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10.1016/j.ijid.2020.05.011 http://scihub22266oqcxt.onion/10.1016/j.ijid.2020.05.011 32437929!7207126!32437929     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32437929&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 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Infect+Dis 2020 ; 96 (ä): 558-561 Nephropedia Template TP {{tp|p=32437929|t=2020. Visualizing COVID-19 pandemic risk through network connectedness |pdf=|usr=}}{{32437929}} gab.com Text Visualizing COVID-19 pandemic risk through network connectedness JO: Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32437929 #FOAvip With the domestic and international spread of the coronavirus disease 2019 (COVID-19), much attention has been given to estimating pandemic risk. We propose the novel application of a well-established scientific approach - the network analysis - to provide a direct visualization of the COVID-19 pandemic risk; infographics are provided in the figures. By showing visually the degree of connectedness between different regions based on reported confirmed cases of COVID-19, we demonstrate that network analysis provides a relatively simple yet powerful way to estimate the pandemic risk. Twit Text FOAVip Visualizing COVID-19 pandemic risk through network connectedness ????Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32437929 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32437929 #FOAvip English Wikipedia <ref>{{Cite pmid|32437929}}</ref> Visualizing COVID-19 pandemic risk through network connectedness #MMPMID32437929 So MKP; Tiwari A; Chu AMY; Tsang JTY; Chan JNL Int J Infect Dis 2020[Jul]; 96 (ä): 558-561 PMID32437929show ga With the domestic and international spread of the coronavirus disease 2019 (COVID-19), much attention has been given to estimating pandemic risk. We propose the novel application of a well-established scientific approach - the network analysis - to provide a direct visualization of the COVID-19 pandemic risk; infographics are provided in the figures. By showing visually the degree of connectedness between different regions based on reported confirmed cases of COVID-19, we demonstrate that network analysis provides a relatively simple yet powerful way to estimate the pandemic risk. |Betacoronavirus[MESH] |COVID-19[MESH] |China[MESH] |Coronavirus Infections/*epidemiology[MESH] |Global Health[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH] |Risk Assessment/*methods[MESH]Visualizing COVID-19 pandemic risk through network connectedness (epmc).pdf DeepDyve Pubget Overpricing