传染病多维度聚集性探测方法

doi:10.11821/xb201204001

地理学报 ›› 2012, Vol. 67 ›› Issue (4): 435-443.doi: 10.11821/xb201204001

• 方法研究 • 下一篇

传染病多维度聚集性探测方法

廖一兰^1,2, 王劲峰², 杨维中³, 李忠杰³, 金莲梅³, 赖圣杰³, 郑晓瑛¹

1. 北京大学人口研究所, 北京 100871;
2. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室, 北京 100101;
3. 中国疾病预防与控制中心, 北京 102206

收稿日期:2011-03-18 修回日期:2011-10-10 出版日期:2012-04-20 发布日期:2012-06-11
通讯作者: 郑晓瑛, E-mail: xyz@pku.edu.cn; 王劲峰, E-mail: wangjf@lreis.ac.cn
基金资助:
国家科技支撑计划课题(2006BAK01A13); 国家自然科学基金项目(41101431); 中国博士后科学基金项目(201004-70604)

A Study on Detecting Multi-dimensional Clusters of Infectious Diseases

LIAO Yilan^1,2, WANG Jinfeng², YANG Weizhong³, LI Zhongjie³, JIN Lianmei³, LAI Shengjie³, ZHENG Xiaoying¹

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:2012-06-11
Supported by:
National Key Technology R&D Program, No.2006BAK01A13; National NaturalScience Foundation of China, No.41101431; China Postdoctoral Science Foundation, No.201004-70604

摘要/Abstract

摘要： 及早发现异常健康事件的苗头是有效进行传染病早期预警的关键.现有的传染病聚集性探测仅限于时间、空间或时空维度,往往容易忽略病例个人情况从其他方面反映的信息,从而造成过度预警.论文结合蚁群聚类算法和Bayesian Gamma-Poisson 模型,提出一种全新的传染病多维度聚类探测技术.研究区麻疹爆发案例证明该技术在继承以往时空聚集性探测技术思想的基础上,考虑了病例的属性信息,能更为灵敏、准确地找出传染病聚集区域.此方法在实际工作中具有潜在的重要应用价值.

关键词: 传染病, 聚集, 蚁群聚类算法, Bayesian Gamma-Poisson模型, 空间分析

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

廖一兰, 王劲峰, 杨维中, 李忠杰, 金莲梅, 赖圣杰, 郑晓瑛. 传染病多维度聚集性探测方法[J]. 地理学报, 2012, 67(4): 435-443.

LIAO Yilan, WANG Jinfeng, YANG Weizhong, LI Zhongjie, JIN Lianmei, LAI Shengjie, ZHENG Xiaoying. A Study on Detecting Multi-dimensional Clusters of Infectious Diseases[J]. Acta Geographica Sinica, 2012, 67(4): 435-443.

参考文献

[1] Ma Fen, Wang Li, Li Hui. Study advance on the alert methods of infectious disease. Health Research, 2008, 37(2):249-251. [马芬, 王丽, 李辉. 传染病预警方法研究进展. 卫生研究, 2008, 37(2): 249-251.]
[2] Jin Lianmei, Yang Weizhong Status analysis of early warning of infectious disease in China Chinese. Journal of PublicHealth. 2008, 7(24): 845-846. [金连梅, 杨维中. 我国传染病预警工作研究现况分析. 中国公共卫生, 2008, 7(24):845-846.]
[3] Stroup D F, Williamson G D, Herndon J L et al. Detection of aberrations in the occurrence of notifiable diseasessurveillance data. Statistics in Medicine, 1989, 8: 323-239.
[4] Stern L, Lightfoot D. Automated outbreak detection: A quantitative retrospective analysis. Epidemiology and Infection,1999, 122: 103-110.
[5] Reis B Y, Mandl K D. Time series modeling for syndromic surveillance. BMC Medical Informatics and DecisionMaking, 2003, 3: 1-11.
[6] Yang Weizhong, Xing Huixian, Wang Hanzhang et al. A study on early detection for seven infectious diseases. ChineseJournal of Epidemiology, 2004, 25(12): 1039-1041. [杨维中, 邢慧娴, 王汉章等. 七种传染病控制图法预警技术研究.中华流行病学杂志, 2004, 25(12): 1039-1041.]
[7] Yin Fei. A study on the applicability of space-time scan statistics in early warning of infectious disease [D]. Chengdu:Sichuan University, 2007. [殷菲. 时—空扫描统计量在传染病早期预警中的应用研究[D]. 成都: 四川大学, 2007.]
[8] Lawson A B, Kleinman K. Spatial and Syndromic Surveillance for Public Health. New York: Wiley, 2005: 3-4.
[9] Lawson A, Clark A B, Vidal-Rodeiro C L. Developments in general and syndromic surveillance for small area healthdata. Journal of Applied Statistics (Special Issue), 2004, 31: 951-966.
[10] Waller L A, Gotway C A. Applied Spatial Statistics for Public Health Data. New York: Wiley, 2004.
[11] Besag J, Newell J. The detection of clusters in rare diseases. Journal of the Royal Statistical Society: Series A, 1991,154: 143-155.
[12] Kurdorff M. Statistical methods for spatial epidemiology: Tests for randomness//Loytonen M, Gatrell A. GIS andHealth. London: Taylor & Francis, 1998: 49-62.
[13] Tango T. Comparison of general tests for spatial clustering//Lawson et al. Disease Mapping and Risk Assessment forPublic Health. London: Wiley, 1999: 111-117.
[14] Lober W B, Karras B T, Wagner M M et al. Roundtable on bioterrorism detection: Information system-basedsurveillance. Journal of American Medical Informatics Association, 2002, 9: 105-115.
[15] Moore A, Cooper G, Tsui R. Summary of biosurveillance relevant statistical and data mining technologies. Center ofBiomedical Informatics University of Pittsburgh, 2002
[16] Kulldorff M. Syndromic surveillance without denominator data: the space-time permutation scan statistic for diseaseoutbreak detection. PloS Medicine, 2005, 2: 216-224.
[17] Norstrom M, Pfeiffer D U, Jarp J. A space-time cluster investigation of an outbreak of acute respiratory disease inNorwegian cattle herds. Preventative Veterinary Medicine, 2000, 47: 107-119.
[18] Liao Y L. A study on early warning of infectious diseases and health economic evaluation of infectious diseasecontrol: Case study on measles [D]. Beijing Graduate University of Chinese Academy of Sciences, 2009. [廖一兰. 传染病早期预警技术及防控策略卫生经济学评价: 以麻疹为例[D]. 北京: 中国科学院研究生院, 2009.]
[19] Yang Yan. A study for ant-based clustering combination algorithm [D]. Chongqing: Southwest Jiaotong University,2006. [杨燕. 基于计算智能的聚类组合算法研究[D]. 重庆: 西南交通大学, 2006.].
[20] Yang Yan, Jin Pan, Kamel M. Clustering combination based on ant colony algorithm. Journal of the China RailwaySociety, 2004, 26(4): 64-68. [杨燕, 靳蕃, Kamel M. 一种基于蚁群算法的聚类组合方法. 铁道学报, 2004, 26(4):64-68.]
[21] Neill D B, Moore A W. Anomalous spatial cluster detection//Proc. KDD 2005 Workshop on Data Mining Methods forAnomaly Detection, 2005: 41-44.
[22] Neill D B, Moore AW. Efficient scan statistics computations//Lawson A, Kleinman K. Spatial and SyndromicSurveillance for Public Health. Chichester: Wiley, 2005.
[23] Neill D B. Detection of spatial and spatio-temporal clusters [D]. Carnegie Mellon University, 2006.

传染病多维度聚集性探测方法

A Study on Detecting Multi-dimensional Clusters of Infectious Diseases

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	范泽孟,黄言,岳天祥. 青藏高原维管植物物种丰富度分布的情景模拟[J]. 地理学报, 2018, 73(1): 164-176.
[2]	崔娜娜,冯长春,宋煜. 北京市居住用地出让价格的空间格局及影响因素[J]. 地理学报, 2017, 72(6): 1049-1062.
[3]	陶伟,王绍续,朱竑. 广州拾荒者的身体实践与空间建构[J]. 地理学报, 2017, 72(12): 2199-2213.
[4]	杨仲元,徐建刚,林蔚. 基于复杂适应系统理论的旅游地空间演化模式——以皖南旅游区为例[J]. 地理学报, 2016, 71(6): 1059-1074.
[5]	包善驹,陆林. 合肥城市规划引导空间演进对地价时空演变的影响[J]. 地理学报, 2015, 70(6): 906-918.
[6]	吴爱芝, 孙铁山, 李国平. 中国纺织服装产业的空间集聚与区域转移[J]. 地理学报, 2013, 68(6): 775-790.
[7]	王姣娥, 胡浩. 中国高铁与民航的空间服务市场竞合分析与模拟[J]. 地理学报, 2013, 68(2): 175-185.
[8]	湛东升, 孟斌. 基于社会属性的北京市居民居住与就业空间集聚特征[J]. 地理学报, 2013, 68(12): 1607-1618.
[9]	张晓祥, 严长清, 徐盼, 戴煜暄, 晏王波, 丁贤荣, 朱晨曦, 梅丹丹. 近代以来江苏沿海滩涂围垦历史演变研究[J]. 地理学报, 2013, 68(11): 1549-1558.
[10]	陈培阳, 朱喜钢. 基于不同尺度的中国区域经济差异[J]. 地理学报, 2012, 67(8): 1085-1097.
[11]	马丽, 金凤君, 刘毅. 中国经济与环境污染耦合度格局及工业结构解析[J]. 地理学报, 2012, 67(10): 1299-1307.
[12]	王法辉. 社会科学和公共政策的空间化和GIS的应用[J]. 地理学报, 2011, 66(8): 1089-1100.
[13]	陆玉麒. 人文地理学科学化的总体目标与实现路径[J]. 地理学报, 2011, 66(12): 1587-1596.
[14]	陈江平, 张瑶, 余远剑. 空间自相关的可塑性面积单元问题效应[J]. 地理学报, 2011, 66(12): 1597-1606.
[15]	张景秋, 陈叶龙. 北京城市办公空间的行业分布及集聚特征[J]. 地理学报, 2011, 66(10): 1299-1308.