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10.1038/s41598-021-86987-5 http://scihub22266oqcxt.onion/10.1038/s41598-021-86987-5 33846443!8041785!33846443     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 7890 Nephropedia Template TP {{tp|p=33846443|t=2021. Geographically varying relationships of COVID-19 mortality with different factors in India |pdf=|usr=}}{{33846443}} gab.com Text Geographically varying relationships of COVID-19 mortality with different factors in India JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33846443 #FOAvip COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ([Formula: see text]) with smaller Akaike Information Criterion (AICc [Formula: see text]) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran's [Formula: see text] and [Formula: see text]) in the residuals. It is found that more than 86% of local [Formula: see text] values are larger than 0.60 and almost 68% of [Formula: see text] values are within the range 0.80-0.97. Moreover, some interesting local variations in the relationships are also found. Twit Text FOAVip Geographically varying relationships of COVID-19 mortality with different factors in India ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33846443 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33846443 #FOAvip English Wikipedia <ref>{{Cite pmid|33846443}}</ref> Geographically varying relationships of COVID-19 mortality with different factors in India #MMPMID33846443 Middya AI; Roy S Sci Rep 2021[Apr]; 11 (1): 7890 PMID33846443show ga COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ([Formula: see text]) with smaller Akaike Information Criterion (AICc [Formula: see text]) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran's [Formula: see text] and [Formula: see text]) in the residuals. It is found that more than 86% of local [Formula: see text] values are larger than 0.60 and almost 68% of [Formula: see text] values are within the range 0.80-0.97. Moreover, some interesting local variations in the relationships are also found. |*Spatial Regression[MESH] |Algorithms[MESH] |COVID-19/*mortality[MESH] |Female[MESH] |Geography[MESH] |Humans[MESH] |India/epidemiology[MESH] |Least-Squares Analysis[MESH] |Male[MESH] |Regression Analysis[MESH] |Risk Factors[MESH] |Socioeconomic Factors[MESH]Geographically varying relationships of COVID-19 mortality with differ(epmc).pdf DeepDyve Pubget Overpricing