Evidence map›Paper›PMID 34075271›Full record

ArticleComputers & industrial engineering2021

Relief Supply Chain Management Using Internet of Things to Address COVID-19 Outbreak.

Amirhossein Salehi-Amiri, Armin Jabbarzadeh, Ali Zahedi, Navid Akbarpour, Mostafa Hajiaghaei-Keshteli

Abstract read
In one paragraph

Article in Computers & industrial engineering, 2021. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Review
  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.

Amirhossein Salehi-AmiriEngineering Department, École de technologie supérieure (ETS), Montreal, Canada.
Armin JabbarzadehEngineering Department, École de technologie supérieure (ETS), Montreal, Canada.
Ali ZahediTecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Puebla, Mexico.
Navid AkbarpourTecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Puebla, Mexico.
Mostafa Hajiaghaei-KeshteliTecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Puebla, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowadays, due to the COVID-19 outbreak, the most significant factor to be considered all over the world is to manage this pandemic and especially to address positive cases, efficiently and effectively. This can be achieved by simultaneously utilizing the present network with supply chain settings and also the Internet of Things (IoT). This consideration enables the accurate monitoring of suspected cases in real-time to optimize total service time. Hence, this paper firstly designs two sub-models to minimize distance and traffic while minimizing total response time. Our main contribution in this paper is to develop a dynamic scheme using IoT to deal with suspected cases. We also investigate the proposed methodology on a real case problem in Canada. A comprehensive analysis of the proposed methodology behavior has been conducted and the results showed the managerial decision-making process to address COVID-19 patients. The proposed approach establishes efficient strategies to identify suspicious COVID-19 cases and provide them with medical observance in a short time when utilized with IoT. The obtained results of the considered scenarios show 12% up to 15% improvement in the ambulance response time when using IoT.

Indexed as

COVID-19Humanitarian LogisticsIndustry 4.0Relief Supply ChainSupply Chain Design

Identifiers

PMID34075271
PMCPMC8159708

What OpenQuestion holds

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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.