地理学报

Acta Geographica Sinica ›› 2020, Vol. 75 ›› Issue (11): 2521-2534.doi: 10.11821/dlxb202011018

• Impacts of COVID-19 • Previous Articles     Next Articles

Spatio-temporal analysis of COVID-19 epidemic risk in Guangdong Province based on population migration

YE Yuyao1,2(), WANG Changjian1,2(), ZHANG Hong'ou1,2, YANG Ji1,2,3, LIU Zhengqian1,2,4, WU Kangmin1,2, DENG Yingbin1,2   

  1. 1. Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
    2. Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
    3. Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou 511458, China
    4. School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China
  • Received:2020-03-09 Revised:2020-10-26 Online:2020-11-25 Published:2021-01-25
  • Contact: WANG Changjian E-mail:yeyuyao@gdas.ac.cn;wwwangcj@126.com
  • Supported by:
    National Key R&D Program of China(2019YFB2103101);Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)(GML2019ZD0301);GDAS Special Project of Science and Technology Development(2020GDASYL-20200102002);GDAS Special Project of Science and Technology Development(2020GDASYL-20200301003)

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Abstract:

Population migration, especially population input from epidemic area, is a key source of the risk related to the COVID-19 epidemic. Taking Guangdong Province as an example, this paper utilizes big data on population migration and the geospatial analysis technique to develop a model to conduct spatiotemporal analysis of COVID-19 risk. The model considers the risk differences among the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities. It further incorporates a time-lag process based on the time distribution of the onset of the imported cases. The model can predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and support the future planning and targeted prevention measures. The research findings indicate that: (1) The COVID-19 epidemic in Guangdong reached a inflection point on January 29, 2020, and then it showed a gradual decline. (2) Based on the time-lag analysis of the onset of the imported cases, there is a time interval between the case importation and the illness onset, and the cases with an interval of 1-14 days account for a high proportion. (3) There are obvious spatial differences in the risk of epidemics, based on their imported risk, susceptibility risk, and risk resisting ability. (4) The connection and the scale of population migration as well as the transportation and location factors of the cities in Guangdong's prefecture-level cities and the source regions of the epidemic, all have significant impacts on the risk classification of the cities in the province. The first-tier cities such as Shenzhen and Guangzhou are the high-risk areas. The cities in the Pearl River Delta that are adjacent to Shenzhen and Guangzhou, including Dongguan, Foshan, Huizhou, Zhuhai and Zhongshan, are the medium-risk cities. The eastern, northern, and western parts of Guangdong, which are outside the metropolitan areas of the Pearl River Delta, are classed into low-risk areas. Therefore, the government should take targeted prevention and control measures in different regions based on local conditions and risk classification so as to ensure people's daily life and wellbeing to the greatest possible extent.

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