上海交通大学学报(医学版) ›› 2020, Vol. 40 ›› Issue (05): 566-572.doi: 10.3969/j.issn.1674-8115.2020.05.002

• 新型冠状病毒防控专栏 • 上一篇    

荆州市新型冠状病毒肺炎时空分布特征分析

蔺茂文*,刘 天*,田克卿,江 鸿,曾旻敏,王 丽,殷 俊,雷若倩,姚梦雷,黄继贵   

  1. 湖北省荆州市疾病预防控制中心,荆州 434000
  • 出版日期:2020-05-28 发布日期:2020-07-07
  • 通讯作者: 黄继贵,电子信箱:704640420@qq.com。
  • 作者简介:蔺茂文(1987—),男,主治医师,学士;电子信箱:jzcdcpd@163.com。刘 天(1992—),男,住院医师,学士;电子信箱:jzcdclt@163.com。*为共同第一作者。

Spatial-temporal distribution of coronavirus disease 2019 in Jingzhou City

LIN Mao-wen*, LIU Tian*, TIAN Ke-qing, JIANG Hong, ZENG Min-min, WANG Li, YIN Jun, LEI Ruo-qian, YAO Meng-lei, HUANG Ji-gui   

  1. Jingzhou Municipal Center for Disease Control and Prevention, Hubei Province, Jingzhou 434000, China
  • Online:2020-05-28 Published:2020-07-07

摘要: 目的·探讨荆州市新型冠状病毒肺炎的空间分布特征及时空聚集性。方法·收集荆州市2020年1月1日—3月12日的新型冠状病毒肺炎病例信息,采用趋势面分析、空间自相关分析和时空扫描分析对荆州市新型冠状病毒肺炎报告的乡镇(街道)级数据进行分析,比较本地病例和输入病例的时空聚集特征。结果·趋势面分析得出荆州市新型冠状病毒肺炎发病率自西向东呈轻微“U”形,东部略高;自南向北呈倒“U”型,且南部略高。全局空间自相关分析表明荆州市新型冠状病毒肺炎发病呈空间正相关(Moran′s I=0.410,P=0.000);局部空间自相关分析显示,新型冠状病毒肺炎高值聚集区和热点区域主要集中在沙市区、荆州区和洪湖市主城区(新堤街道)(P<0.05)。输入病例时空扫描得到5个聚集区域,主聚集区聚集时间为2020年1月18日—2月3日,聚集区域以联合街道为中心,涵盖沙市区、荆州区的15个乡镇(街道)(LLR=174.944,RR=7.395,P=0.000)。本地病例时空扫描得到5个聚集区域,主聚集区聚集时间为2020年1月20日—2月24日,聚集区域为洪湖市新堤街道(LLR=224.434,RR=16.133,P=0.000)。结论·荆州市新型冠状病毒肺炎存在时空聚集性,沙市区、荆州区、洪湖市为疫情高发地区。

关键词: 新型冠状病毒肺炎, 空间自相关, 时空扫描, 趋势面分析

Abstract: Objective · To explore the spatial distribution and spatial-temporal clustering of coronavirus disease 2019 (COVID-19) in Jingzhou City. Methods · Data of COVID-19 cases in Jingzhou City from January 1 to March 12, 2020 were collected. Trend surface analysis, spatial autocorrelation and spatial-temporal scanning analysis were conducted to understand the spatial-temporal distribution of COVID-19 at town (street) level in Jingzhou City, and the spatial-temporal clustering characteristics of local cases and imported cases were compared. Results · Trend surface analysis showed that the incidence rate of COVID-19 in Jingzhou City was slightly “U” from west to east, slightly higher in the east, and inverted “U” from south to north, slightly higher in the south. Global autocorrelation showed that the incidence rate of COVID-19 in Jingzhou City was positively correlated (Moran's I=0.410, P=0.000). Local spatial autocorrelation analysis showed that the highly clustered areas and hot spot areas were mainly in Shashi District, Jingzhou District and the main urban area of Honghu City (Xindi Street) (P<0.05). Five clusters were found by spatial-temporal scanning of imported cases. The cluster time of the main cluster was from January 18 to February 3, 2020, and it was centered on Lianhe Street, covering 15 towns (streets) in Shashi District and Jingzhou District (LLR=174.944, RR=7.395, P=0.000). Five clusters were found by spatial-temporal scanning of local cases. The cluster time of the main cluster was from January 20 to February 24, 2020, which was located in Xindi Street, Honghu City (LLR=224.434, RR=16.133, P=0.000). Conclusion · Obvious spatial-temporal clustering of COVID-19 was found in Jingzhou City, and Shashi District, Jingzhou District and Honghu City were the most prevalent areas.

Key words: coronavirus disease 2019 (COVID-19), spatial autocorrelation, spatial-temporal scanning, trend surface analysis

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