Chines Journal of Vector Biology and Control ›› 2018, Vol. 29 ›› Issue (5): 439-441.DOI: 10.11853/j.issn.1003.8280.2018.05.004

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The preliminary quantitative description of epidemic intensity in enzootic plague based on information entropy theory

GE Jun-qi1, LI Jing-hui2, MA Yong-kang3, GONG Zheng-da3,4   

  1. 1 Chaoyang District Center for Disease Control and Prevention, Beijing 100021, China;
    2 National Institute for ommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention;
    3 Yunnan Institute of Endemic Diseases Control and Prevention;
    4 Yunnan Institute of Parasitic Diseases Control and Prevention
  • Received:2018-06-11 Online:2018-10-20 Published:2018-10-20
  • Supported by:
    Supported by the Municipal Natural Science Foundation of Beijing of China (No. 7173262)

基于信息熵的动物鼠疫流行强度定量化初探

葛军旗1, 李镜辉2, 马永康3, 龚正达3,4   

  1. 1 北京市朝阳区疾病预防控制中心, 北京 100021;
    2 中国疾病预防控制中心传染病预防控制所, 北京 102206;
    3 云南省地方病防治所, 云南 大理 671000;
    4 云南省寄生虫病防治所, 云南 普洱 665000
  • 作者简介:葛军旗,男,硕士,副主任医师,从事消毒与病媒生物防制研究,Email:18785550@qq.com
  • 基金资助:
    北京市自然科学基金(7173262)

Abstract: Objective To explore the feasibility using information entropy formula to measure the epidemic intensity of enzootic plague. Methods The epidemic intensity formula named Ia for enzootic plague based on the information entropy formula was proposed, and used to calculate the epidemic intensity of hypothetical data in 8 epidemic foci (the number of township ≥ 8). The spearman correlation coefficient of the epidemic intensity between Yingjiang and Longchuan counties during 1982-2005 was calculated. Results The epidemic intensity of the hypothetical data showed that the epidemic intensity was the smallest (0.288) when 8 foci were concentrated in 1 townships, while the largest (2.079) when 8 townships each had 1 epidemic focus; with the same foci, the more townships involved in the epidemic, the greater the prevalence intensity. The largest epidemic intensity of Yingjiang (2.107) and Longchuan (1.642) were discovered in 1995 and 1990 respectively; the biggest epidemic intensity of Yingjiang appeared in 1995 (Ia=2.107), rather than in 1993 (Ia=1.885) with the most epidemic foci. The correlation coefficient of epidemic intensity in two counties was 0.301 (P=0.150), which showed that there was no statistical correlation. Conclusion The formula verified by simulated and actual data is proved to be able to describe the features of complexity and hierarchical structure of plague epidemic, and make possible the comparisons of epidemic intensity temporally and spatially.

Key words: Information entropy, Plague, Epidemic intensity

摘要: 目的 探索使用信息熵公式测量动物鼠疫流行强度的可行性。方法 基于信息熵公式构建动物鼠疫的流行强度公式Ia,尝试用该公式计算8个疫点(乡镇数≥8)模拟数据的流行强度,并对1982-2005年云南省盈江、陇川两个县疫情数据的流行强度进行Spearman相关分析。结果 模拟数据的流行强度结果显示,8个疫点集中于1个乡镇时,动物鼠疫的流行强度最小(Ia=0.288),8个乡镇均有疫情时,动物鼠疫的流行强度最大(Ia=2.079),在疫点相同的情况下,疫情涉及的乡镇数越多,流行强度越大;疫情数据的流行强度显示,盈江和陇川县动物鼠疫流行强度最大的年份分别出现在1995年(Ia=2.107)和1990年(Ia=1.642);盈江县动物鼠疫流行强度最大的年份出现在1995年(Ia=2.107),而不是发生疫点数最多的1993年(Ia=1.885)。对2个县Ia做Spearman相关分析,相关系数为0.301(P=0.150),显示2个县动物鼠疫流行情况不存在统计学上的关联。结论 流行强度公式能够区分鼠疫疫情的复杂性和层次结构,使动物鼠疫流行强度在同一疫源地的不同时段、或同一时段不同疫源地之间产生可比性。

关键词: 信息熵, 鼠疫, 流行强度

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