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博士生陈娒杰,陈玉敏的论文在SUSTAINABLE CITIES AND SOCIETY 刊出
发布时间:2022-12-02 15:32:36     发布者:易真     浏览次数:

标题: Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19

作者: Chen, MJ (Chen, Meijie); Chen, YM (Chen, Yumin); Xu, YQ (Xu, Yanqing); An, QY (An, Qianying); Min, WK (Min, Wankun)

来源出版物: SUSTAINABLE CITIES AND SOCIETY : 87 文献号: 104256 DOI: 10.1016/j.scs.2022.104256 出版年: DEC 2022

摘要: The COVID-19 pandemic has had great impact on human health and social economy. Several studies examined spatial and temporal patterns of health risk factors associated with COVID-19, but population flow spillover effect has not been sufficiently considered. In this paper, a population flow-based spatial-temporal eigenvector filtering model (FLOW-ESTF) was developed to consider spatial-temporal patterns and population flow con-nectivity simultaneously. The proposed FLOW-ESTF method efficiently improved model prediction accuracy, which could help the government aware of the infection risk level and to make suitable control policies. The selected population flow spatial-temporal eigenvector contributed most to modeling and the visualization of corresponding eigenvector set helped to explore the underlying spatial-temporal patterns and pandemic trans-mission nodes. The model coefficients could reflect how health risk factors contribute the modeling of state-level COVID-19 weekly increased cases and how their influence changed through time, which could help people and government to better aware the potential health risks and to adjust control measures at different stage. The extracted population flow spatial-temporal eigenvector not only represents influence of population flow and its spillover effects but also represents some possible omitted health risk factors. This could provide an efficient path to solve the problem of spatial and temporal autocorrelation in COVID-19 modeling and an intuitive way to discover underlying spatial patterns, which will partially compensate for the problems of insufficient consider-ation of potential risk variables and missing data.

作者关键词: COVID-19; Spatial -temporal; Eigenvector spatial filtering; Population flow network contiguity

地址: [Chen, Meijie; Chen, Yumin; An, Qianying; Min, Wankun] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

[Xu, Yanqing] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Hubei, Peoples R China.

通讯作者地址: Chen, YM (通讯作者),Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

电子邮件地址: ymchen@whu.edu.cn

影响因子:10.696

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