Evidence map›Paper›PMID 34642569›Full record

ArticleChemical engineering journal (Lausanne, Switzerland : 1996)2022

Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport - A machine learning approach.

Mehrdad Mesgarpour, Javad Mohebbi Najm Abad, Rasool Alizadeh, Somchai Wongwises, Mohammad Hossein Doranehgard, Saeed Jowkar, Nader Karimi

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Chemical engineering journal (Lausanne, Switzerland : 1996), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
6.8field-weighted citation impact, top 2% 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

2 citing papers in PubMed, 51 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 6 institutions in 4 countries.

Mehrdad MesgarpourFluid Mechanics, Thermal Engineering and Multiphase Flow Research Lab. (FUTURE), Department of Mechanical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi (KMUTT), Bangmod, Bangkok 10140, Thailand.
Javad Mohebbi Najm AbadDepartment of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran.
Rasool AlizadehDepartment of Mechanical Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran.
Somchai WongwisesFluid Mechanics, Thermal Engineering and Multiphase Flow Research Lab. (FUTURE), Department of Mechanical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi (KMUTT), Bangmod, Bangkok 10140, Thailand.
Mohammad Hossein DoranehgardDepartment of Civil and Environmental Engineering, School of Mining and Petroleum Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada.
Saeed JowkarAerospace Engineering Department, Sharif University of Technology 14588-89694, Iran.
Nader KarimiSchool of Engineering and Materials Science, Queen Mary University of London, London E1 4NS, United Kingdom.
Islamic Azad University Quchan · IRKing Mongkut's University of Technology Thonburi · THNational Science and Technology Development Agency · THQueen Mary University of London · GBSharif University of Technology · IRUniversity of Alberta · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human-generated droplets constitute the main route for the transmission of coronavirus. However, the details of such transmission in enclosed environments are yet to be understood. This is because geometrical and environmental parameters can immensely complicate the problem and turn the conventional analyses inefficient. As a remedy, this work develops a predictive tool based on computational fluid dynamics and machine learning to examine the distribution of sneezing droplets in realistic configurations. The time-dependent effects of environmental parameters, including temperature, humidity and ventilation rate, upon the droplets with diameters between 1 and 250

Indexed as

Computational Fluid DynamicsCOVID-19Droplets distributionDroplet SuspensionMachine learningPrediction Models

Identifiers

PMID34642569
PMCPMC8495004
OpenAlexW3204778125

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.