上海交通大学学报(医学版) ›› 2020, Vol. 40 ›› Issue (06): 713-718.doi: 10.3969/j.issn.1674-8115.2020.06.003

• 新型冠状病毒防控专栏 • 上一篇    

基于肘聚类分析的中国疫情模式回顾性分析

李 蔷1, 2,孙 喆2,钱碧云2, 3,冯铁男2, 4   

  1. 1.上海交通大学公共卫生学院,上海200025;2.上海交通大学医学院临床研究中心,上海200025;3. 上海申康医院发展中心临床研究促进与发展中心,上海200041;4. 中国核工业集团公司416医院,成都医学院附属第二医院,成都610057
  • 出版日期:2020-06-28 发布日期:2020-08-11
  • 通讯作者: 冯铁男,电子信箱:tienan_feng@126.com。
  • 作者简介:李 蔷(1996—),女,硕士生;电子信箱:119711910075@sjtu.edu.cn。
  • 基金资助:
    上海交通大学医工交叉项目(YG2017QN70);上海交通大学医学院科技处技术转移项目(ZT201919)。

Retrospective analysis of Chinese epidemic situation model based on elbow cluster analysis

LI Qiang1, 2, SUN Zhe2, QIAN Bi-yun2, 3, FENG Tie-nan2, 4   

  1. 1. Shanghai Jiao University School of Public Health, Shanghai 200025, China; 2. Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; 3. Clinical Research Promotion and Development Center, Shanghai Shenkang Hospital Development Center, Shanghai 200041, China; 4. Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu 610057, China
  • Online:2020-06-28 Published:2020-08-11
  • Supported by:
    Medical Engineering Cross Project of Shanghai Jiao Tong University (YG2017QN70); Technology Transfer Project of Science and Technology Department of Shanghai Jiao Tong University School of Medicine (ZT201919).

摘要: 目的·探讨中国各省级行政区新型冠状病毒肺炎(coronavirus disease 2019,COVID-19)疫情早期的关联模式,并预测之后的疫情发展。方法·回顾性分析2020年1月13日—2月13日中华人民共和国国家卫生健康委员会公布的关于全国各省级行政区COVID-19疫情的数据,采用肘聚类分析法对全国各省级行政区进行聚类分析,利用SEIR(susceptible-exposed-infectious-recovered)模型计算不同聚集区基本传染数(R0),预测各类聚集区疫情变化趋势。结果·根据患病率从低到高将全国34个省级行政区分为4类聚集区:Ⅰ类聚集区(22个省级行政区)、Ⅱ类聚集区(9个省级行政区)、Ⅲ类聚集区(2个省级行政区)、Ⅳ类聚集区(湖北)。湖北的患病率显著高于其他3类聚集区(P=0.000),但各类聚集区之间的治愈率和病死率差异无统计学意义。4类聚集区的R0值分别为2.764、3.056、3.899、3.984。截至2020年2月13日,除湖北外,其他聚集区的累积患病率曲线趋于平稳,治愈率曲线上升;湖北的患病率和病死率仍较高,治愈率较低。结论·2020年1月13日—2月13日,中国34个省级行政区根据COVID-19疫情严重程度可分为4类,Ⅳ类聚集区的患病率显著高于其他3类聚集区;且截至2020年2月13日,Ⅰ、Ⅱ、Ⅲ类聚集区疫情有所缓解,Ⅳ类聚集区疫情仍较为严峻。

关键词: 新型冠状病毒肺炎, 肘聚类分析, SEIR模型, 回顾性分析

Abstract: Objective · To explore the correlation patterns of the new coronavirus disease 2019 (COVID-19) epidemic in various provincial administrative regions in China at the early stage of the epidemic, and forecast the following development of epidemic situation. Methods · The data on the COVID-19 epidemic situation in various provincial administrative regions in China published by National Health Commission of People's Republic of China from Jan. 13 to Feb. 13, 2020, were retrospectively analyzed. The elbow cluster analysis method was used to cluster the provincial administrative regions. The SEIR (susceptible-exposed-infectious-recovered) model was used to calculate the basic infection number (R0) of different clusters, whose changing trends were also predicted. Results · According to the prevalence rates, the 34 provincial administrative regions were divided into four types of clusters: Cluster Ⅰ (22 provincial administrative regions) , Cluster Ⅱ (9 provincial administrative regions), Cluster Ⅲ (2 provincial administrative regions) and Cluster Ⅳ (Hubei). The prevalence rate of Hubei was higher than those of other clusters (P=0.000), but the differences in the cure rate and the case-fatality rate among the four clusters were not statistically significant; the R0 values based on the SEIR model of them were 2.764, 3.056, 3.899 and 3.984, respectively. By Feb. 13, 2020, except for Hubei, the cumulative prevalence curves of the other clusters tended to be stable and the cure rates increased. The prevalence rate and case-fatality rate of Hubei were still higher, and the cure rate was lower. Conclusion · From Jan. 13 to Feb. 13, 2020, 34 provincial administrative regions in China can be divided into four clusters according to the severity of the COVID-19 epidemic, and the prevalence rate of Cluster Ⅳ was significantly higher than those of other three clusters; by Feb. 13, 2020, the epidemic situations in the Cluster Ⅰ , Ⅱ and Ⅲ has been alleviated, and the epidemic situation in Cluster Ⅳ areas were still severe.

Key words: coronavirus disease 2019 (COVID-19), elbow cluster analysis, SEIR (susceptible-exposed-infectious-recovered) model, retrospective analysis

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