Evidence map›Paper›PMID 36644198›Full record

ReviewInformatics in medicine unlocked2023

COVID-19 Vaccines: Computational tools and Development.

Victor Chukwudi Osamor, Excellent Ikeakanam, Janet U Bishung, Theresa N Abiodun, Raphael Henshaw Ekpo

Abstract readReview
In one paragraph

Review in Informatics in medicine unlocked, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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.

Victor Chukwudi OsamorDepartment of Computer and Information Sciences, Covenant University, Canaanland, Ota, Ogun State, Nigeria.
Excellent IkeakanamDepartment of Computer and Information Sciences, Covenant University, Canaanland, Ota, Ogun State, Nigeria.
Janet U BishungDepartment of Computer and Information Sciences, Covenant University, Canaanland, Ota, Ogun State, Nigeria.
Theresa N AbiodunDepartment of Computer and Information Sciences, Covenant University, Canaanland, Ota, Ogun State, Nigeria.
Raphael Henshaw EkpoDepartment of Computer and Information Sciences, Covenant University, Canaanland, Ota, Ogun State, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The 2019 coronavirus outbreak, also known as COVID-19, poses a serious threat to global health and has already had widespread, devastating effects around the world. Scientists have been working tirelessly to develop vaccines to stop the virus from spreading as much as possible, as its cure has not yet been found. As of December 2022, 651,918,402 cases and 6,656,601 deaths had been reported. Globally, over 13 billion doses of vaccine have been administered, representing 64.45% of the world's population that has received the vaccine. To expedite the vaccine development process, computational tools have been utilized. This paper aims to analyze some computational tools that aid vaccine development by presenting positive evidence for proving the efficacy of these vaccines to suppress the spread of the virus and for the use of computational tools in the development of vaccines for emerging diseases.

Indexed as

Computational toolsCOVID-19 vaccinesSARS-CoV-2Vaccines

Identifiers

PMID36644198
PMCPMC9830932

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.