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

doi:10.1007/s11442-020-1823-7

Abstract

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

YE Yuyao, WANG Changjian, ZHANG Hong’ou, YANG Ji, LIU Zhengqian, WU Kangmin, DENG Yingbin. Spatiotemporal analysis of COVID-19 risk in Guangdong Province based on population migration[J].Journal of Geographical Sciences, 2020, 30(12): 1985-2001.

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Variable	Explanation	Value range and type
Case number	Released by Shenzhen Health Commission
Sex	Patient’s sex	Male/female
Age	Patient’s age	Integer, 0-100
Place of residency	Province-City, e.g., Guangdong-Shenzhen
Time in Wuhan	Time period, YYYY/MM/DD-YYYY/MM/DD	Data/time period
Time of arrival at Shenzhen	YYYY/MM/DD	Date
Time of illness onset	YYYY/MM/DD	Date
Cause of infection	0: Resided in or visited Wuhan; 1: Resided in or visited a place in Hubei outside of Wuhan 2: Had close contact with a person who resided in or visited Hubei but did not reside in or visit Hubei; 3: Had close contact with a confirmed case but did not reside in or visit Hubei; NA: Unknown	0/1/2/3/NA

Variable	Unit	Minimum value	Maximum value	Mean	Standard deviation
Size of permanent population	10,000 person	189.1100	1490.4400	540.2857	340.7044
Size of mobile population	10,000 person	15.5563	875.4548	155.5160	233.0246
Number of health care institutions		838.0000	4598.0000	2453.6667	1067.6306
Number of hospital beds		6682.0000	95134.0000	24617.7619	19017.7786
Number of health care workers		13666.0000	188695.0000	43890.6190	40236.0240
Number of industrial firms		244.0000	7937.0000	2328.7143	2600.5551
Is there an airport	/	0	1	0.3810	0.4976
Traffic volume	10,000 vehicles	2189.95	107122.2201	18495.63725	27728.30962