太阳成8722(中国)有限公司-GREEN NO.1

标题: Space-Time Cluster's Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties

作者: Zhang, JT (Zhang, Jinting); Wu, X (Wu, Xiu); Chow, TE (Chow, T. Edwin)

来源出版物: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 卷: 18 期: 11 文献号: 5541 DOI: 10.3390/ijerph18115541 出版年: JUN 2021

摘要: As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020 and the first quarter in 2021. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental impact's indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, adult population, natural supply, economic condition, air quality or medical care. We established the GWR model to seek the sensitive factors. The result shows that adult population, economic condition, air quality, and medical care are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).

入藏号: WOS:000659963300001

PubMed ID: 34067291

语言: English

文献类型: Article

作者关键词: geographical weighted regression; space-time cluster's detection; COVID-19; mortality

地址: [Zhang, Jinting] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Wu, Xiu; Chow, T. Edwin] Texas State Univ, Dept Geog, San Marcos, TX 78666 USA.

通讯作者地址: Wu, X (通讯作者)，Texas State Univ, Dept Geog, San Marcos, TX 78666 USA.

电子邮件地址: whuzjt@whu.edu.cn; x_w10@txstate.edu; chow@txstate.edu

影响因子：2.849