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

TL;DR

This paper introduces PHIRVD, a machine learning-based infection kinetic model that accurately predicts COVID-19 mortality and assesses lockdown strategies across 18 countries, emphasizing the importance of early, strategic lockdowns.

Contribution

The study presents the first accurate mortality prediction model for COVID-19 using a 6-stage infection kinetic framework, replacing the reproduction number with mortality-to-infection ratio as a key pandemic descriptor.

Findings

Accurate 30-day mortality predictions for 18 countries.

Quantitative assessment of early versus late lockdown impacts.

Prediction of secondary relapse timelines.

Abstract

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.