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10.3390/ijerph17186712 http://scihub22266oqcxt.onion/10.3390/ijerph17186712 32942626!7558510!32942626     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2020 ; 17 (18): ä Nephropedia Template TP {{tp|p=32942626|t=2020. Associating COVID-19 Severity with Urban Factors: A Case Study of Wuhan |pdf=|usr=}}{{32942626}} gab.com Text Associating COVID-19 Severity with Urban Factors: A Case Study of Wuhan JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32942626 #FOAvip Wuhan encountered a serious attack in the first round of the coronavirus disease 2019 (COVID-19) pandemic, which has resulted in a public health social impact, including public mental health. Based on the Weibo help data, we inferred the spatial distribution pattern of the epidemic situation and its impacts. Seven urban factors, i.e., urban growth, general hospital, commercial facilities, subway station, land-use mixture, aging ratio, and road density, were selected for validation with the ordinary linear model, in which the former six factors presented a globally significant association with epidemic severity. Then, the geographically weighted regression model (GWR) was adopted to identify their unevenly distributed effects in the urban space. Among the six factors, the distribution and density of major hospitals exerted significant effects on epidemic situation. Commercial facilities appear to be the most prevalently distributed significant factor on epidemic situation over the city. Urban growth, in particular the newly developed residential quarters with high-rise buildings around the waterfront area of Hanyang and Wuchang, face greater risk of the distribution. The influence of subway stations concentrates at the adjacency place where the three towns meet and some near-terminal locations. The aging ratio of the community dominantly affects the hinterland of Hankou to a broader extent than other areas in the city. Upon discovering the result, a series of managerial implications that coordinate various urban factors were proposed. This research may contribute toward developing specific planning and design responses for different areas in the city based on a better understanding of the occurrence, transmission, and diffusion of the COVID-19 epidemic in the metropolitan area. Twit Text FOAVip Associating COVID-19 Severity with Urban Factors: A Case Study of Wuhan ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32942626 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32942626 #FOAvip English Wikipedia <ref>{{Cite pmid|32942626}}</ref> Associating COVID-19 Severity with Urban Factors: A Case Study of Wuhan #MMPMID32942626 Li X; Zhou L; Jia T; Peng R; Fu X; Zou Y Int J Environ Res Public Health 2020[Sep]; 17 (18): ä PMID32942626show ga Wuhan encountered a serious attack in the first round of the coronavirus disease 2019 (COVID-19) pandemic, which has resulted in a public health social impact, including public mental health. Based on the Weibo help data, we inferred the spatial distribution pattern of the epidemic situation and its impacts. Seven urban factors, i.e., urban growth, general hospital, commercial facilities, subway station, land-use mixture, aging ratio, and road density, were selected for validation with the ordinary linear model, in which the former six factors presented a globally significant association with epidemic severity. Then, the geographically weighted regression model (GWR) was adopted to identify their unevenly distributed effects in the urban space. Among the six factors, the distribution and density of major hospitals exerted significant effects on epidemic situation. Commercial facilities appear to be the most prevalently distributed significant factor on epidemic situation over the city. Urban growth, in particular the newly developed residential quarters with high-rise buildings around the waterfront area of Hanyang and Wuchang, face greater risk of the distribution. The influence of subway stations concentrates at the adjacency place where the three towns meet and some near-terminal locations. The aging ratio of the community dominantly affects the hinterland of Hankou to a broader extent than other areas in the city. Upon discovering the result, a series of managerial implications that coordinate various urban factors were proposed. This research may contribute toward developing specific planning and design responses for different areas in the city based on a better understanding of the occurrence, transmission, and diffusion of the COVID-19 epidemic in the metropolitan area. |*Built Environment[MESH] |*Urban Health[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |China[MESH] |Cities[MESH] |Coronavirus Infections/*epidemiology[MESH] |Environment Design[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH]Associating COVID-19 Severity with Urban Factors: A Case Study of Wuha(epmc).pdf DeepDyve Pubget Overpricing