Evidence map›Paper›PMID 37983230›Full record

ArticlePloS one2023

Optimization of cabin seating arrangement strategies based on the Wells-Riley risk theory.

Yanxi Liu, Xuan Cheng, Dengzhao Tang, Xinyue Wang

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. 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
0.5field-weighted citation impact, top 27% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

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

4 authors at 1 institution in 1 country.

Yanxi LiuDepartment of Airport, School of Transportation Science and Engineering, Civil Aviation University of China, Tianjin, China.ORCID 0000-0002-2278-5844
Xuan ChengDepartment of Airport, School of Transportation Science and Engineering, Civil Aviation University of China, Tianjin, China.
Dengzhao TangDepartment of Airport, School of Transportation Science and Engineering, Civil Aviation University of China, Tianjin, China.
Xinyue WangDepartment of Airport, School of Transportation Science and Engineering, Civil Aviation University of China, Tianjin, China.
Civil Aviation University of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Civil aviation transport is an important source of global respiratory disease spread due to the closely-spaced environment. In order to reduce the probability of infection of passengers, an improved Wells-Riley model for cabin passenger risk assessment have been given in this work, the cabin ventilation and passenger nose and mouth orientation were considered. The model's effectiveness has been verified with published data. Finally, how the load factor and use of an empty seat scheme are associated with the number of infected people was assessed. The results demonstrated that the number of infected people positively correlates with the passenger load factor, and the most suitable load factor can be determined by controlling the final number of infected people with the condition of the epidemic situation in the departure city. Additionally, infection risk was found to be lower among passengers in window seats than in those in aisle seats and middle seats, and keeping empty seats in the middle or aisle could reduce the cabin average probability of infection by up to 37.47%. Using the model developed here, airlines can determine the optimal load factor threshold and seating arrangement strategy to improve economic benefits and reduce the probability of passenger infection.

Indexed as

AviationHumansProbabilityRisk AssessmentVentilation

Identifiers

PMID37983230
PMCPMC10659163
OpenAlexW4388835250

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.