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10.1098/rsif.2020.0494 http://scihub22266oqcxt.onion/10.1098/rsif.2020.0494 32752997!7482569!32752997     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+R+Soc+Interface 2020 ; 17 (169): 20200494 Nephropedia Template TP {{tp|p=32752997|t=2020. Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2 |pdf=|usr=}}{{32752997}} gab.com Text Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2 JO: J R Soc Interface http://www.kidney.de/pget.php?&tw=1&pmid=32752997 #FOAvip We introduce a novel methodology for predicting the time evolution of the number of individuals in a given country reported to be infected with SARS-CoV-2. This methodology, which is based on the synergy of explicit mathematical formulae and deep learning networks, yields algorithms whose input is only the existing data in the given country of the accumulative number of individuals who are reported to be infected. The analytical formulae involve several constant parameters that were determined from the available data using an error-minimizing algorithm. The same data were also used for the training of a bidirectional long short-term memory network. We applied the above methodology to the epidemics in Italy, Spain, France, Germany, USA and Sweden. The significance of these results for evaluating the impact of easing the lockdown measures is discussed. Twit Text FOAVip Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2 ????J R Soc Interface http://www.kidney.de/pget.php?&tw=1&pmid=32752997 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32752997 #FOAvip English Wikipedia <ref>{{Cite pmid|32752997}}</ref> Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2 #MMPMID32752997 Fokas AS; Dikaios N; Kastis GA J R Soc Interface 2020[Aug]; 17 (169): 20200494 PMID32752997show ga We introduce a novel methodology for predicting the time evolution of the number of individuals in a given country reported to be infected with SARS-CoV-2. This methodology, which is based on the synergy of explicit mathematical formulae and deep learning networks, yields algorithms whose input is only the existing data in the given country of the accumulative number of individuals who are reported to be infected. The analytical formulae involve several constant parameters that were determined from the available data using an error-minimizing algorithm. The same data were also used for the training of a bidirectional long short-term memory network. We applied the above methodology to the epidemics in Italy, Spain, France, Germany, USA and Sweden. The significance of these results for evaluating the impact of easing the lockdown measures is discussed. |*Betacoronavirus[MESH] |*Deep Learning[MESH] |*Models, Biological[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology/prevention & control/*transmission[MESH] |Europe/epidemiology[MESH] |Humans[MESH] |Pandemics/prevention & control[MESH] |Pneumonia, Viral/*epidemiology/prevention & control/*transmission[MESH]Mathematical models and deep learning for predicting the number of ind(epmc).pdf DeepDyve Pubget Overpricing