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10.3390/biology9070156 http://scihub22266oqcxt.onion/10.3390/biology9070156 32646014!7407614!32646014     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       Biology+(Basel) 2020 ; 9 (7): ä Nephropedia Template TP {{tp|p=32646014|t=2020. CoVid-19 Pandemic Trend Modeling and Analysis to Support Resilience Decision-Making |pdf=|usr=}}{{32646014}} gab.com Text CoVid-19 Pandemic Trend Modeling and Analysis to Support Resilience Decision-Making JO: Biology (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=32646014 #FOAvip Policy decision-making for system resilience to a hazard requires the estimation and prediction of the trends of growth and decline of the impacts of the hazard. With focus on the recent worldwide spread of CoVid-19, we take the infection rate as the relevant metric whose trend of evolution to follow for verifying the effectiveness of the countermeasures applied. By comparison with the theories of growth and recovery in coupled socio-medical systems, we find that the data for many countries show infection rate trends that are exponential in form. In particular, the recovery trajectory is universal in trend and consistent with the learning theory, which allows for predictions useful in the assistance of decision-making of emergency recovery actions. The findings are validated by extensive data and comparison to medical pandemic models. Twit Text FOAVip CoVid-19 Pandemic Trend Modeling and Analysis to Support Resilience Decision-Making ????Biology (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=32646014 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32646014 #FOAvip English Wikipedia <ref>{{Cite pmid|32646014}}</ref> CoVid-19 Pandemic Trend Modeling and Analysis to Support Resilience Decision-Making #MMPMID32646014 Duffey RB; Zio E Biology (Basel) 2020[Jul]; 9 (7): ä PMID32646014show ga Policy decision-making for system resilience to a hazard requires the estimation and prediction of the trends of growth and decline of the impacts of the hazard. With focus on the recent worldwide spread of CoVid-19, we take the infection rate as the relevant metric whose trend of evolution to follow for verifying the effectiveness of the countermeasures applied. By comparison with the theories of growth and recovery in coupled socio-medical systems, we find that the data for many countries show infection rate trends that are exponential in form. In particular, the recovery trajectory is universal in trend and consistent with the learning theory, which allows for predictions useful in the assistance of decision-making of emergency recovery actions. The findings are validated by extensive data and comparison to medical pandemic models. äCoVid-19 Pandemic Trend Modeling and Analysis to Support Resilience D(epmc).pdf DeepDyve Pubget Overpricing