Evidence map›Paper›PMID 35034977›Full record

ArticleJournal of aerosol science2022

An improved numerical model for epidemic transmission and infection risks assessment in indoor environment.

Yidan Shang, Jingliang Dong, Lin Tian, Fajiang He, Jiyuan Tu

Open access · greenAbstract read
In one paragraph

Article in Journal of aerosol science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
4.9field-weighted citation impact, top 4% 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

6 citing papers in PubMed, 32 citations in OpenAlex.

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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 at 2 institutions in 2 countries.

Yidan ShangCollege of Air Transportation, Shanghai University of Engineering Science, Shanghai 201620, China.
Jingliang DongSchool of Engineering, RMIT University, PO Box 71, Bundoora, VIC 3083, Australia.
Lin TianSchool of Engineering, RMIT University, PO Box 71, Bundoora, VIC 3083, Australia.
Fajiang HeCollege of Air Transportation, Shanghai University of Engineering Science, Shanghai 201620, China.
Jiyuan TuSchool of Engineering, RMIT University, PO Box 71, Bundoora, VIC 3083, Australia.
RMIT University · AUShanghai University of Engineering Science · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social distance will remain the key measure to contain COVID-19 before the global widespread vaccination coverage expected in 2024. Containing the virus outbreak in the office is prioritised to relieve socio-economic burdens caused by COVID-19 and potential pandemics in the future. However, "what is the transmissible distance of SARS-CoV-2" and "what are the appropriate ventilation rates in the office" have been under debate. Without quantitative evaluation of the infection risk, some studies challenged the current social distance policies of 1-2 m adopted by most countries and suggested that longer social distance rule is required as the maximum transmission distance of cough ejected droplets could reach 3-10 m. With the emergence of virus variants such as the Delta variant, the applicability of previous social distance rules are also in doubt. To address the above problem, this study conducted transient Computational Fluid Dynamics (CFD) simulations to evaluate the infection risks under calm and wind scenarios. The calculated Social Distance Index (SDI) indicates that lower humidity leads to a higher infection risk due to weaker evaporation. The infection risk in office was found more sensitive to social distance than ventilation rate. In standard ventilation conditions, social distance of 1.7 m-1.8 m is sufficient distances to reach low probability of infection (PI) target in a calm scenario when coughing is the dominant transmission route. However in the wind scenario (0.25 m/s indoor wind), distance of 2.8 m is required to contain the wild virus type and 3 m is insufficient to contain the spread of the Delta variant. The numerical methods developed in this study provide a framework to evaluate the COVID-19 infection risk in indoor environment. The predicted PI will be beneficial for governments and regulators to make appropriate social-distance and ventilation rules in the office.

Indexed as

CFDCOVID-19Social distance

Identifiers

PMID35034977
PMCPMC8748225
OpenAlexW4206832322

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.