Evidence map›Paper›PMID 33798755›Full record

ArticleInfection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases2021

Predictive analysis of COVID-19 eradication with vaccination in India, Brazil, and U.S.A.

Deepa Chaturvedi, U Chakravarty

Open access · greenAbstract read
In one paragraph

Article in Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
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

13 citing papers in PubMed, 25 citations in OpenAlex.

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  9. Observational
  10. COVID-19 vaccine distribution planning using a congested queuing system-A real case from Australia.Transportation research. Part E, Logistics and transportation review · 2022
    Article
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  13. Effectiveness of COVID-19 vaccines and their challenges (Review).Experimental and therapeutic medicine · 2021
    Review
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

2 authors at 1 institution in 1 country.

Deepa ChaturvediDepartment Applied Science, Alard College of Engineering and Management, Marunji, Pune 411057, India. Electronic address: waytodeepa@gmail.com.
U ChakravartyPhotonics Nanomaterial Lab, Laser Materials Processing Division, Raja Ramanna Centre for Advanced Technology, Indore 452 013, India; Homi Bhabha National Institute, Training School Complex, Anushaktinagar, Mumbai 400094, India.
Raja Ramanna Centre for Advanced Technology · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The most important question and concern in these circumstances of COVID-19 epidemic outspread is when will the pandemic end? Vaccination is the only solution to restore life to normalcy in the fastest and safest possible manner. Therefore, we have carried out a predictive analysis for realistic timescale estimates for overcoming the epidemic considering vaccination rate effect on the dynamics of COVID-19 control. In particular we discuss the worst affected large countries like India, Brazil and USA for estimating effect of vaccination rate in expediting the end of the COVID-19 epidemic. We analytically simulated the dynamic evolution of active cases of these countries in the last nine months using the modified SIR model and then included the effect of vaccination to forecast the proliferation dynamics. We hence obtained the transmission parameters, the variation in the reproduction numbers and the impact of the different values of the vaccination shots in the expected curves of active cases in the coming times to predicted the timescales of the end of the epidemic.

Indexed as

SARS-CoV-2BrazilCOVID-19COVID-19 VaccinesDisease EradicationHumansIndiaModels, BiologicalUnited StatesVaccinationCOVID-19 VaccinesCOVID-19ImmunologyNumerical and analytical modeling of epidemicReproduction numberSIR modelVaccination

Identifiers

PMID33798755
PMCPMC8010329
OpenAlexW3152174608

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.