  Covid-19 is raging a devastating trail with the highest mortality-to-infected
ratio ever for a pandemic. Lack of vaccine and therapeutic has rendered social
exclusion through lockdown as the singular mode of containment. Harnessing the
predictive powers of Machine Learning within a 6 dimensional infection kinetic
model, depicting interactive evolution of 6 infection stages - healthy
susceptible ($H$), 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 varying
stages of strategic lockdown, up to 30 days beyond last data training. PHIRVD
establishes mortality-to-infection ratio as the correct pandemic descriptor,
substituting reproduction number, and highlights the importance of early and
prolonged 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 lockdown
impositions.
  3. Accurate prediction of secondary relapse timelines/
  4. Establishment of mortality-to-infected ratio as the correct pandemic
descriptor substituting the popular choice of reproduction number, a proven
failure 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/Ebola
group incursion.
