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

Mesgarpour M

Mesgarpour M(1), Abad JMN(2), Alizadeh R(3), Wongwises S(4), Doranehgard MH(5), Ghaderi S(6), Karimi N(7).
Author information:
(1)Fluid 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.
(2)Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran.
(3)Department of Mechanical Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran.
(4)Fluid 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; National Science and Technology Development Agency
(NSTDA), Pathum Thani 12120, Thailand.
(5)Department of Civil and Environmental Engineering, School of Mining and Petroleum Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada.
(6)Department of General Surgery, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
(7)School of Engineering and Materials Science, Queen Mary University of London, London E1 4NS, United Kingdom; James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, United Kingdom. Electronic address: [Email]

https://dx.doi.org/10.1016/j.jhazmat.2021.125358

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.