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10.1016/j.scitotenv.2020.138884 http://scihub22266oqcxt.onion/10.1016/j.scitotenv.2020.138884 32335404!7175907!32335404     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Total+Environ 2020 ; 728 (ä): 138884 Nephropedia Template TP {{tp|p=32335404|t=2020. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States |pdf=|usr=}}{{32335404}} gab.com Text GIS-based spatial modeling of COVID-19 incidence rate in the continental United States JO: Sci Total Environ http://www.kidney.de/pget.php?&tw=1&pmid=32335404 #FOAvip During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R(2): 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions. Twit Text FOAVip GIS-based spatial modeling of COVID-19 incidence rate in the continental United States ????Sci Total Environ http://www.kidney.de/pget.php?&tw=1&pmid=32335404 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32335404 #FOAvip English Wikipedia <ref>{{Cite pmid|32335404}}</ref> GIS-based spatial modeling of COVID-19 incidence rate in the continental United States #MMPMID32335404 Mollalo A; Vahedi B; Rivera KM Sci Total Environ 2020[Aug]; 728 (ä): 138884 PMID32335404show ga During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R(2): 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions. |*Geographic Information Systems[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology[MESH] |Demography[MESH] |Environment[MESH] |Humans[MESH] |Incidence[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH] |SARS-CoV-2[MESH] |Socioeconomic Factors[MESH] |Spatial Analysis[MESH] |Spatial Regression[MESH]GIS-based spatial modeling of COVID-19 incidence rate in the continent(epmc).pdf DeepDyve Pubget Overpricing