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10.1093/jtm/taaa120 http://scihub22266oqcxt.onion/10.1093/jtm/taaa120 32701135!7454796!32701135     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Travel+Med 2020 ; 27 (5): ä Nephropedia Template TP {{tp|p=32701135|t=2020. Geolocated Twitter social media data to describe the geographic spread of SARS-CoV-2 |pdf=|usr=}}{{32701135}} gab.com Text Geolocated Twitter social media data to describe the geographic spread of SARS-CoV-2 JO: J Travel Med http://www.kidney.de/pget.php?&tw=1&pmid=32701135 #FOAvip Openly available, geotagged Twitter data from 2013 to 2015 was used to estimate the 2019-2020 human mobility patterns in and outside of China to predict the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2. Countries with the highest number of visiting Twitter users outside of China were the USA, Japan, UK, Germany and Turkey. A high correlation was observed when comparing country-level Twitter user visits and reported cases. Twit Text FOAVip Geolocated Twitter social media data to describe the geographic spread of SARS-CoV-2 ????J Travel Med http://www.kidney.de/pget.php?&tw=1&pmid=32701135 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32701135 #FOAvip English Wikipedia <ref>{{Cite pmid|32701135}}</ref> Geolocated Twitter social media data to describe the geographic spread of SARS-CoV-2 #MMPMID32701135 Bisanzio D; Kraemer MUG; Brewer T; Brownstein JS; Reithinger R J Travel Med 2020[Aug]; 27 (5): ä PMID32701135show ga Openly available, geotagged Twitter data from 2013 to 2015 was used to estimate the 2019-2020 human mobility patterns in and outside of China to predict the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2. Countries with the highest number of visiting Twitter users outside of China were the USA, Japan, UK, Germany and Turkey. A high correlation was observed when comparing country-level Twitter user visits and reported cases. |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology/*transmission[MESH] |Global Health[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology/*transmission[MESH] |SARS-CoV-2[MESH] |Social Media/*statistics & numerical data[MESH]Geolocated Twitter social media data to describe the geographic spread(epmc).pdf DeepDyve Pubget Overpricing