Journal Article

Spatiotemporal spread pattern of the COVID-19 cases in China

Luan, Kuifeng, Zhai, Shuting, Liu, Shijie, Gao, Chen, Tong, Xiaohua, Liu, Sicong, Wei, Chao, Chen, Shurui, Lei, Zhenkun, Xv, Xiong, Wang, Rong, Jin, Yanmin, Feng, Yongjiu, Xu, Jinfu, Yan, Xiongfeng, Jiang, Hua, Wang, Jiafeng, Xie, Huan, Xiao, Changjiang, Wang, Chao, Guo, Yiyou, Chen, Peng, Zhou, Yilun, Li, Qingmei, Hong, Zhonghua

2020

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Analysis / Biology and Life Sciences / China / Cities - epidemiology / Clustering / Coronaviruses / COVID-19 / COVID-19 - epidemiology / COVID-19 - transmission / Disease transmission / Distribution / Earth Sciences / Epidemics / Generalized linear models / Geospatial data / Hong Kong - epidemiology / Humans / Informatics / Macau - epidemiology / Mathematical models / Medicine and Health Sciences / Models, Biological / Pandemics / Pattern analysis / People and Places / Population density / Public safety / Social Sciences / Sociodemographics / Software / Spatial Analysis