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10.1016/j.ufug.2021.127126 http://scihub22266oqcxt.onion/10.1016/j.ufug.2021.127126 33824634!8017915!33824634     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       Urban+For+Urban+Green 2021 ; 62 (ä): 127126 Nephropedia Template TP {{tp|p=33824634|t=2021. Using urban landscape pattern to understand and evaluate infectious disease risk |pdf=|usr=}}{{33824634}} gab.com Text Using urban landscape pattern to understand and evaluate infectious disease risk JO: Urban For Urban Green http://www.kidney.de/pget.php?&tw=1&pmid=33824634 #FOAvip COVID-19 case numbers in 161 sub-districts of Wuhan were investigated based on landscape epidemiology, and their landscape metrics were calculated based on land use/land cover (LULC). Initially, a mediation model verified a partially mediated population role in the relationship between landscape pattern and infection number. Adjusted incidence rate (AIR) and community safety index (CSI), two indicators for infection risk in sub-districts, were 25.82 approximately 63.56 per 10 thousand and 3.00 approximately 15.87 respectively, and central urban sub-districts were at higher infection risk. Geographically weighted regression (GWR) performed better than OLS regression with AICc differences of 7.951 approximately 181.261. The adjusted R(2) in GWR models of class-level index and infection risk were 0.697 to 0.817, while for the landscape-level index they were 0.668 to 0.835. Secondly, 16 key landscape metrics were identified based on GWR, and then a prediction model for infection risk in sub-districts and communities was developed. Using principal component analysis (PCA), development intensity, landscape level, and urban blue-green space were considered to be principal components affecting disease infection risk, explaining 73.1 % of the total variance. Cropland (PLAND and LSI), urban land (NP, LPI, and LSI) and unused land (NP) represent development intensity, greatly affecting infection risk in urban areas. Landscape level CONTAG, DIVISION, SHDI, and SHEI represent mobility and connectivity, having a profound impact on infection risk in both urban and suburban areas. Water (PLAND, NP, LPI, and LSI) and woodland (NP, and LSI) represent urban blue-green spaces, and were particularly important for infection risk in suburban areas. Based on urban landscape pattern, we proposed a framework to understand and evaluate infection risk. These findings provide a basis for risk evaluation and policy-making of urban infectious disease, which is significant for community management and urban planning for infectious disease worldwide. Twit Text FOAVip Using urban landscape pattern to understand and evaluate infectious disease risk ????Urban For Urban Green http://www.kidney.de/pget.php?&tw=1&pmid=33824634 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33824634 #FOAvip English Wikipedia <ref>{{Cite pmid|33824634}}</ref> Using urban landscape pattern to understand and evaluate infectious disease risk #MMPMID33824634 Ye Y; Qiu H Urban For Urban Green 2021[Jul]; 62 (ä): 127126 PMID33824634show ga COVID-19 case numbers in 161 sub-districts of Wuhan were investigated based on landscape epidemiology, and their landscape metrics were calculated based on land use/land cover (LULC). Initially, a mediation model verified a partially mediated population role in the relationship between landscape pattern and infection number. Adjusted incidence rate (AIR) and community safety index (CSI), two indicators for infection risk in sub-districts, were 25.82 approximately 63.56 per 10 thousand and 3.00 approximately 15.87 respectively, and central urban sub-districts were at higher infection risk. Geographically weighted regression (GWR) performed better than OLS regression with AICc differences of 7.951 approximately 181.261. The adjusted R(2) in GWR models of class-level index and infection risk were 0.697 to 0.817, while for the landscape-level index they were 0.668 to 0.835. Secondly, 16 key landscape metrics were identified based on GWR, and then a prediction model for infection risk in sub-districts and communities was developed. Using principal component analysis (PCA), development intensity, landscape level, and urban blue-green space were considered to be principal components affecting disease infection risk, explaining 73.1 % of the total variance. Cropland (PLAND and LSI), urban land (NP, LPI, and LSI) and unused land (NP) represent development intensity, greatly affecting infection risk in urban areas. Landscape level CONTAG, DIVISION, SHDI, and SHEI represent mobility and connectivity, having a profound impact on infection risk in both urban and suburban areas. Water (PLAND, NP, LPI, and LSI) and woodland (NP, and LSI) represent urban blue-green spaces, and were particularly important for infection risk in suburban areas. Based on urban landscape pattern, we proposed a framework to understand and evaluate infection risk. These findings provide a basis for risk evaluation and policy-making of urban infectious disease, which is significant for community management and urban planning for infectious disease worldwide. äUsing urban landscape pattern to understand and evaluate infectious di(epmc).pdf DeepDyve Pubget Overpricing