Evidence map›Paper›PMID 36313592›Full record

ArticleComputer communications2023

A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues.

Deepanshi, Ishan Budhiraja, Deepak Garg, Neeraj Kumar, Rohit Sharma

Open access · greenAbstract read
In one paragraph

Article in Computer communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

3 citing papers in PubMed, 24 citations in OpenAlex.

  1. Trial
  2. Article
  3. 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 at 3 institutions in 2 countries.

DeepanshiSchool of Computer Science Engineering and Technology, Bennett University, Uttar Pradesh, India.
Ishan BudhirajaSchool of Computer Science Engineering and Technology, Bennett University, Uttar Pradesh, India.
Deepak GargSchool of Computer Science Engineering and Technology, Bennett University, Uttar Pradesh, India.
Neeraj KumarDepartment of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India.
Rohit SharmaDepartment of Electronics & Communication Engineering, SRM Institute of Science and Technology, NCR Campus, Modinagar, Ghaziabad, UP, India.
Bennett University · INLebanese American University · LBSRM Institute of Science and Technology · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

SARS-CoV-2 is an infected disease caused by one of the variants of Coronavirus which emerged in December 2019. It is declared a pandemic by WHO in March 2020. COVID-19 outbreak has put the world on a halt and is a major threat to the public health system. It has shattered the world with its effects on different areas as the pandemic hit the world in a number of waves with different variants and mutations. Each variant and mutation have different transmission and infection rates in the human population. More than 609 million people have tested positive and more than 6.5 million people have died due to this disease as per 14th September 2022. Despite of numerous efforts, precautions and vaccination the infection has grown rapidly in the world. In this paper, we aim to give a holistic overview of COVID-19 its variants, game theory perspective, effects on the different social and economic areas, diagnostic advancements, treatment methods. A taxonomy is made for the proper insight of the work demonstrated in the paper. Finally, we discuss the open issues associated with COVID-19 in different fields and futuristic research trends in the area. The main aim of the paper is to provide comprehensive literature that covers all the areas and provide an expert understanding of the COVID-19 techniques and potentially be further utilized to combat the outbreak of COVID-19.

Indexed as

COVID-19Deep learningGame theoryMachine learningSARS-CoV-2Variants

Identifiers

PMID36313592
PMCPMC9598046
OpenAlexW4307570201

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.