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10.1007/s00466-020-01889-z http://scihub22266oqcxt.onion/10.1007/s00466-020-01889-z 32836598!7394277!32836598     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Mech 2020 ; 66 (5): 1055-1068 Nephropedia Template TP {{tp|p=32836598|t=2020. Bayesian-based predictions of COVID-19 evolution in Texas using multispecies mixture-theoretic continuum models |pdf=|usr=}}{{32836598}} gab.com Text Bayesian-based predictions of COVID-19 evolution in Texas using multispecies mixture-theoretic continuum models JO: Comput Mech http://www.kidney.de/pget.php?&tw=1&pmid=32836598 #FOAvip We consider a mixture-theoretic continuum model of the spread of COVID-19 in Texas. The model consists of multiple coupled partial differential reaction-diffusion equations governing the evolution of susceptible, exposed, infectious, recovered, and deceased fractions of the total population in a given region. We consider the problem of model calibration, validation, and prediction following a Bayesian learning approach implemented in OPAL (the Occam Plausibility Algorithm). Our goal is to incorporate COVID-19 data to calibrate the model in real-time and make meaningful predictions and specify the confidence level in the prediction by quantifying the uncertainty in key quantities of interests. Our results show smaller mortality rates in Texas than what is reported in the literature. We predict 7003 deceased cases by September 1, 2020 in Texas with 95% CI 6802-7204. The model is validated for the total deceased cases, however, is found to be invalid for the total infected cases. We discuss possible improvements of the model. Twit Text FOAVip Bayesian-based predictions of COVID-19 evolution in Texas using multispecies mixture-theoretic continuum models ????Comput Mech http://www.kidney.de/pget.php?&tw=1&pmid=32836598 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32836598 #FOAvip English Wikipedia <ref>{{Cite pmid|32836598}}</ref> Bayesian-based predictions of COVID-19 evolution in Texas using multispecies mixture-theoretic continuum models #MMPMID32836598 Jha PK; Cao L; Oden JT Comput Mech 2020[]; 66 (5): 1055-1068 PMID32836598show ga We consider a mixture-theoretic continuum model of the spread of COVID-19 in Texas. The model consists of multiple coupled partial differential reaction-diffusion equations governing the evolution of susceptible, exposed, infectious, recovered, and deceased fractions of the total population in a given region. We consider the problem of model calibration, validation, and prediction following a Bayesian learning approach implemented in OPAL (the Occam Plausibility Algorithm). Our goal is to incorporate COVID-19 data to calibrate the model in real-time and make meaningful predictions and specify the confidence level in the prediction by quantifying the uncertainty in key quantities of interests. Our results show smaller mortality rates in Texas than what is reported in the literature. We predict 7003 deceased cases by September 1, 2020 in Texas with 95% CI 6802-7204. The model is validated for the total deceased cases, however, is found to be invalid for the total infected cases. We discuss possible improvements of the model. äBayesian-based predictions of COVID-19 evolution in Texas using multis(epmc).pdf DeepDyve Pubget Overpricing