Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis
COVID-19 has seriously threatened people’s health and well-being across the globe since it was first reported in Wuhan, China in late 2019. This study investigates the mechanism of COVID-19 transmission in different periods within and between cities in China to better understand the nature of the ou...
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doaj-5ecc6f19d019497da054b401bcf03a142021-08-26T13:50:50ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-07-011051051010.3390/ijgi10080510Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model AnalysisHanchen Yu0Jingwei Li1Sarah Bardin2Hengyu Gu3Chenjing Fan4Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USASchool of Architecture and Design, Beijing Jiaotong University, Beijing 100044, ChinaSpatial Analysis Research Center, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USASchool of Government, Peking University, Beijing 100871, ChinaCollege of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaCOVID-19 has seriously threatened people’s health and well-being across the globe since it was first reported in Wuhan, China in late 2019. This study investigates the mechanism of COVID-19 transmission in different periods within and between cities in China to better understand the nature of the outbreak. We use Moran’s I, a measure of spatial autocorrelation, to examine the spatial dependency of COVID-19 and a dynamic spatial autoregressive model to explore the transmission mechanism. We find that the spatial dependency of COVID-19 decreased over time and that the transmission of the disease could be divided into three distinct stages: an eruption stage, a stabilization stage, and a declination stage. The infection rate between cities was close to one-third of the infection rate within cities at the eruption stage, while it reduced to zero at the declination stage. We also find that the infection rates within cities at the eruption stage and declination stage were similar. China’s policies for controlling the spread of the epidemic, specifically with respect to limiting inter-city mobility and implementing intra-city travel restrictions (social isolation), were most effective in reducing the viral transmission of COVID-19. The findings from this study indicate that the elimination of inter-city mobility had the largest impact on controlling disease transmission.https://www.mdpi.com/2220-9964/10/8/510COVID-19spatial dependencydynamic spatial autoregressive modelspatial diffusion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hanchen Yu Jingwei Li Sarah Bardin Hengyu Gu Chenjing Fan |
spellingShingle |
Hanchen Yu Jingwei Li Sarah Bardin Hengyu Gu Chenjing Fan Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis ISPRS International Journal of Geo-Information COVID-19 spatial dependency dynamic spatial autoregressive model spatial diffusion |
author_facet |
Hanchen Yu Jingwei Li Sarah Bardin Hengyu Gu Chenjing Fan |
author_sort |
Hanchen Yu |
title |
Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis |
title_short |
Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis |
title_full |
Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis |
title_fullStr |
Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis |
title_full_unstemmed |
Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis |
title_sort |
spatiotemporal dynamic of covid-19 diffusion in china: a dynamic spatial autoregressive model analysis |
publisher |
MDPI AG |
series |
ISPRS International Journal of Geo-Information |
issn |
2220-9964 |
publishDate |
2021-07-01 |
description |
COVID-19 has seriously threatened people’s health and well-being across the globe since it was first reported in Wuhan, China in late 2019. This study investigates the mechanism of COVID-19 transmission in different periods within and between cities in China to better understand the nature of the outbreak. We use Moran’s I, a measure of spatial autocorrelation, to examine the spatial dependency of COVID-19 and a dynamic spatial autoregressive model to explore the transmission mechanism. We find that the spatial dependency of COVID-19 decreased over time and that the transmission of the disease could be divided into three distinct stages: an eruption stage, a stabilization stage, and a declination stage. The infection rate between cities was close to one-third of the infection rate within cities at the eruption stage, while it reduced to zero at the declination stage. We also find that the infection rates within cities at the eruption stage and declination stage were similar. China’s policies for controlling the spread of the epidemic, specifically with respect to limiting inter-city mobility and implementing intra-city travel restrictions (social isolation), were most effective in reducing the viral transmission of COVID-19. The findings from this study indicate that the elimination of inter-city mobility had the largest impact on controlling disease transmission. |
topic |
COVID-19 spatial dependency dynamic spatial autoregressive model spatial diffusion |
url |
https://www.mdpi.com/2220-9964/10/8/510 |
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1721192813705560064 |