Evidence map›Paper›PMID 39684997›Full record

ArticleHealthcare (Basel, Switzerland)2024

Assessment of Rapid Antigen Diagnostic Tests at Mass Events: Identifying Optimal Floor Plan Configurations for Enhanced Efficiency.

Anas A Khan, Ahmad F Turki

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Anas A KhanEmergency Medicine Department, College of Medicine, King Saud University, Riyadh 11362, Saudi Arabia.ORCID 0000-0001-5075-7392
Ahmad F TurkiElectrical and Computer Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.ORCID 0000-0002-1328-9937

Funding

This research work was funded by The Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia has funded this Project under grant no (GPIP: 396-135-2024). The authors gratefully acknowledge technical and financial sup GPIP: 396-135-2024
6 · The paper itself

Abstract

BACKGROUND/

objectivesThe COVID-19 pandemic underscored the urgent need for rapid, efficient testing methods at large-scale events to control virus spread. This study leverages queueing theory to explore how different floor plan configurations affect the efficiency of Rapid Antigen Diagnostic Test (RADT) centers at mass gatherings, aiming to enhance throughput and minimize wait times.

methodsEmploying the

resultsThe findings demonstrated that the U-shaped layout was more efficient than the straight-line configuration, reducing the average time from sample collection to results acquisition-1.6 minutes in the U-shaped layout versus 1.8 minutes in the straight-line layout. The efficiency of the U-shaped layout was particularly notable at the results stage, with statistically significant differences (

conclusionsThe study confirms the feasibility of implementing RADT procedures at mass gatherings and identifies the U-shaped floor plan as the optimal configuration. This layout significantly enhances testing efficiency and effectiveness, suggesting its suitability for future large-scale testing scenarios. The research contributes to optimizing mass testing strategies, vital for public health emergency management during pandemics.

Indexed as

agile managementantigen testingCOVID-19 testingMAP/PH/c modelmass gatheringsoperational efficiencyprocess optimizationpublic health emergency managementqueueing theorysystem throughput

Identifiers

PMID39684997
PMCPMC11641218

What OpenQuestion holds

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LicenceCC BY
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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.