Evidence map›Paper›PMID 34078929›Full record

ArticleScientific reports2021

Infection kinetics of Covid-19 and containment strategy.

Amit K Chattopadhyay, Debajyoti Choudhury, Goutam Ghosh, Bidisha Kundu, Sujit Kumar Nath

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Amit K ChattopadhyayMathematics, College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET, UK. a.k.chattoadhyay@aston.ac.uk.
Debajyoti ChoudhuryDepartment of Physics and Astrophysics, University of Delhi, Delhi, 110007, India.
Goutam GhoshGandhi Institute of Engineering and Technology University, Gunupur, Odisha, 765022, India.
Bidisha KunduMathematics, College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET, UK.
Sujit Kumar NathSchool of Computing and Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Basic Reproduction NumberBayes TheoremCommunicable Disease ControlComorbidityCOVID-19Disease SusceptibilityHumansImmunity, HerdIndiaKineticsMachine LearningMarkov ChainsModels, TheoreticalMonte Carlo MethodMortalityUnited Kingdom

Identifiers

PMID34078929
PMCPMC8173017

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.