic for multiple clusters. Mathematical Biosciences, 233 (2), 135-142. (doi:10.1016/j.mbs.2011.07.004). (PMID:21827771)

Record type: Article

Abstract

Spatial scan statistics are commonly used for geographical disease surveillance and cluster detection. While there are multiple clusters coexisting in the study area, they become difficult to detect because of clusters' shadowing effect to each other. The recently proposed sequential method showed its better power for detecting the second weaker cluster, but did not improve the ability of detecting the first stronger cluster which is more important than the second one. We propose a new extension of the spatial scan statistic which could be used to detect multiple clusters. Through constructing two or more clusters in the alternative hypothesis, our proposed method accounts for other coexisting clusters in the detecting and evaluating process. The performance of the proposed method is compared to the sequential method through an intensive simulation study, in which our proposed method shows better power in terms of both rejecting the null hypothesis and accurately detecting the coexisting clusters. In the real study of hand-foot-mouth disease data in Pingdu city, a true cluster town is successfully detected by our proposed method, which cannot be evaluated to be statistically significant by the standard method due to another cluster's shadowing effect.

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More information

Accepted/In Press date: 23 July 2011
Published date: October 2011
Keywords: geographic disease surveillance, multiple clusters, spatial scan statistic, statistical power
Organisations: Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 373610
URI: http://eprints.soton.ac.uk/id/eprint/373610
ISSN: 0025-5564
PURE UUID: 032a4274-c28a-4d40-800b-3e1b2be1d55b
ORCID for Shengjie Lai: ORCID iD orcid.org/0000-0001-9781-8148

Catalogue record

Date deposited: 26 Jan 2015 13:56
Last modified: 15 Mar 2024 04:02

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Contributors

Author: Xiao-Zhou Li
Author: Jin-Feng Wang
Author: Wei-Zhong Yang
Author: Zhong-Jie Li
Author: Shengjie Lai ORCID iD

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