Evidence map›Paper›PMID 36281429›Full record

ArticleJournal of ambient intelligence and humanized computing2023

COVICT: an IoT based architecture for COVID-19 detection and contact tracing.

Mirza Anas Wahid, Syed Hashim Raza Bukhari, Ahmad Daud, Saeed Ehsan Awan, Muhammad Asif Zahoor Raja

Abstract read
In one paragraph

Article in Journal of ambient intelligence and humanized computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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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.

Mirza Anas WahidDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Islamabad, Pakistan.
Syed Hashim Raza BukhariDepartment of Electrical and Computer Engineering, Air University Islamabad, Islamabad, Pakistan.
Ahmad DaudDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Islamabad, Pakistan.
Saeed Ehsan AwanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Islamabad, Pakistan.
Muhammad Asif Zahoor RajaDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Islamabad, Pakistan.ORCID 0000-0001-9953-822X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The world we live in has been taken quite surprisingly by the outbreak of a novel virus namely SARS-CoV-2. COVID-19 i.e. the disease associated with the virus, has not only shaken the world economy due to enforced lockdown but has also saturated the public health care systems of even most advanced countries due to its exponential spread. The fight against COVID-19 pandemic will continue until majority of world's population get vaccinated or herd immunity is achieved. Many researchers have exploited the Artificial intelligence (AI) knacks based IoT architecture for early detection and monitoring of potential COVID-19 cases to control the transmission of the virus. However, the main cause of the spread is that people infected with COVID-19 do not show any symptoms and are asymptomatic but can still transmit virus to the masses. Researcher have introduced contact tracing applications to automatically detect contacts that can be infected by the index case. However, these fully automated contact tracing apps have not been accepted due to issues like privacy and cross-app compatibility. In the current study, an IoT based COVID-19 detection and monitoring system with semi-automated and improved contact tracing capability namely COVICT has been presented with application of real-time data of symptoms collected from individuals and contact tracing. The deployment of COVICT, the prediction of infected persons can be made more effective and contaminated areas can be identified to mitigate the further propagation of the virus by imposing Smart Lockdown. The proposed IoT based architecture can be quite helpful for regulatory authorities for policy making to fight COVID-19.

Indexed as

Contact tracingCOVID-19D2D communicationEarly identificationInternet of thingsPandemicSmart lockdown

Identifiers

PMID36281429
PMCPMC9583058

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Registered trials

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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.