Spatiotemporal spread pattern of the COVID-19 cases in China.

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...

Full description

Bibliographic Details
Main Authors: Yongjiu Feng, Qingmei Li, Xiaohua Tong, Rong Wang, Shuting Zhai, Chen Gao, Zhenkun Lei, Shurui Chen, Yilun Zhou, Jiafeng Wang, Xiongfeng Yan, Huan Xie, Peng Chen, Shijie Liu, Xiong Xv, Sicong Liu, Yanmin Jin, Chao Wang, Zhonghua Hong, Kuifeng Luan, Chao Wei, Jinfu Xu, Hua Jiang, Changjiang Xiao, Yiyou Guo
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0244351
_version_ 1819035544496111616
author Yongjiu Feng
Qingmei Li
Xiaohua Tong
Rong Wang
Shuting Zhai
Chen Gao
Zhenkun Lei
Shurui Chen
Yilun Zhou
Jiafeng Wang
Xiongfeng Yan
Huan Xie
Peng Chen
Shijie Liu
Xiong Xv
Sicong Liu
Yanmin Jin
Chao Wang
Zhonghua Hong
Kuifeng Luan
Chao Wei
Jinfu Xu
Hua Jiang
Changjiang Xiao
Yiyou Guo
author_facet Yongjiu Feng
Qingmei Li
Xiaohua Tong
Rong Wang
Shuting Zhai
Chen Gao
Zhenkun Lei
Shurui Chen
Yilun Zhou
Jiafeng Wang
Xiongfeng Yan
Huan Xie
Peng Chen
Shijie Liu
Xiong Xv
Sicong Liu
Yanmin Jin
Chao Wang
Zhonghua Hong
Kuifeng Luan
Chao Wei
Jinfu Xu
Hua Jiang
Changjiang Xiao
Yiyou Guo
author_sort Yongjiu Feng
collection DOAJ
description 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.
first_indexed 2024-12-21T07:51:19Z
format Article
id doaj.art-6dea811f17fe42c591f704e579d54ef4
institution Directory Open Access Journal
issn 1932-6203
language English
last_indexed 2024-12-21T07:51:19Z
publishDate 2020-01-01
publisher Public Library of Science (PLoS)
record_format Article
series PLoS ONE
spelling doaj.art-6dea811f17fe42c591f704e579d54ef42022-12-21T19:11:05ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011512e024435110.1371/journal.pone.0244351Spatiotemporal spread pattern of the COVID-19 cases in China.Yongjiu FengQingmei LiXiaohua TongRong WangShuting ZhaiChen GaoZhenkun LeiShurui ChenYilun ZhouJiafeng WangXiongfeng YanHuan XiePeng ChenShijie LiuXiong XvSicong LiuYanmin JinChao WangZhonghua HongKuifeng LuanChao WeiJinfu XuHua JiangChangjiang XiaoYiyou GuoThe 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.https://doi.org/10.1371/journal.pone.0244351
spellingShingle Yongjiu Feng
Qingmei Li
Xiaohua Tong
Rong Wang
Shuting Zhai
Chen Gao
Zhenkun Lei
Shurui Chen
Yilun Zhou
Jiafeng Wang
Xiongfeng Yan
Huan Xie
Peng Chen
Shijie Liu
Xiong Xv
Sicong Liu
Yanmin Jin
Chao Wang
Zhonghua Hong
Kuifeng Luan
Chao Wei
Jinfu Xu
Hua Jiang
Changjiang Xiao
Yiyou Guo
Spatiotemporal spread pattern of the COVID-19 cases in China.
PLoS ONE
title Spatiotemporal spread pattern of the COVID-19 cases in China.
title_full Spatiotemporal spread pattern of the COVID-19 cases in China.
title_fullStr Spatiotemporal spread pattern of the COVID-19 cases in China.
title_full_unstemmed Spatiotemporal spread pattern of the COVID-19 cases in China.
title_short Spatiotemporal spread pattern of the COVID-19 cases in China.
title_sort spatiotemporal spread pattern of the covid 19 cases in china
url https://doi.org/10.1371/journal.pone.0244351
work_keys_str_mv AT yongjiufeng spatiotemporalspreadpatternofthecovid19casesinchina
AT qingmeili spatiotemporalspreadpatternofthecovid19casesinchina
AT xiaohuatong spatiotemporalspreadpatternofthecovid19casesinchina
AT rongwang spatiotemporalspreadpatternofthecovid19casesinchina
AT shutingzhai spatiotemporalspreadpatternofthecovid19casesinchina
AT chengao spatiotemporalspreadpatternofthecovid19casesinchina
AT zhenkunlei spatiotemporalspreadpatternofthecovid19casesinchina
AT shuruichen spatiotemporalspreadpatternofthecovid19casesinchina
AT yilunzhou spatiotemporalspreadpatternofthecovid19casesinchina
AT jiafengwang spatiotemporalspreadpatternofthecovid19casesinchina
AT xiongfengyan spatiotemporalspreadpatternofthecovid19casesinchina
AT huanxie spatiotemporalspreadpatternofthecovid19casesinchina
AT pengchen spatiotemporalspreadpatternofthecovid19casesinchina
AT shijieliu spatiotemporalspreadpatternofthecovid19casesinchina
AT xiongxv spatiotemporalspreadpatternofthecovid19casesinchina
AT sicongliu spatiotemporalspreadpatternofthecovid19casesinchina
AT yanminjin spatiotemporalspreadpatternofthecovid19casesinchina
AT chaowang spatiotemporalspreadpatternofthecovid19casesinchina
AT zhonghuahong spatiotemporalspreadpatternofthecovid19casesinchina
AT kuifengluan spatiotemporalspreadpatternofthecovid19casesinchina
AT chaowei spatiotemporalspreadpatternofthecovid19casesinchina
AT jinfuxu spatiotemporalspreadpatternofthecovid19casesinchina
AT huajiang spatiotemporalspreadpatternofthecovid19casesinchina
AT changjiangxiao spatiotemporalspreadpatternofthecovid19casesinchina
AT yiyouguo spatiotemporalspreadpatternofthecovid19casesinchina