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10.1038/s41598-021-90698-2 http://scihub22266oqcxt.onion/10.1038/s41598-021-90698-2 34078929!8173017!34078929     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       Sci+Rep 2021 ; 11 (1): 11606 Nephropedia Template TP {{tp|p=34078929|t=2021. Infection kinetics of Covid-19 and containment strategy |pdf=|usr=}}{{34078929}} gab.com Text Infection kinetics of Covid-19 and containment strategy JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34078929 #FOAvip The devastating trail of Covid-19 is characterized by one of the highest mortality-to-infected ratio for a pandemic. Restricted therapeutic and early-stage vaccination still renders social exclusion through lockdown as the key containment mode.To understand the dynamics, we propose PHIRVD, a mechanistic infection propagation model that Machine Learns (Bayesian Markov Chain Monte Carlo) the evolution of six infection stages, namely healthy susceptible (H), predisposed comorbid susceptible (P), infected (I), recovered (R), herd immunized (V) and mortality (D), providing a highly reliable mortality prediction profile for 18 countries at varying stages of lockdown. Training data between 10 February to 29 June 2020, PHIRVD can accurately predict mortality profile up to November 2020, including the second wave kinetics. The model also suggests mortality-to-infection ratio as a more dynamic pandemic descriptor, substituting reproduction number. PHIRVD establishes the importance of early and prolonged but strategic lockdown to contain future relapse, complementing futuristic vaccine impact. Twit Text FOAVip Infection kinetics of Covid-19 and containment strategy ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34078929 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34078929 #FOAvip English Wikipedia <ref>{{Cite pmid|34078929}}</ref> Infection kinetics of Covid-19 and containment strategy #MMPMID34078929 Chattopadhyay AK; Choudhury D; Ghosh G; Kundu B; Nath SK Sci Rep 2021[Jun]; 11 (1): 11606 PMID34078929show ga The devastating trail of Covid-19 is characterized by one of the highest mortality-to-infected ratio for a pandemic. Restricted therapeutic and early-stage vaccination still renders social exclusion through lockdown as the key containment mode.To understand the dynamics, we propose PHIRVD, a mechanistic infection propagation model that Machine Learns (Bayesian Markov Chain Monte Carlo) the evolution of six infection stages, namely healthy susceptible (H), predisposed comorbid susceptible (P), infected (I), recovered (R), herd immunized (V) and mortality (D), providing a highly reliable mortality prediction profile for 18 countries at varying stages of lockdown. Training data between 10 February to 29 June 2020, PHIRVD can accurately predict mortality profile up to November 2020, including the second wave kinetics. The model also suggests mortality-to-infection ratio as a more dynamic pandemic descriptor, substituting reproduction number. PHIRVD establishes the importance of early and prolonged but strategic lockdown to contain future relapse, complementing futuristic vaccine impact. |Basic Reproduction Number[MESH] |Bayes Theorem[MESH] |COVID-19/*epidemiology/etiology/*prevention & control[MESH] |Communicable Disease Control/methods[MESH] |Comorbidity[MESH] |Disease Susceptibility[MESH] |Humans[MESH] |Immunity, Herd[MESH] |India/epidemiology[MESH] |Kinetics[MESH] |Machine Learning[MESH] |Markov Chains[MESH] |Models, Theoretical[MESH] |Monte Carlo Method[MESH] |Mortality[MESH]Infection kinetics of Covid-19 and containment strategy (epmc).pdf DeepDyve Pubget Overpricing