A study on detecting multi-dimensional clusters of infectious diseases
A study on detecting multi-dimensional clusters of infectious diseases
To indentify early signs of unusual health events is critical to early warning of infectious diseases. A new method for detecting multi-dimensional clusters of infectious diseases is presented in this paper. Ant colony clustering algorithm is applied to classify the cases of specified infectious diseases according to their crowd characters; then the cases belonging to the same class in terms of the space adjacency is separated; finally, the prior information about previous diseases outbreaks in the study area is applied to test the hypothesis that there was no disease cluster at various sub-regions. The detection ability of the method shows that this method does not need to accumulate case data within a long time period to detect irregular-shaped hot spots. It is useful for introducing spatial analysis to detection of infectious disease outbreaks.
Ant colony clustering algorithm, Bayesian Gamma-Poisson model, Cluster, Infectious diseases, Spatial analysis
435-443
Liao, Yilan
7a1a861c-a091-417a-8c94-c1c6d5e6c875
Wang, Jinfeng
3b2e15d2-baff-451c-8a30-d05c3970059f
Yang, Weizhong
65d18fbc-d752-42a7-ac38-01534ceda15c
Li, Zhongjie
8c060065-5459-449e-a776-29d55614adb7
Jin, Lianmei
339ce7b9-541a-4ed4-ac63-e62cd3545000
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Zheng, Xiaoying
c549e9fe-1c5e-48fe-bd3c-60a818bdeb80
1 January 2012
Liao, Yilan
7a1a861c-a091-417a-8c94-c1c6d5e6c875
Wang, Jinfeng
3b2e15d2-baff-451c-8a30-d05c3970059f
Yang, Weizhong
65d18fbc-d752-42a7-ac38-01534ceda15c
Li, Zhongjie
8c060065-5459-449e-a776-29d55614adb7
Jin, Lianmei
339ce7b9-541a-4ed4-ac63-e62cd3545000
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Zheng, Xiaoying
c549e9fe-1c5e-48fe-bd3c-60a818bdeb80
Liao, Yilan, Wang, Jinfeng, Yang, Weizhong, Li, Zhongjie, Jin, Lianmei, Lai, Shengjie and Zheng, Xiaoying
(2012)
A study on detecting multi-dimensional clusters of infectious diseases.
Dili Xuebao, 67 (4), .
Abstract
To indentify early signs of unusual health events is critical to early warning of infectious diseases. A new method for detecting multi-dimensional clusters of infectious diseases is presented in this paper. Ant colony clustering algorithm is applied to classify the cases of specified infectious diseases according to their crowd characters; then the cases belonging to the same class in terms of the space adjacency is separated; finally, the prior information about previous diseases outbreaks in the study area is applied to test the hypothesis that there was no disease cluster at various sub-regions. The detection ability of the method shows that this method does not need to accumulate case data within a long time period to detect irregular-shaped hot spots. It is useful for introducing spatial analysis to detection of infectious disease outbreaks.
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Published date: 1 January 2012
Additional Information:
Acta Geographica Sinica
Keywords:
Ant colony clustering algorithm, Bayesian Gamma-Poisson model, Cluster, Infectious diseases, Spatial analysis
Identifiers
Local EPrints ID: 429996
URI: http://eprints.soton.ac.uk/id/eprint/429996
ISSN: 0375-5444
PURE UUID: a2648c54-c5dd-42a3-a99b-c3f708a6b460
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Date deposited: 09 Apr 2019 16:30
Last modified: 06 Jun 2024 02:03
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Contributors
Author:
Yilan Liao
Author:
Jinfeng Wang
Author:
Weizhong Yang
Author:
Zhongjie Li
Author:
Lianmei Jin
Author:
Xiaoying Zheng
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