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10.1016/j.ibmed.2020.100002 http://scihub22266oqcxt.onion/10.1016/j.ibmed.2020.100002 32995759!7456591!32995759     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32995759&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Intell+Based+Med 2020 ; 1 (ä): 100002 Nephropedia Template TP {{tp|p=32995759|t=2020. Unsupervised learning for county-level typological classification for COVID-19 research |pdf=|usr=}}{{32995759}} gab.com Text Unsupervised learning for county-level typological classification for COVID-19 research JO: Intell Based Med http://www.kidney.de/pget.php?&tw=1&pmid=32995759 #FOAvip The analysis of county-level COVID-19 pandemic data faces computational and analytic challenges, particularly when considering the heterogeneity of data sources with variation in geographic, demographic, and socioeconomic factors between counties. This study presents a method to join relevant data from different sources to investigate underlying typological effects and disparities across typologies. Both consistencies within and variations between urban and non-urban counties are demonstrated. When different county types were stratified by age group distribution, this method identifies significant community mobility differences occurring before, during, and after the shutdown. Counties with a larger proportion of young adults (age 20-24) have higher baseline mobility and had the least mobility reduction during the lockdown. Twit Text FOAVip Unsupervised learning for county-level typological classification for COVID-19 research ????Intell Based Med http://www.kidney.de/pget.php?&tw=1&pmid=32995759 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32995759 #FOAvip English Wikipedia <ref>{{Cite pmid|32995759}}</ref> Unsupervised learning for county-level typological classification for COVID-19 research #MMPMID32995759 Lai Y; Charpignon ML; Ebner DK; Celi LA Intell Based Med 2020[Nov]; 1 (ä): 100002 PMID32995759show ga The analysis of county-level COVID-19 pandemic data faces computational and analytic challenges, particularly when considering the heterogeneity of data sources with variation in geographic, demographic, and socioeconomic factors between counties. This study presents a method to join relevant data from different sources to investigate underlying typological effects and disparities across typologies. Both consistencies within and variations between urban and non-urban counties are demonstrated. When different county types were stratified by age group distribution, this method identifies significant community mobility differences occurring before, during, and after the shutdown. Counties with a larger proportion of young adults (age 20-24) have higher baseline mobility and had the least mobility reduction during the lockdown. äUnsupervised learning for county-level typological classification for (epmc).pdf DeepDyve Pubget Overpricing