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10.1590/1413-81232020259.17082020 http://scihub22266oqcxt.onion/10.1590/1413-81232020259.17082020 32876254!ä!32876254 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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\32876254.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Cien+Saude+Colet 2020 ; 25 (9): 3377-3384 Nephropedia Template TP {{tp|p=32876254|t=2020. Spatial analysis of the COVID-19 distribution pattern in Sao Paulo State, Brazil |pdf=|usr=}}{{32876254}} gab.com Text Spatial analysis of the COVID-19 distribution pattern in Sao Paulo State, Brazil JO: Cien Saude Colet http://www.kidney.de/pget.php?&tw=1&pmid=32876254 #FOAvip At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of Sao Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in Sao Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures. Twit Text FOAVip Spatial analysis of the COVID-19 distribution pattern in Sao Paulo State, Brazil ????Cien Saude Colet http://www.kidney.de/pget.php?&tw=1&pmid=32876254 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32876254 #FOAvip English Wikipedia <ref>{{Cite pmid|32876254}}</ref> Spatial analysis of the COVID-19 distribution pattern in Sao Paulo State, Brazil #MMPMID32876254 Rex FE; Borges CAS; Kafer PS Cien Saude Colet 2020[Sep]; 25 (9): 3377-3384 PMID32876254show ga At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of Sao Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in Sao Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures. |*Disease Outbreaks[MESH] |Brazil/epidemiology[MESH] |COVID-19[MESH] |Cities[MESH] |Coronavirus Infections/*epidemiology[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH] |Public Health[MESH]Spatial analysis of the COVID-19 distribution pattern in Sao Paulo Sta(epmc).pdf DeepDyve Pubget Overpricing