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A coupled computational fluid dynamics and Wells-Riley model to predict COVID-19 infection probability for passengers on long-distance trains

A coupled computational fluid dynamics and Wells-Riley model to predict COVID-19 infection probability for passengers on long-distance trains

Wang, Zhaozhi ORCID: 0000-0002-8986-0554, Galea, Edwin R. ORCID: 0000-0002-0001-6665, Grandison, Angus ORCID: 0000-0002-9714-1605, Ewer, John ORCID: 0000-0003-0609-272X and Jia, Fuchen ORCID: 0000-0003-1850-7961 (2021) A coupled computational fluid dynamics and Wells-Riley model to predict COVID-19 infection probability for passengers on long-distance trains. Safety Science, 147:105572. ISSN 0925-7535 (doi:https://doi.org/10.1016/j.ssci.2021.105572)

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34385 GALEA_COVID-19_Infection_Probability_(AAM)_2021.pdf - Accepted Version
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34385 GALEA_Supplementary_Material_2021.pdf - Supplemental Material
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Abstract

Coupled Wells-Riley (WR) and Computational Fluid Dynamics (CFD) modelling (WR-CFD) facilitates a detailed analysis of COVID-19 infection probability (IP). This approach overcomes issues associated with the WR ‘well-mixed’ assumption. The WR-CFD model, which makes uses of a scalar approach to simulate quanta dispersal, is applied to Chinese long-distance trains (G-train). Predicted IPs, at multiple locations, are validated using statistically derived (SD) IPs from reported infections on G-trains. This is the first known attempt to validate a coupled WR-CFD approach using reported COVID-19 infections derived from the rail environment. There is reasonable agreement between trends in predicted and SD IPs, with the maximum SD IP being 10.3% while maximum predicted IP was 14.8%. Additionally, predicted locations of highest and lowest IP, agree with those identified in the statistical analysis. Furthermore, the study demonstrates that the distribution of infectious aerosols is non-uniform and dependent on the nature of the ventilation. This suggests that modelling techniques neglecting these differences are inappropriate for assessing mitigation measures such as physical distancing. A range of mitigation strategies were analysed; the most effective being the majority (90%) of passengers correctly wearing high efficiency masks (e.g. N95). Compared to the base case (40% of passengers wearing low efficiency masks) there was a 95% reduction in average IP. Surprisingly, HEPA filtration was only effective for passengers distant from an index patient, having almost no effect for those in close proximity. Finally, as the approach is based on CFD it can be applied to a range of other indoor environments.

Item Type: Article
Uncontrolled Keywords: CFD; Wells-Riley equation; COVID-19; infection probability; passenger train
Subjects: Q Science > QA Mathematics
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Centre for Numerical Modelling & Process Analysis (CNMPA)
Faculty of Engineering & Science > Centre for Numerical Modelling & Process Analysis (CNMPA) > Fire Safety Engineering Group (FSEG)
Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified: 10 Mar 2022 09:59
URI: http://gala.gre.ac.uk/id/eprint/34385

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