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Understanding the spatial diffusion dynamics of the COVID-19 pandemic in the city system in China.
Gu, Lijuan; Yang, Linsheng; Wang, Li; Guo, Yanan; Wei, Binggan; Li, Hairong.
  • Gu L; Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. Electronic address: gulj@igsnrr.ac.cn.
  • Yang L; Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China. Electronic address: yangls@igsnrr.ac.c
  • Wang L; Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. Electronic address: wangl@igsnrr.ac.cn.
  • Guo Y; Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. Electronic address: guoyn.14s@igsnrr.ac.cn.
  • Wei B; Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. Electronic address: Weibg@igsnrr.ac.cn.
  • Li H; Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. Electronic address: lihr@igsnrr.ac.cn.
Soc Sci Med ; 302: 114988, 2022 06.
Article in English | MEDLINE | ID: covidwho-1815178
ABSTRACT
Investigating the spatial epidemic dynamics of COVID-19 is crucial in understanding the routine of spatial diffusion and in surveillance, prediction, identification and prevention of another potential outbreak. However, previous studies attempting to evaluate these spatial diffusion dynamics are limited. Using city as the research unit and spatial association analysis as the primary strategy, this study explored the changing primary risk factors impacting the spatial spread of COVID-19 across Chinese cities under various diffusion assumptions and throughout the epidemic stage. Moreover, this study investigated the characteristics and geographical distributions of high-risk areas in different epidemic stages. The results empirically indicated rapid intercity diffusion at the early stage and primarily intracity diffusion thereafter. Before countermeasures took effect, proximity, GDP per capita, medical resources, outflows from Wuhan and intercity mobility significantly affected early diffusion. With speedily effective countermeasures, outflows from the epicenter, proximity, and intracity outflows played an important role. At the early stage, high-risk areas were mainly cities adjacent to the epicenter, with higher GDP per capita, or a combination of higher GDP per capita and better medical resources, with more outflow from the epicenter, or more intercity mobility. After countermeasures were effected, cities adjacent to the epicenter, or with more outflow from the epicenter or more intracity mobility became high-risk areas. This study provides an insightful understanding of the spatial diffusion of COVID-19 across cities. The findings are informative for effectively handling the potential recurrence of COVID-19 in various settings.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Soc Sci Med Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Soc Sci Med Year: 2022 Document Type: Article