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山东大学学报 (医学版) ›› 2020, Vol. 58 ›› Issue (10): 66-73.doi: 10.6040/j.issn.1671-7554.0.2020.0746

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浙江省新型冠状病毒肺炎的流行特征与空间分析

贾艳1,李春雨1,刘利利1,佘凯丽1,刘廷轩1,朱雨辰1,齐畅1,张丹丹1,王旭1,陈恩富2,李秀君1   

  1. 1. 山东大学齐鲁医学院公共卫生学院生物统计学系, 山东 济南 250012;2. 浙江省疾病预防控制中心传染病预防控制所, 浙江 杭州 310051
  • 发布日期:2020-10-08
  • 通讯作者: 李秀君. E-mail:xjli@sdu.edu.cn陈恩富. E-mail:enfchen@cdc.zj.cn
  • 基金资助:
    国家自然科学基金(81673238);国家重点研发计划(2019YFC1200500,2019YFC1200502);山东大学新冠肺炎应急攻关科研专项(2020XGC01)

Epidemic characteristics and spatial analysis of COVID-19 in Zhejiang Province

JIA Yan1, LI Chunyu1, LIU Lili1, SHE Kaili1, LIU Tingxuan1, ZHU Yuchen1, QI Chang1, ZHANG Dandan1, WANG Xu1, CHEN Enfu2, LI Xiujun1   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, Zhejiang, China
  • Published:2020-10-08

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摘要： 目的 探讨浙江省新型冠状病毒肺炎(COVID-19)确诊病例的时空分布特征,确定确诊病例数与地理、人口学因素间的相关性,以便为疫情防控提供理论依据。 方法 收集浙江省2020年1月21日至2月19日的COVID-19确诊病例数据;描述性分析确诊病例的三间分布特征及暴露史特征;以县区为单位,分析空间自相关性,并使用层次聚类分析对11个地市进行分类;利用Spearman等级相关分析确诊病例数与地理、人口学因素的相关性。 结果 确诊病例的年龄主要集中在18~60岁(848例,占71.44%);性别间差异无统计学意义(P=0.742)。各地市在1月29日前后达到日新增确诊病例数峰值,在1月30日之后,新增确诊病例以其他地区暴露为主。浙江省各县区确诊病例数存在一定的空间聚集性,聚集热点集中在温州和台州的部分县区。将11个地市划分为4类,温州、宁波分别为一类,杭州和台州归为一类,其他地市归为一类。武汉迁入人口规模与病例数呈正相关(rs=0.93, P<0.001)。 结论 浙江省COVID-19疫情前期以湖北地区暴露病例为主,后期以续发病例为主,病例聚集热点为温州、台州的部分县区,目前浙江省疫情防控已见成效,应继续实行控制措施,防止重点地区出现疫情反弹,积极应对返工、返学带来的疫情风险,并加强对高危地区输入人员的监测和管理。

关键词: 新型冠状病毒肺炎, 浙江省, 流行病学特征, 空间自相关, 聚类分析

Abstract: Objective To explore the temporal and spatial distribution characteristics of confirmed cases of coronavirus disease(COVID-19)in Zhejiang Province and to determine the correlation between number of confirmed cases and geographical demographic factors, so as to provide theoretical basis for the prevention and control of COVID-19. Methods Data of COVID-19 cases confirmed during Jan. 21 and Feb. 19, 2020 in Zhejiang Province were collected. The demographic, temporal and spatial distribution characteristics and exposure history were descriptively analyzed. With county as a unit, the spatial autocorrelation was analyzed, and 11 cities were classified with hierarchical clustering. The correlation between number of confirmed cases and geographical demographic factors was determined with Spearman rank correlation analysis. Results 71.44%(848 cases)of the patients were aged 18-60 years, and there was no statistically significant difference between the sexes(P=0.742). The number of daily confirmed new cases reached the peak around Jan. 29 in various cities. After Jan. 30, The majority of daily confirmed new cases had exposure history of other areas. The confirmed cases in various counties and districts of Zhejiang Province showed characteristic of spatial clustering, and the clustering hotspots were some counties of Wenzhou and Taizhou City. The 11 cities were classified into 4 categories: Wenzhou; Ningbo; Hangzhou and Taizhou; other cities. Population size moving in from Wuhan was positively correlated with the number of cases(rs=0.93, P<0.001). Conclusion In the early stage of COVID-19 epidemic, the majority of cases had exposure history of Hubei; in the later stage, reported cases were mainly secondary cases. Clustering hotspots were some counties of Wenzhou and Taizhou City. Currently, the prevention and control of the epidemic in Zhejiang Province has been effective. It is necessary to continue implementing control measures to prevent the outbreak from rebounding in high-risk areas, and to actively respond to the epidemic risk caused by return to work and school. In addition, people from high-risk areas should be strictly monitored and managed.

Key words: Coronavirus disease 2019, Zhejiang Province, Epidemiological characteristics, Spatial autocorrelation, Cluster analysis

中图分类号: 

  • R181.3
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