我国新型冠状病毒肺炎早期时空演变规律分析

高瑞; 于石成; 王琦琦; 周晓华; 刘楠堃; 谭枫

Abstract

高瑞,于石成,王琦琦,周晓华,刘楠堃,谭枫.我国新型冠状病毒肺炎早期时空演变规律分析[J].Chinese journal of Epidemiology,2022,43(3):297-304

我国新型冠状病毒肺炎早期时空演变规律分析

Spatiotemporal evolution of COVID-19 epidemic in the early phase in China

Received:December 17, 2021

DOI：10.3760/cma.j.cn112338-20211217-00996

KeyWord: 新型冠状病毒肺炎时空聚集性疫情发展演化

English Key Word: COVID-19 Spatiotemporal clustering Development and evolution of the epidemic

FundProject:基于新型数学和统计模型的新型冠状病毒（2019-nCoV）发生和发展规律的研究（82041023）

Author Name	Affiliation	E-mail
Gao Rui	Chinese Center for Disease Control and Prevention, Beijing 102206, China
Yu Shicheng	Chinese Center for Disease Control and Prevention, Beijing 102206, China
Wang Qiqi	Chinese Center for Disease Control and Prevention, Beijing 102206, China
Zhou Xiaohua	Peking University Health Science Center, Beijing 100191, China
Liu Nankun	Chinese Center for Disease Control and Prevention, Beijing 102206, China
Tan Feng	Chinese Center for Disease Control and Prevention, Beijing 102206, China	tanfeng@chinacdc.cn

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Abstract:

目的基于地理信息系统探索我国新型冠状病毒肺炎（COVID-19）起始阶段（疫情出现到武汉市解封）在地级行政区层面的时空聚集性及发展演化规律。方法收集2019年12月8日至2020年4月8日全国367个区域研究单元COVID-19确诊病例的相关信息和数据，应用GeoDa软件进行空间自相关分析，并将分析结果通过ArcGIS软件进行可视化；应用Satscan软件对COVID-19确诊情况进行时空扫描分析，确定疫情热点区域，并将其可视化。结果 2019年12月8日至2020年3月4日COVID-19发病率具有全局空间正相关性，局部空间自相关结果显示不同时间点的COVID-19发病率空间分布不同；2020年3月5日至4月8日全局空间自相关系数无统计学意义。时空扫描统计分析识别出2个时空聚集区，一级聚集区包括10个区域研究单元，主要聚集于湖北省，时间跨度为2020年1月13日至2月25日；二级聚集区包括142个区域研究单元，主要聚集于湖北省以北和以东的其他省份，时间跨度为2020年1月23日至2月1日。结论全国COVID-19起始阶段早期（2019年12月8日至2020年3月4日）疫情分布具有明显的时空聚集性；起始阶段后期（2020年3月5日至4月8日）随着病例基数的减少和疫情防控经验的成熟，地区间疫情不再具有明显关联性；研究结果与全国各省份应急响应等级的启动和调整时间节点一致。此外，缩短对新发疫情的认知响应期，采取及时有效的疫情防控措施对阻断疫情暴发具有重要作用。

English Abstract:

Objective Based on the geographic information systems, we exploreed the spatiotemporal clustering and the development and evolution of COVID-19 epidemic at prefectural level in China from the time when the epidemic was discovered to the time when the lockdown ended in Wuhan. Methods The information and data of the confirmed COVID-19 cases from December 8, 2019 to April 8, 2020 were collected from 367 prefectures in China for a spatial autocorrelation analysis with software GeoDa, and software ArcGIS was used to visualize the results. Software SatScan was used for spatiotemporal scanning analysis to visualize the hot-spot areas of the epidemic. Results The incidence of new cases of COVID-19 had obvious global autocorrelation and the partial autocorrelation results showed that incidence of COVID-19 had different spatial distribution at different times from December 8, 2019 to March 4, 2020. There was no significant difference in global autocorrelation coefficient from March 5, 2020 to April 8, 2020. The statistical analysis of spatiotemporal scanning identified two kinds of spatiotemporal clustering areas, the first class clustering areas included 10 prefectures, mainly distributed in Hubei, from January 13 to February 25, 2020. The secondary class clustering areas included 142 prefectures, mainly distributed in provinces in the north and east of Hubei, from January 23 to February 1, 2020. Conclusions There was a clear spatiotemporal correlation in the distribution of the outbreaks in the early phase of COVID-19 epidemic (December 8, 2019-March 4, 2020) in China. With the decrease of the case and effective prevention and control measures, the epidemics had no longer significant correlations among areas from March 5 to April 8. The study results showed relationship with time points of start and adjustment of emergency response at different degree in provinces. Furthermore, improving the early detection of new outbreaks and taking timely and effective prevention and control measures played an important role in blocking the transmission.

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