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10.1007/s00466-020-01911-4 http://scihub22266oqcxt.onion/10.1007/s00466-020-01911-4 32904528!7455787!32904528     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Mech 2020 ; 66 (5): 1179-1193 Nephropedia Template TP {{tp|p=32904528|t=2020. Dynamics identification and forecasting of COVID-19 by switching Kalman filters |pdf=|usr=}}{{32904528}} gab.com Text Dynamics identification and forecasting of COVID-19 by switching Kalman filters JO: Comput Mech http://www.kidney.de/pget.php?&tw=1&pmid=32904528 #FOAvip The COVID-19 pandemic has captivated scientific activity since its early days. Particular attention has been dedicated to the identification of underlying dynamics and prediction of future trend. In this work, a switching Kalman filter formalism is applied on dynamics learning and forecasting of the daily new cases of COVID-19. The main feature of this dynamical system is its ability to switch between different linear Gaussian models based on the observations and specified probabilities of transitions between these models. It is thus able to handle the problem of hidden state estimation and forecasting for models with non-Gaussian and nonlinear effects. The potential of this method is explored on the daily new cases of COVID-19 both at the state-level and the country-level in the US. The results suggest a common disease dynamics across states that share certain features. We also demonstrate the ability to make short to medium term predictions with quantifiable error bounds. Twit Text FOAVip Dynamics identification and forecasting of COVID-19 by switching Kalman filters ????Comput Mech http://www.kidney.de/pget.php?&tw=1&pmid=32904528 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32904528 #FOAvip English Wikipedia <ref>{{Cite pmid|32904528}}</ref> Dynamics identification and forecasting of COVID-19 by switching Kalman filters #MMPMID32904528 Zeng X; Ghanem R Comput Mech 2020[]; 66 (5): 1179-1193 PMID32904528show ga The COVID-19 pandemic has captivated scientific activity since its early days. Particular attention has been dedicated to the identification of underlying dynamics and prediction of future trend. In this work, a switching Kalman filter formalism is applied on dynamics learning and forecasting of the daily new cases of COVID-19. The main feature of this dynamical system is its ability to switch between different linear Gaussian models based on the observations and specified probabilities of transitions between these models. It is thus able to handle the problem of hidden state estimation and forecasting for models with non-Gaussian and nonlinear effects. The potential of this method is explored on the daily new cases of COVID-19 both at the state-level and the country-level in the US. The results suggest a common disease dynamics across states that share certain features. We also demonstrate the ability to make short to medium term predictions with quantifiable error bounds. äDynamics identification and forecasting of COVID-19 by switching Kalma(epmc).pdf DeepDyve Pubget Overpricing