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10.1093/pubmed/fdaa140 http://scihub22266oqcxt.onion/10.1093/pubmed/fdaa140 32808010!7454828!32808010     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Public+Health+(Oxf) 2021 ; 43 (1): 98-106 Nephropedia Template TP {{tp|p=32808010|t=2021. A first insight about spatial dimension of COVID-19: analysis at municipality level |pdf=|usr=}}{{32808010}} gab.com Text A first insight about spatial dimension of COVID-19: analysis at municipality level JO: J Public Health (Oxf) http://www.kidney.de/pget.php?&tw=1&pmid=32808010 #FOAvip BACKGROUND: This paper is about spatial patterns of by corona virus disease-2019 (COVID-19). METHODS: Using data for the first 21 weeks from municipalities in Catalonia, we analyse whether reported positive cases appear randomly or following some kind of spatial dependence. Global and local measures of spatial autocorrelation are used. RESULTS: There are some clusters alongside Catalan municipalities that change over time. CONCLUSIONS: Use of spatial analysis techniques is suggested to identify spatial disease patterns and to provide spatially disaggregated public health policy recommendations. Twit Text FOAVip A first insight about spatial dimension of COVID-19: analysis at municipality level ????J Public Health (Oxf) http://www.kidney.de/pget.php?&tw=1&pmid=32808010 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32808010 #FOAvip English Wikipedia <ref>{{Cite pmid|32808010}}</ref> A first insight about spatial dimension of COVID-19: analysis at municipality level #MMPMID32808010 Arauzo-Carod JM J Public Health (Oxf) 2021[Apr]; 43 (1): 98-106 PMID32808010show ga BACKGROUND: This paper is about spatial patterns of by corona virus disease-2019 (COVID-19). METHODS: Using data for the first 21 weeks from municipalities in Catalonia, we analyse whether reported positive cases appear randomly or following some kind of spatial dependence. Global and local measures of spatial autocorrelation are used. RESULTS: There are some clusters alongside Catalan municipalities that change over time. CONCLUSIONS: Use of spatial analysis techniques is suggested to identify spatial disease patterns and to provide spatially disaggregated public health policy recommendations. |COVID-19/*epidemiology[MESH] |Cities/epidemiology[MESH] |Humans[MESH] |Spain/epidemiology[MESH]A first insight about spatial dimension of COVID-19: analysis at munic(epmc).pdf DeepDyve Pubget Overpricing