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10.1016/j.dib.2021.106843 http://scihub22266oqcxt.onion/10.1016/j.dib.2021.106843 33589875!7865094!33589875     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       Data+Brief 2021 ; 35 (ä): 106843 Nephropedia Template TP {{tp|p=33589875|t=2021. Spatio-temporal dataset of COVID-19 outbreak in Mexico |pdf=|usr=}}{{33589875}} gab.com Text Spatio-temporal dataset of COVID-19 outbreak in Mexico JO: Data Brief http://www.kidney.de/pget.php?&tw=1&pmid=33589875 #FOAvip Our understanding of how COVID-19 spreads over a territory needs to be improved. For example, the evaluation of disease spatiotemporal distribution and its association with other characteristics can help identify covariates, model the behavior of the epidemic, and provide useful information for decision making. Data were compiled from the National Population Council (CONAPO), Google, the National Institute of Statistics and Geography (INEGI), and the Secretary of Health. The data describe the cases of COVID and characteristics of the population, such as distribution, mobility, and prevalence of chronic diseases such as diabetes, hypertension, and obesity. These data were processed to be compatible and georeferenced to a common geographic framework to facilitate spatial analysis in a geographic information system (GIS). Twit Text FOAVip Spatio-temporal dataset of COVID-19 outbreak in Mexico ????Data Brief http://www.kidney.de/pget.php?&tw=1&pmid=33589875 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33589875 #FOAvip English Wikipedia <ref>{{Cite pmid|33589875}}</ref> Spatio-temporal dataset of COVID-19 outbreak in Mexico #MMPMID33589875 Mas JF Data Brief 2021[Apr]; 35 (ä): 106843 PMID33589875show ga Our understanding of how COVID-19 spreads over a territory needs to be improved. For example, the evaluation of disease spatiotemporal distribution and its association with other characteristics can help identify covariates, model the behavior of the epidemic, and provide useful information for decision making. Data were compiled from the National Population Council (CONAPO), Google, the National Institute of Statistics and Geography (INEGI), and the Secretary of Health. The data describe the cases of COVID and characteristics of the population, such as distribution, mobility, and prevalence of chronic diseases such as diabetes, hypertension, and obesity. These data were processed to be compatible and georeferenced to a common geographic framework to facilitate spatial analysis in a geographic information system (GIS). äSpatio-temporal dataset of COVID-19 outbreak in Mexico (epmc).pdf DeepDyve Pubget Overpricing