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Modeling the factors that influence exposure to SARS‐CoV‐2 on a subway train carriage
We propose the Transmission of Virus in Carriages (TVC) model, a computational model which simulates the potential exposure to SARS‐CoV‐2 for passengers traveling in a subway rail system train. This model considers exposure through three different routes: fomites via contact with contaminated surfac...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111599/ https://www.ncbi.nlm.nih.gov/pubmed/35133673 http://dx.doi.org/10.1111/ina.12976 |
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author | Miller, Daniel King, Marco‐Felipe Nally, James Drodge, Joseph R. Reeves, Gary I. Bate, Andrew M. Cooper, Henry Dalrymple, Ursula Hall, Ian López‐García, Martín Parker, Simon T. Noakes, Catherine J. |
author_facet | Miller, Daniel King, Marco‐Felipe Nally, James Drodge, Joseph R. Reeves, Gary I. Bate, Andrew M. Cooper, Henry Dalrymple, Ursula Hall, Ian López‐García, Martín Parker, Simon T. Noakes, Catherine J. |
author_sort | Miller, Daniel |
collection | PubMed |
description | We propose the Transmission of Virus in Carriages (TVC) model, a computational model which simulates the potential exposure to SARS‐CoV‐2 for passengers traveling in a subway rail system train. This model considers exposure through three different routes: fomites via contact with contaminated surfaces; close‐range exposure, which accounts for aerosol and droplet transmission within 2 m of the infectious source; and airborne exposure via small aerosols which does not rely on being within 2 m distance from the infectious source. Simulations are based on typical subway parameters and the aim of the study is to consider the relative effect of environmental and behavioral factors including prevalence of the virus in the population, number of people traveling, ventilation rate, and mask wearing as well as the effect of model assumptions such as emission rates. Results simulate generally low exposures in most of the scenarios considered, especially under low virus prevalence. Social distancing through reduced loading and high mask‐wearing adherence is predicted to have a noticeable effect on reducing exposure through all routes. The highest predicted doses happen through close‐range exposure, while the fomite route cannot be neglected; exposure through both routes relies on infrequent events involving relatively few individuals. Simulated exposure through the airborne route is more homogeneous across passengers, but is generally lower due to the typically short duration of the trips, mask wearing, and the high ventilation rate within the carriage. The infection risk resulting from exposure is challenging to estimate as it will be influenced by factors such as virus variant and vaccination rates. |
format | Online Article Text |
id | pubmed-9111599 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91115992022-05-17 Modeling the factors that influence exposure to SARS‐CoV‐2 on a subway train carriage Miller, Daniel King, Marco‐Felipe Nally, James Drodge, Joseph R. Reeves, Gary I. Bate, Andrew M. Cooper, Henry Dalrymple, Ursula Hall, Ian López‐García, Martín Parker, Simon T. Noakes, Catherine J. Indoor Air Review Article We propose the Transmission of Virus in Carriages (TVC) model, a computational model which simulates the potential exposure to SARS‐CoV‐2 for passengers traveling in a subway rail system train. This model considers exposure through three different routes: fomites via contact with contaminated surfaces; close‐range exposure, which accounts for aerosol and droplet transmission within 2 m of the infectious source; and airborne exposure via small aerosols which does not rely on being within 2 m distance from the infectious source. Simulations are based on typical subway parameters and the aim of the study is to consider the relative effect of environmental and behavioral factors including prevalence of the virus in the population, number of people traveling, ventilation rate, and mask wearing as well as the effect of model assumptions such as emission rates. Results simulate generally low exposures in most of the scenarios considered, especially under low virus prevalence. Social distancing through reduced loading and high mask‐wearing adherence is predicted to have a noticeable effect on reducing exposure through all routes. The highest predicted doses happen through close‐range exposure, while the fomite route cannot be neglected; exposure through both routes relies on infrequent events involving relatively few individuals. Simulated exposure through the airborne route is more homogeneous across passengers, but is generally lower due to the typically short duration of the trips, mask wearing, and the high ventilation rate within the carriage. The infection risk resulting from exposure is challenging to estimate as it will be influenced by factors such as virus variant and vaccination rates. John Wiley and Sons Inc. 2022-02-08 2022-02 /pmc/articles/PMC9111599/ /pubmed/35133673 http://dx.doi.org/10.1111/ina.12976 Text en © 2022 Crown copyright. Indoor Air published by John Wiley & Sons Ltd. This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Miller, Daniel King, Marco‐Felipe Nally, James Drodge, Joseph R. Reeves, Gary I. Bate, Andrew M. Cooper, Henry Dalrymple, Ursula Hall, Ian López‐García, Martín Parker, Simon T. Noakes, Catherine J. Modeling the factors that influence exposure to SARS‐CoV‐2 on a subway train carriage |
title | Modeling the factors that influence exposure to SARS‐CoV‐2 on a subway train carriage |
title_full | Modeling the factors that influence exposure to SARS‐CoV‐2 on a subway train carriage |
title_fullStr | Modeling the factors that influence exposure to SARS‐CoV‐2 on a subway train carriage |
title_full_unstemmed | Modeling the factors that influence exposure to SARS‐CoV‐2 on a subway train carriage |
title_short | Modeling the factors that influence exposure to SARS‐CoV‐2 on a subway train carriage |
title_sort | modeling the factors that influence exposure to sars‐cov‐2 on a subway train carriage |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111599/ https://www.ncbi.nlm.nih.gov/pubmed/35133673 http://dx.doi.org/10.1111/ina.12976 |
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