Infection kinetics of Covid-19 and containment strategy

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,...

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Autores principales:	Amit K Chattopadhyay, Debajyoti Choudhury, Goutam Ghosh, Bidisha Kundu, Sujit Kumar Nath
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5613c1f813474223b519d2afddfbdecf
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spelling	oai:doaj.org-article:5613c1f813474223b519d2afddfbdecf2021-12-02T15:02:57ZInfection kinetics of Covid-19 and containment strategy10.1038/s41598-021-90698-22045-2322https://doaj.org/article/5613c1f813474223b519d2afddfbdecf2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90698-2https://doaj.org/toc/2045-2322Abstract 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.Amit K ChattopadhyayDebajyoti ChoudhuryGoutam GhoshBidisha KunduSujit Kumar NathNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Amit K Chattopadhyay Debajyoti Choudhury Goutam Ghosh Bidisha Kundu Sujit Kumar Nath Infection kinetics of Covid-19 and containment strategy
description	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.
format	article
author	Amit K Chattopadhyay Debajyoti Choudhury Goutam Ghosh Bidisha Kundu Sujit Kumar Nath
author_facet	Amit K Chattopadhyay Debajyoti Choudhury Goutam Ghosh Bidisha Kundu Sujit Kumar Nath
author_sort	Amit K Chattopadhyay
title	Infection kinetics of Covid-19 and containment strategy
title_short	Infection kinetics of Covid-19 and containment strategy
title_full	Infection kinetics of Covid-19 and containment strategy
title_fullStr	Infection kinetics of Covid-19 and containment strategy
title_full_unstemmed	Infection kinetics of Covid-19 and containment strategy
title_sort	infection kinetics of covid-19 and containment strategy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5613c1f813474223b519d2afddfbdecf
work_keys_str_mv	AT amitkchattopadhyay infectionkineticsofcovid19andcontainmentstrategy AT debajyotichoudhury infectionkineticsofcovid19andcontainmentstrategy AT goutamghosh infectionkineticsofcovid19andcontainmentstrategy AT bidishakundu infectionkineticsofcovid19andcontainmentstrategy AT sujitkumarnath infectionkineticsofcovid19andcontainmentstrategy
_version_	1718389076829143040

Infection kinetics of Covid-19 and containment strategy

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