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Spatial disparities of self-reported COVID-19 cases and influencing factors in Wuhan, China.
Xu, Gang; Jiang, Yuhan; Wang, Shuai; Qin, Kun; Ding, Jingchen; Liu, Yang; Lu, Binbin.
  • Xu G; School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
  • Jiang Y; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
  • Wang S; Wuhan Geomatics Institute, Wansongyuan Road, Wuhan 430022, China.
  • Qin K; School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
  • Ding J; School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
  • Liu Y; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
  • Lu B; School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
Sustain Cities Soc ; 76: 103485, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1492614
ABSTRACT
The lack of detailed COVID-19 cases at a fine spatial resolution restricts the investigation of spatial disparities of its attack rate. Here, we collected nearly one thousand self-reported cases from a social media platform during the early stage of COVID-19 epidemic in Wuhan, China. We used kernel density estimation (KDE) to explore spatial disparities of epidemic intensity and adopted geographically weighted regression (GWR) model to quantify influences of population dynamics, transportation, and social interactions on COVID-19 epidemic. Results show that self-reported COVID-19 cases concentrated in commercial centers and populous residential areas. Blocks with higher population density, higher aging rate, more metro stations, more main roads, and more commercial point-of-interests (POIs) have a higher density of COVID-19 cases. These five explanatory variables explain 76% variance of self-reported cases using an OLS model. Commercial POIs have the strongest influence, which increase COVID-19 cases by 28% with one standard deviation increase. The GWR model performs better than OLS model with the adjusted R 2 of 0.96. Spatial heterogeneities of coefficients in the GWR model show that influencing factors play different roles in diverse communities. We further discussed potential implications for the healthy city and urban planning for the sustainable development of cities.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Sustain Cities Soc Year: 2022 Document Type: Article Affiliation country: J.scs.2021.103485

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Sustain Cities Soc Year: 2022 Document Type: Article Affiliation country: J.scs.2021.103485