Journal of Geographical Sciences ›› 2020, Vol. 30 ›› Issue (12): 1985-2001.doi: 10.1007/s11442-020-1823-7

• Research Articles • Previous Articles     Next Articles

Spatiotemporal analysis of COVID-19 risk in Guangdong Province based on population migration

YE Yuyao1, WANG Changjian1(), ZHANG Hong’ou1, YANG Ji1,2, LIU Zhengqian1,3, WU Kangmin1, DENG Yingbin1   

  1. 1. Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
    2. Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou 511458, China
    3. School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China
  • Received:2020-03-09 Accepted:2020-09-28 Online:2020-12-25 Published:2021-01-05
  • About author:Wang Changjian (1986-), Associate Professor, specialized in economic geography and sustainability. E-mail: wwwangcj@126.com
  • Supported by:
    National Key Research and Development Program(2019YFB2103101);GDAS Special Project of Science and Technology Development(2020GDASYL-20200301003);Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)(GML2019ZD0301)

Abstract:

Population migration, especially population inflow from epidemic areas, is a key source of the risk related to the coronavirus disease 2019 (COVID-19) epidemic. This paper selects Guangdong Province, China, for a case study. It utilizes big data on population migration and the geospatial analysis technique to develop a model to achieve spatiotemporal analysis of COVID-19 risk. The model takes into consideration the risk differential between the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities of population migration. It further incorporates a time-lag process based on the time distribution of the onset of the imported cases. In theory, the model will be able to predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and provide support for advanced planning and targeted prevention measures. The research findings indicate the following: (1) The COVID-19 epidemic in Guangdong Province reached a turning point on January 29, 2020, after which it showed a gradual decreasing trend. (2) Based on the time-lag analysis of the onset of the imported cases, it is common for a time interval to exist between case importation and illness onset, and the proportion of the cases with an interval of 1-14 days is relatively high. (3) There is evident spatial heterogeneity in the epidemic risk; the risk varies significantly between different areas based on their imported risk, susceptibility risk, and ability to prevent the spread. (4) The degree of connectedness and the scale of population migration between Guangdong’s prefecture-level cities and their counterparts in the source regions of the epidemic, as well as the transportation and location factors of the cities in Guangdong, have a significant impact on the risk classification of the cities in Guangdong. The first-tier cities - Shenzhen and Guangzhou - are high-risk regions. The cities in the Pearl River Delta that are adjacent to Shenzhen and Guangzhou, including Dongguan, Foshan, Huizhou, Zhuhai, Zhongshan, are medium-risk cities. The eastern, northern, and western parts of Guangdong, which are outside of the metropolitan areas of the Pearl River Delta, are considered to have low risks. Therefore, the government should develop prevention and control measures that are specific to different regions based on their risk classification to enable targeted prevention and ensure the smooth operation of society.

Key words: population migration, COVID-19, epidemic risk, time-lag process, spatiotemporal analysis