基于百日咳时间序列特征的聚类分析  

Cluster analysis based on the characteristics of pertussis time series

作　　者：陈佳 宋秋月 李芳 张彦琦 刘岭[1] 易东[1] 伍亚舟[1] CHEN Jia;SONG Qiu-yue;LI Fang;ZHANG Yan-qi;LIU Ling;YI Dong;WU Ya-zhou(Department of Military Health Statistics,Department of Military Preventive Medicine,Army Medical University,Chongqing 400038,China)

机构地区：[1]陆军军医大学军事预防医学系军队卫生统计学教研室,重庆400038

出　　处：《中华疾病控制杂志》2022年第9期1065-1071,共7页Chinese Journal of Disease Control & Prevention

基　　金：国家自然科学基金(81872716)。

摘　　要：目的利用时间序列特征提取方法对中国25个省级行政区的百日咳发病数据进行聚类,根据聚类结果分析出各地区百日咳不同的发病模式,为中国实施百日咳疾病防控统一规划提供科学依据。方法提取全国25个省级行政区百日咳时间序列的9个全局特征,利用主成分分析将9个指标转化为3个主成分组成的特征矩阵进行层次聚类分析。选择最佳聚类数划分百日咳时间序列不同的发病模式。结果层次聚类最佳聚类数为3类,即对应百日咳的3种发病模式,分别为无周期性有季节性无趋势性模式(共9个省级行政区)、无周期性有季节性有趋势性模式(共10个省级行政区)和有周期性有季节性有趋势性模式(共6个省级行政区)。结论时间序列特征提取的层次聚类能够很好地将相似模式紧密的分在一组,并准确的划分出中国25个省级行政区百日咳疫情的发病模式,聚类结果可为相关部门制定不同省份百日咳的防控措施提供理论依据。Objective The data on the incidence of pertussis in 25 provincial administrative regions in China were clustered using a time-series feature extraction method.Based on the clustering results,the different incidence patterns of pertussis in various regions are analyzed to provide a scientific basis for the unified planning and implementation of pertussis disease prevention and control in China.Methods The nine global features of pertussis time series from 25 provincial administrative regions in China were extracted,and the nine indicators were transformed into a feature matrix consisting of three principal components using principal component analysis for hierarchical clustering analysis.The optimal number of clusters was selected to classify the different incidence patterns of pertussis time series.Results The optimal cluster number of hierarchical clustering was three categories,i.e.corresponding to the three incidence patterns of pertussis:acyclic,seasonal and non-trend pattern(9 provincial administrative regions in total),acyclic,seasonal and trend pattern(10 provincial administrative regions in total)and cyclic,seasonal and trend pattern(6 provincial administrative regions in total).Conclusions The hierarchical clustering by time series feature extraction can well group similar patterns closely together and accurately delineate the incidence patterns of pertussis in 25 provincial administrative regions of China.The clustering results can provide a theoretical basis for relevant departments to formulate prevention and control measures for pertussis in different Provinces.

关 键 词：百日咳 时间序列特征提取 主成分分析 聚类分析 发病模式 

分 类 号：R183.1[医药卫生—流行病学]