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Covid-19 is raging a devastating trail with the highest mortality-to-infectedratio ever for a pandemic. Lack of vaccine and therapeutic has rendered socialexclusion through lockdown as the singular mode of containment. Harnessing thepredictive powers of Machine Learning within a 6 dimensional infection kineticmodel, depicting interactive evolution of 6 infection stages - healthysusceptible ($H$), predisposed comorbid susceptible ($P$), infected ($I$),recovered ($R$), herd immunized ($V$) and mortality ($D$) - the model, PHIRVD,provides the first accurate mortality prediction of 18 countries at varyingstages of strategic lockdown, up to 30 days beyond last data training. PHIRVDestablishes mortality-to-infection ratio as the correct pandemic descriptor,substituting reproduction number, and highlights the importance of early andprolonged but strategic lockdown to contain secondary relapse.  Significance Statement:  1. Accurate prediction of the day-by-day mortality profiles of 18 countries,30 days beyond the last data of data training.  2. Precise quantification of the impact of early-vs-later lockdownimpositions.  3. Accurate prediction of secondary relapse timelines/  4. Establishment of mortality-to-infected ratio as the correct pandemicdescriptor substituting the popular choice of reproduction number, a provenfailure in predicting future infection kinetics and secondary surge.  The outcomes have potential to redefine healthy policy landscape,particularly in light of secondary relapse and possible future SARS-COV/Ebolagroup incursion.

Infection Kinetics of Covid-19: Is Lockdown a Potent Containment Tool?