Acta Geographica Sinica ›› 2012, Vol. 67 ›› Issue (4): 435-443.doi: 10.11821/xb201204001

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A Study on Detecting Multi-dimensional Clusters of Infectious Diseases

LIAO Yilan1,2, WANG Jinfeng2, YANG Weizhong3, LI Zhongjie3, JIN Lianmei3, LAI Shengjie3, ZHENG Xiaoying1   

  1. 1. Institute of Population Research, Peking University, Beijing 100871, China;
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    3. Chinese center for Disease Control and Prevention, Beijing 102206, China
  • Received:2011-03-18 Revised:2011-10-10 Online:2012-04-20 Published:2012-04-20
  • Supported by:
    National Key Technology R&D Program, No.2006BAK01A13; National NaturalScience Foundation of China, No.41101431; China Postdoctoral Science Foundation, No.201004-70604

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.

Key words: infectious diseases, cluster, ant colony clustering algorithm, Bayesian Gamma-Poisson model, spatial analysis