Population flow drives spatio-temporal distribution of COVID-19 in China

doi:10.1038/s41586-020-2284-y

GSTDTAP > 地球科学

DOI	10.1038/s41586-020-2284-y
	Population flow drives spatio-temporal distribution of COVID-19 in China
	Fernandez, Diego Carlos 1; Komal, Ruchi 1; Langel, Jennifer 1; Ma, Jun 1; Duy, Phan Q.1,3; Penzo, Mario A.1; Zhao, Haiqing 2; Hattar, Samer 1
	2020-05-01
发表期刊	NATURE
ISSN	0028-0836
EISSN	1476-4687
出版年	2020
文章类型	Article;Early Access
语种	英语
国家	Peoples R China; Sweden; USA
英文关键词	Sudden, large-scale and diffuse human migration can amplify localized outbreaks of disease into widespread epidemics(1-4). Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. Here we use 11,478,484 counts of mobile phone data from individuals leaving or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout mainland China. First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographical distribution of infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) until 19 February 2020, across mainland China. Third, we develop a spatio-temporal ' risk source' model that leverages population flow data (which operationalize the risk that emanates from epidemic epicentres) not only to forecast the distribution of confirmed cases, but also to identify regions that have a high risk of transmission at an early stage. Fourth, we use this risk source model to statistically derive the geographical spread of COVID-19 and the growth pattern based on the population outflow from Wuhan the model yields a benchmark trend and an index for assessing the risk of community transmission of COVID-19 over time for different locations. This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan the allocation of limited resources ahead of ongoing outbreaks. Modelling of population flows in China enables the forecasting of the distribution of confirmed cases of COVID-19 and the identification of areas at high risk of SARS-CoV-2 transmission at an early stage.
领域	地球科学 ; 气候变化 ; 资源环境
收录类别	SCI-E
WOS记录号	WOS:000541034400001
WOS关键词	HUMAN MOBILITY ; TRANSPORTATION ; PREDICTABILITY ; NETWORK
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/281032
专题	地球科学资源环境科学气候变化
作者单位	1.NIMH, NIH, Bethesda, MD 20892 USA; 2.Johns Hopkins Univ, Dept Biol, Baltimore, MD 21218 USA; 3.Yale Univ, MSTP, New Haven, CT USA
推荐引用方式 GB/T 7714	Fernandez, Diego Carlos,Komal, Ruchi,Langel, Jennifer,et al. Population flow drives spatio-temporal distribution of COVID-19 in China[J]. NATURE,2020.
APA	Fernandez, Diego Carlos.,Komal, Ruchi.,Langel, Jennifer.,Ma, Jun.,Duy, Phan Q..,...&Hattar, Samer.(2020).Population flow drives spatio-temporal distribution of COVID-19 in China.NATURE.
MLA	Fernandez, Diego Carlos,et al."Population flow drives spatio-temporal distribution of COVID-19 in China".NATURE (2020).