Infection kinetics of Covid-19 and containment strategy


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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Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Uncontrolled Keywords: General
Publication ISSN: 2045-2322
Last Modified: 20 May 2024 07:36
Date Deposited: 07 Jun 2021 14:16
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Related URLs: https://www.nat ... 598-021-90698-2 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-06-02
Accepted Date: 2021-05-07
Authors: Chattopadhyay, Amit K (ORCID Profile 0000-0001-5499-6008)
Choudhury, Debajyoti
Ghosh, Goutam
Kundu, Bidisha
Nath, Sujit Kumar



Version: Published Version

License: Creative Commons Attribution

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