Prediction of the spread of Corona-virus carrying droplets in a bus- a computational based artificial intelligence approach

Mesgarpour, M., Abad, J. M. N., Alizadeh, R., Wongwises, S., Doranehgard, M. H., Ghaderi, S. and Karimi, N. (2021) Prediction of the spread of Corona-virus carrying droplets in a bus- a computational based artificial intelligence approach. Journal of Hazardous Materials, 413, 125358. (doi: 10.1016/j.jhazmat.2021.125358)

Abstract

Public transport has been identified as high risk as the corona-virus carrying droplets generated by the infected passengers could be distributed to other passengers. Therefore, predicting the patterns of droplet spreading in public transport environment is of primary importance. This paper puts forward a novel computational and artificial intelligence (AI) framework for fast prediction of the spread of droplets produced by a sneezing passenger in a bus. The formation of droplets of salvia is numerically modelled using a volume of fluid methodology applied to the mouth and lips of an infected person during the sneezing process. This is followed by a large eddy simulation of the resultant two phase flow in the vicinity of the person while the effects of droplet evaporation and ventilation in the bus are considered. The results are subsequently fed to an AI tool that employs deep learning to predict the distribution of droplets in the entire volume of the bus. This combined framework is two orders of magnitude faster than the pure computational approach. It is shown that the droplets with diameters less than 250 micrometers are most responsible for the transmission of the virus, as they can travel the entire length of the bus.

• Item Type: Articles
• Additional Information: The first author (M. Mesgarpour) acknowledges Postdoctoral Fellowship from KMUTT. S. Wongwises acknowledges the support provided by the "Research Chair Grant "National Science and Technology Development Agency (NSTDA), and King Mongkut's University of Technology Thonburi through the "KMUTT 55th Anniversary Commemorative Fluid. N. Karimi acknowledges the financial support by the Engineering and Physical Science Research Council through the grant number EP/V036777/1 Risk Evaluation Fast Intelligent Tool (RELIANT) for COVID 19.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Karimi, Dr Nader

Creator Roles

Karimi, N.: Conceptualization, Supervision, Project administration, Writing – review and editing

• Authors: Mesgarpour, M., Abad, J. M. N., Alizadeh, R., Wongwises, S., Doranehgard, M. H., Ghaderi, S., and Karimi, N.
• College/School: College of Science and Engineering > School of Engineering
• Journal Name: Journal of Hazardous Materials
• Publisher: Elsevier
• ISSN: 0304-3894
• ISSN (Online): 1873-3336
• Published Online: 09 February 2021
• Copyright Holders: Copyright © 2021 Elsevier
• First Published: First published413: 125358
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
311655		Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19	Andrea Cammarano	Engineering and Physical Sciences Research Council (EPSRC)	EP/V036777/1	ENG - Aerospace Sciences