Evidence map›Paper›PMID 37362841›Full record

ReviewVacunas2023

The status of COVID-19 vaccines in India: A review.

Deepak Kumar Jha, Kumar Pranay, Samiksha, Amit Kumar, Niti Yashvardhini

Abstract readReview
In one paragraph

Review in Vacunas, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Deepak Kumar JhaDepartment of Zoology, S.M.P. Rajkiya Mahila Mahavidyalaya, Ballia 277401, India.
Kumar PranayDepartment of Biochemistry, IGIMS, Patna 800 014, India.
SamikshaDepartment of Microbiology, Patna Women's College, Patna, 800 001, India.
Amit KumarDepartment of Botany, Patna University, Patna 800 005, India.
Niti YashvardhiniDepartment of Microbiology, Patna Women's College, Patna, 800 001, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of SARS-CoV-2, an etiologic agent of the COVID-19 pandemic disease in late December 2019 has left the whole world aghast with huge health and economic losses. Due to a lack of specific knowledge and understanding at the initial stages, an unprecedented rise in COVID-19 cases has been recorded globally. Various preventive measures and strategies were implemented, however, for the radical control of SARS-CoV-2 infections; it seems that the only effective way to control the ongoing infections is large-scale vaccination. So far, WHO has approved 11 vaccines for emergency use namely Pfizer/BioNTech, Oxford/Astra Zeneca, Johnson and Johnson, Moderna, Covilo, Novavax, Covovax, Spikevax, Can Sino, Comirnaty, and Coronavac while five other needs approval. The worldwide vaccination dataset reveals that 65.7% of the world population has received their first dose of the COVID-19 vaccine. As a consequence of the proactive implementation of India's vaccination program, a historical milestone of administering over 1.9 billion doses of COVID-19 vaccines have been achieved on 19th May 2022. This review summarizes the different types of traditional and modern vaccine designing strategies with an emphasis on COVID-19. Moreover, the review highlights the status of vaccines for COVID-19 approved in India which includes both indigenous and non-indigenous vaccines. The present article also encompasses vaccine designing and developmental strategies, efficacy, safety profile and usage among the population, and the efficacy of modern vaccines over traditional ones.

Indexed as

COVID-19IndiaIndigenous vaccinePandemicVaccination

Identifiers

PMID37362841
PMCPMC10126226

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.