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10.1016/j.chaos.2020.109993 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.109993 32546901!7290208!32546901     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32546901&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 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos+Solitons+Fractals 2020 ; 138 (ä): 109993 Nephropedia Template TP {{tp|p=32546901|t=2020. Assessing functional propagation patterns in COVID-19 |pdf=|usr=}}{{32546901}} gab.com Text Assessing functional propagation patterns in COVID-19 JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32546901 #FOAvip Among the many efforts done by the scientific community to help coping with the COVID-19 pandemic, one of the most important has been the creation of models to describe its propagation, as these are expected to guide the deployment of containment and health policies. These models are commonly based on exogenous information, as e.g. mobility data, whose limitedness always compromise the reliability of obtained results. In this contribution we propose a different approach, based on extracting relationships between the evolution of the disease in different regions through information theoretical metrics. In a way similar to what is commonly done in neuroscience, propagation is understood as information transfer, and the resulting propagation patterns are represented and studied as functional networks. By applying this methodology to the dynamics of COVID-19 in several countries and regions thereof, we were able to reconstruct static and time-varying propagation graphs. We further discuss the advantages, promises and open research questions associated with this functional approach. Twit Text FOAVip Assessing functional propagation patterns in COVID-19 ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32546901 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32546901 #FOAvip English Wikipedia <ref>{{Cite pmid|32546901}}</ref> Assessing functional propagation patterns in COVID-19 #MMPMID32546901 Zanin M; Papo D Chaos Solitons Fractals 2020[Sep]; 138 (ä): 109993 PMID32546901show ga Among the many efforts done by the scientific community to help coping with the COVID-19 pandemic, one of the most important has been the creation of models to describe its propagation, as these are expected to guide the deployment of containment and health policies. These models are commonly based on exogenous information, as e.g. mobility data, whose limitedness always compromise the reliability of obtained results. In this contribution we propose a different approach, based on extracting relationships between the evolution of the disease in different regions through information theoretical metrics. In a way similar to what is commonly done in neuroscience, propagation is understood as information transfer, and the resulting propagation patterns are represented and studied as functional networks. By applying this methodology to the dynamics of COVID-19 in several countries and regions thereof, we were able to reconstruct static and time-varying propagation graphs. We further discuss the advantages, promises and open research questions associated with this functional approach. äAssessing functional propagation patterns in COVID-19 (epmc).pdf DeepDyve Pubget Overpricing