ArticleJournal of hazardous materials2021
Prediction of the spread of Corona-virus carrying droplets in a bus - A computational based artificial intelligence approach.
Article in Journal of hazardous materials, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 81 citations in OpenAlex.
- Effect of an accelerating metro cabin on the diffusion of cough droplets.Scientific reports · 2024Article
- COVID-19 transmission and control in land public transport: A literature review.Fundamental research · 2024Review
- Influence of natural ventilation design on the dispersion of pathogen-laden droplets in a coach bus.The Science of the total environment · 2023Article
- Microbial Risks Caused by Livestock Excrement: Current Research Status and Prospects.Microorganisms · 2023Review
- Finding the proper position of supply and return registers of air condition system in a conference hall in term of COVID-19 virus spread.Revue internationale du froid · 2023Article
- A frontal air intake may improve the natural ventilation in urban buses.Scientific reports · 2022Article
- Ventilation reconstruction in bathrooms for restraining hazardous plume: Mitigate COVID-19 and beyond.Journal of hazardous materials · 2022Article
- Transmission and control of SARS-CoV-2 on ground public transport: A rapid review of the literature up to May 2021.Journal of transport & health · 2022Review
- Optimized mechanism for fast removal of infectious pathogen-laden aerosols in the negative-pressure unit.Journal of hazardous materials · 2022Article
- Role of pathogen-laden expiratory droplet dispersion and natural ventilation explaining a COVID-19 outbreak in a coach bus.Building and environment · 2022Article
- The emission and dynamics of droplets from human expiratory activities and COVID-19 transmission in public transport system: A review.Building and environment · 2022Review
- Air exchange rates and advection-diffusion of COIndoor air · 2022Article
- Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport - A machine learning approach.Chemical engineering journal (Lausanne, Switzerland : 1996) · 2022Article
- Investigation of the effects of mechanical and underfloor heating systems on the COVID-19 viruses distribution.European physical journal plus · 2022Article
- Exploring the potentials of personalized ventilation in mitigating airborne infection risk for two closely ranged occupants with different risk assessment models.Energy and buildings · 2021Article
- Ventilation in worker dormitories and its impact on the spread of respiratory droplets.Sustainable cities and society · 2021Article
- Passenger exposure to respiratory aerosols in a train cabin: Effects of window, injection source, output flow location.Sustainable cities and society · 2021Article
- Computational study on the transmission of the SARS-CoV-2 virus through aerosol in an elevator cabin: Effect of the ventilation system.Physics of fluids (Woodbury, N.Y. : 1994) · 2021Article
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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.