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A Scan Statistic for Binary Outcome Based on Hypergeometric Probability Model, with an Application to Detecting Spatial Clusters of Japanese Encephalitis

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  • Xing Zhao
  • Xiao-Hua Zhou
  • Zijian Feng
  • Pengfei Guo
  • Hongyan He
  • Tao Zhang
  • Lei Duan
  • Xiaosong Li

Abstract

As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff’s methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff’s statistics for clusters of high population density or large size; otherwise Kulldorff’s statistics are superior.

Suggested Citation

  • Xing Zhao & Xiao-Hua Zhou & Zijian Feng & Pengfei Guo & Hongyan He & Tao Zhang & Lei Duan & Xiaosong Li, 2013. "A Scan Statistic for Binary Outcome Based on Hypergeometric Probability Model, with an Application to Detecting Spatial Clusters of Japanese Encephalitis," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-7, June.
    • Handle: RePEc:plo:pone00:0065419
    DOI: 10.1371/journal.pone.0065419
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    References listed on IDEAS

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    1. Duczmal, Luiz & Assuncao, Renato, 2004. "A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 269-286, March.
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