Evidence map›Paper›PMID 36913229›Full record

ArticleIEEE sensors journal2023

Minimizing Viral Transmission in COVID-19 Like Pandemics: Technologies, Challenges, and Opportunities.

Shibli Nisar, Abdul Wakeel, Wania Tahir, Muhammad Tariq

Open access · bronzeAbstract read
In one paragraph

Article in IEEE sensors journal, 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
1.3field-weighted citation impact, top 16% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

4 authors at 3 institutions in 1 country.

Shibli NisarDepartment of Electrical EngineeringMilitary College of SignalsNational University of Sciences and Technology (NUST) Rawalpindi 46000 Pakistan.ORCID https://orcid.org/0000-0002-0311-0876
Abdul WakeelDepartment of Electrical EngineeringMilitary College of SignalsNational University of Sciences and Technology (NUST) Rawalpindi 46000 Pakistan.ORCID https://orcid.org/0000-0002-9746-6290
Wania TahirDepartment of Electrical EngineeringBalochistan University of Information Technology, Engineering and Management Sciences (BUITEMS) Quetta 87300 Pakistan.ORCID https://orcid.org/0000-0002-0894-4810
Muhammad TariqDepartment of Electrical EngineeringNational University of Computer and Emerging Sciences Islamabad 44000 Pakistan.ORCID https://orcid.org/0000-0003-1296-2058
National University of Sciences and Technology · PKBalochistan University of Information Technology, Engineering and Management Sciences · PKNational University of Computer and Emerging Sciences · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus (COVID-19) pandemic has incurred huge loss to human lives throughout the world. Scientists, researchers, and doctors are trying their best to develop and distribute the COVID-19 vaccine throughout the world at the earliest. In current circumstances, different tracking systems are utilized to control or stop the spread of the virus till the whole population of the world gets vaccinated. To track and trace patients in COVID-19 like pandemics, various tracking systems based on different technologies are discussed and compared in this paper. These technologies include, cellular, cyber, satellite-based radio navigation and low range wireless technologies. The main aim of this paper is to conduct a comprehensive survey that can overview all such tracking systems, which are used in minimizing the spread of COVID-19 like pandemics. This paper also highlights the shortcoming of each tracking systems and suggests new mechanisms to overcome such limitations. In addition, the authors propose some futuristic approaches to track patients in prospective pandemics, based on artificial intelligence and big data analysis. Potential research directions, challenges, and the introduction of next-generation tracking systems for minimizing the spread of prospective pandemics, are also discussed at the end.

Indexed as

artificial intelligencecellular forensicsCOVID-19hidden patternspandemictracking systems

Identifiers

PMID36913229
PMCPMC9983691
OpenAlexW4225533047

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.