中文

Journal of Shanghai University（Natural Science Edition） ›› 2014, Vol. 20 ›› Issue (3): 274-280.doi: 10.3969/j.issn.1007-2861.2014.02.012

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Application of Multivariate Normal Scan Statistic to Most Severe Cluster of Local Diseases

JIANG Wei1, SHEN Xiao-bei2, TSUNG Fugee3   

  1. 1. Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai 200052, China;
    2. School of Management, Shanghai University, Shanghai 200444, China;
    3. Department of Industrial Engineering and Logistic Management, The Hong Kong University of Science and Technology, Hong Kong 999077, China
  • Received:2014-05-02 Online:2014-06-26 Published:2014-06-26
  • Contact: 宗福季(1967—), 男, 国际质量研究院院士, 教授, 博士生导师, 博士, 研究方向为质量管理和统计过程控制等. E-mail: ftsung@gmail.com E-mail:ftsung@gmail.com
  • About author:宗福季(1967—), 男, 国际质量研究院院士, 教授, 博士生导师, 博士, 研究方向为质量管理和统计过程控制等. E-mail: ftsung@gmail.com
  • Supported by:

    RGC Competitive Earmarked Research (2012–2014)

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Abstract: This paper focuses on the problem of detecting ageographical cluster with the most severe status in multiple groups of populations with the consideration of limited medical resources. In an early stage of a disease, an outbreak may only be present in some specific population group. Therefore, to efficiently detect the outbreak, specific groups need to be particularly monitored and evaluated. The objective of detection as the most severe cluster (MSC) is defined. Considering interaction among population groups, a multivariate normal scan statistic is proposed. The method is applied to an example of lung cancer in New York State to detect the MSC with a high mortality rate at the aggregate level.

Key words: correlation between population groups, geographical cluster with the most severe status, multiple groups of population, continuous data

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