地理学报 ›› 2022, Vol. 77 ›› Issue (2): 443-456.doi: 10.11821/dlxb202202012

• 人口地理 • 上一篇    下一篇

中国COVID-19疫情扩散的时空模式及影响因素

王博云1(), 刘天禹1, 李露凝1, 李强1(), 贾鹏飞2, 陈晋1   

  1. 1.北京师范大学地理科学学部,北京 100875
    2.中国城市规划设计研究院,北京 100044
  • 收稿日期:2020-12-30 修回日期:2021-09-17 出版日期:2022-02-25 发布日期:2022-04-25
  • 通讯作者: 李强(1967-), 女, 博士, 教授, 博士生导师, 研究方向为区域规划与资源管理、城市交通需要管理。E-mail: liqiang@bnu.edu.cn
  • 作者简介:王博云(1998-), 女, 汉族, 硕士生, 研究方向为土地资源与区域发展。E-mail: wangboyun@mail.bnu.edu.cn
  • 基金资助:
    国家重点研发计划(2017YFC1503004)

Spatial and temporal patterns and factors influencing the spread of the COVID-19 pandemic in China

WANG Boyun1(), LIU Tianyu1, LI Luning1, LI Qiang1(), JIA Pengfei2, CHEN Jin1   

  1. 1. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    2. China Academy of Urban Planning and Design, Beijing 100044, China
  • Received:2020-12-30 Revised:2021-09-17 Published:2022-02-25 Online:2022-04-25
  • Supported by:
    National Key R&D Program of China(2017YFC1503004)

摘要:

在2020年全球暴发新型冠状病毒肺炎(COVID-19)疫情的背景下,揭示中国疫情扩散时空模式及影响因素对于科学制定防疫策略具有重要作用。针对2020年1月24日—3月18日期间中国COVID-19疫情从快速扩散到逐步控制的完整过程,基于累计确诊病例数据,以317个地级市为对象,建立疫情扩散时空模式判别模型,结合峰位置、半峰间距、峰度、偏度等参数,解析时空模式的基本特征;基于交通可达性、城市关联程度和人口流动构建多元Logistic回归模型,揭示时空模式的关键影响因素。结果显示:① 距武汉市直线距离588 km为判别疫情扩散4种空间模式的有效边界,综合同一空间模式下的时间过程类别,得到13类疫情扩散时空模式。② 蛙跳型的疫情扩散相对严重;除近距离蛙跳型以外,其余空间模式的疫情扩散时间过程差异明显;各种时空模式的新增确诊病例峰值大多为2020年2月3日;所有普通类城市的平均半峰间距约为14 d,与COVID-19病毒的潜伏期一致。③ 与武汉市的人口关联度主要影响蔓延型和近距离蛙跳型空间模式,与武汉市的通航状况对远距离蛙跳型空间模式具有正向影响,迁出人口数量对蛙跳型空间模式有显著作用,综合型空间模式受初级和次级疫情暴发地的双重影响。不同城市应根据自身的疫情扩散时空模式,在疫情期间高度重视交通管控,从关键环节遏制疫情扩散。

关键词: COVID-19疫情, 时空模式, 时间过程一致性, 多元Logistic回归模型, 中国

Abstract:

It is essential to unravel the spatial and temporal patterns of the spread of the epidemic in China during the backdrop of the global coronavirus disease 2019 (COVID-19) outbreak in 2020, as the underlying drivers are crucial for scientific formulation of epidemy-preventing strategies. A discriminant model for the spatio-temporal pattern of epidemic spread was developed for 317 prefecture-level cities using accumulated data on confirmed cases. The model was introduced for the real-time evolution of the outbreak starting from the rapid spread of COVID-19 on January 24, 2020, until the control on March 18, 2020. The model was used to analyze the basic characteristics of the spatio-temporal patterns of the epidemic spread by combining parameters such as peak position, full width at half maximum, kurtosis, and skewness. A multivariate logistic regression model was developed to unravel the key drivers of the spatio-temporal patterns based on traffic accessibility, urban connectivity, and population flow. The results of the study are as follows. (1) The straight-line distance of 588 km from Wuhan was used as the effective boundary to identify the four spatial patterns of epidemic spread, and 13 types of spatio-temporal patterns were obtained by combining the time-course categories of the same spatial pattern. (2) The spread of the epidemic was relatively severe in the leapfrogging model. Besides the short-distance leapfrogging model, significant differences emerged in the spatial patterns of the time course of epidemic spread. The peaks of the new confirmed cases in various spatio-temporal patterns were mostly observed on February 3, 2020. The average full widths at the half maximum of all ordinary cities were approximately 14 days, thus, resonating with the incubation period of the COVID-19 virus. (3) The degree of the population correlation with Wuhan city has mainly influenced the spreading and the short-distance leapfrogging spatial patterns. The existence of direct flight from Wuhan city exhibited a positive effect on the long-distance leapfrogging spatial pattern. The number of population outflows has significantly affected the leapfrogging spatial pattern. The integrated spatial pattern was influenced by both primary and secondary epidemic outbreak sites. Thus, cities should pay great attention to traffic control during the epidemic as analysis has shown that the spatio-temporal patterns of epidemic spread in the respective cities can curb the spread of the epidemic from key links.

Key words: COVID-19 outbreak, spatio-temporal pattern, time-course consistency, multivariate logistic regression model, China