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Abstract
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.
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Details
1 Aston University, Mathematics, College of Engineering and Physical Sciences, Birmingham, UK (GRID:grid.7273.1) (ISNI:0000 0004 0376 4727)
2 University of Delhi, Department of Physics and Astrophysics, Delhi, India (GRID:grid.8195.5) (ISNI:0000 0001 2109 4999)
3 Gandhi Institute of Engineering and Technology University, Gunupur, India (GRID:grid.506618.c)
4 Aston University, Mathematics, College of Engineering and Physical Sciences, Birmingham, UK (GRID:grid.7273.1) (ISNI:0000 0004 0376 4727); University of Lincoln, School of Life Sciences, College of Science, Lincoln, UK (GRID:grid.36511.30) (ISNI:0000 0004 0420 4262)
5 University of Leeds, School of Computing and Faculty of Biological Sciences, Leeds, UK (GRID:grid.9909.9) (ISNI:0000 0004 1936 8403)