Your browser doesn't support javascript.
Spatiotemporal spread pattern of the COVID-19 cases in China.
Feng, Yongjiu; Li, Qingmei; Tong, Xiaohua; Wang, Rong; Zhai, Shuting; Gao, Chen; Lei, Zhenkun; Chen, Shurui; Zhou, Yilun; Wang, Jiafeng; Yan, Xiongfeng; Xie, Huan; Chen, Peng; Liu, Shijie; Xv, Xiong; Liu, Sicong; Jin, Yanmin; Wang, Chao; Hong, Zhonghua; Luan, Kuifeng; Wei, Chao; Xu, Jinfu; Jiang, Hua; Xiao, Changjiang; Guo, Yiyou.
  • Feng Y; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Li Q; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Tong X; College of Engineering, Peking University, Beijing, China.
  • Wang R; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Zhai S; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Gao C; College of Marine Sciences, Shanghai Ocean University, Shanghai, China.
  • Lei Z; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Chen S; College of Marine Sciences, Shanghai Ocean University, Shanghai, China.
  • Zhou Y; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Wang J; College of Marine Sciences, Shanghai Ocean University, Shanghai, China.
  • Yan X; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Xie H; College of Marine Sciences, Shanghai Ocean University, Shanghai, China.
  • Chen P; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Liu S; College of Marine Sciences, Shanghai Ocean University, Shanghai, China.
  • Xv X; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Liu S; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Jin Y; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Wang C; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Hong Z; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Luan K; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Wei C; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Xu J; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Jiang H; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Xiao C; College of Surveying and Geo-Informatics, Tongji University, Shanghai, China.
  • Guo Y; College of Marine Sciences, Shanghai Ocean University, Shanghai, China.
PLoS One ; 15(12): e0244351, 2020.
Article in English | MEDLINE | ID: covidwho-1004462
ABSTRACT
The COVID-19 pandemic is currently spreading widely around the world, causing huge threats to public safety and global society. This study analyzes the spatiotemporal pattern of the COVID-19 pandemic in China, reveals China's epicenters of the pandemic through spatial clustering, and delineates the substantial effect of distance to Wuhan on the pandemic spread. The results show that the daily new COVID-19 cases mostly occurred in and around Wuhan before March 6, and then moved to the Grand Bay Area (Shenzhen, Hong Kong and Macau). The total COVID-19 cases in China were mainly distributed in the east of the Huhuanyong Line, where the epicenters accounted for more than 60% of the country's total in/on 24 January and 7 February, half in/on 31 January, and more than 70% from 14 February. The total cases finally stabilized at approximately 84,000, and the inflection point for Wuhan was on 14 February, one week later than those of Hubei (outside Wuhan) and China (outside Hubei). The generalized additive model-based analysis shows that population density and distance to provincial cities were significantly associated with the total number of the cases, while distances to prefecture cities and intercity traffic stations, and population inflow from Wuhan after 24 January, had no strong relationships with the total number of cases. The results and findings should provide valuable insights for understanding the changes in the COVID-19 transmission as well as implications for controlling the global COVID-19 pandemic spread.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 / Models, Biological Type of study: Observational study Limits: Humans Country/Region as subject: Asia Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2020 Document Type: Article Affiliation country: Journal.pone.0244351

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 / Models, Biological Type of study: Observational study Limits: Humans Country/Region as subject: Asia Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2020 Document Type: Article Affiliation country: Journal.pone.0244351