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A spatiotemporally resolved infection risk model for airborne transmission of COVID-19 variants in indoor spaces

The classic Wells-Riley model is widely used for estimation of the transmission risk of airborne pathogens in indoor spaces. However, the predictive capability of this zero-dimensional model is limited as it does not resolve the highly heterogeneous spatiotemporal distribution of airborne pathogens,...

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Autores principales: Li, Xiangdong, Lester, Daniel, Rosengarten, Gary, Aboltins, Craig, Patel, Milan, Cole, Ivan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8695516/
https://www.ncbi.nlm.nih.gov/pubmed/34954184
http://dx.doi.org/10.1016/j.scitotenv.2021.152592
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author Li, Xiangdong
Lester, Daniel
Rosengarten, Gary
Aboltins, Craig
Patel, Milan
Cole, Ivan
author_facet Li, Xiangdong
Lester, Daniel
Rosengarten, Gary
Aboltins, Craig
Patel, Milan
Cole, Ivan
author_sort Li, Xiangdong
collection PubMed
description The classic Wells-Riley model is widely used for estimation of the transmission risk of airborne pathogens in indoor spaces. However, the predictive capability of this zero-dimensional model is limited as it does not resolve the highly heterogeneous spatiotemporal distribution of airborne pathogens, and the infection risk is poorly quantified for many pathogens. In this study we address these shortcomings by developing a novel spatiotemporally resolved Wells-Riley model for prediction of the transmission risk of different COVID-19 variants in indoor environments. This modelling framework properly accounts for airborne infection risk by incorporating the latest clinical data regarding viral shedding by COVID-19 patients and SARS-CoV-2 infecting human cells. The spatiotemporal distribution of airborne pathogens is determined via computational fluid dynamics (CFD) simulations of airflow and aerosol transport, leading to an integrated model of infection risk associated with the exposure to SARS-CoV-2, which can produce quantitative 3D infection risk map for a specific SARS-CoV-2 variant in a given indoor space. Application of this model to airborne COVID-19 transmission within a hospital ward demonstrates the impact of different virus variants and respiratory PPE upon transmission risk. With the emergence of highly contagious SARS-CoV-2 variants such as the Delta and Omicron strains, respiratory PPE alone may not provide effective protection. These findings suggest a combination of optimal ventilation and respiratory PPE must be developed to effectively control the transmission of COVID-19 in healthcare settings and indoor spaces in general. This generalised risk estimation framework has the flexibility to incorporate further clinical data as such becomes available, and can be readily applied to consider a wide range of factors that impact transmission risk, including location and movement of infectious persons, virus variant and stage of infection, level of PPE and vaccination of infectious and susceptible individuals, impacts of coughing, sneezing, talking and breathing, and natural and mechanised ventilation and filtration.
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spelling pubmed-86955162021-12-23 A spatiotemporally resolved infection risk model for airborne transmission of COVID-19 variants in indoor spaces Li, Xiangdong Lester, Daniel Rosengarten, Gary Aboltins, Craig Patel, Milan Cole, Ivan Sci Total Environ Article The classic Wells-Riley model is widely used for estimation of the transmission risk of airborne pathogens in indoor spaces. However, the predictive capability of this zero-dimensional model is limited as it does not resolve the highly heterogeneous spatiotemporal distribution of airborne pathogens, and the infection risk is poorly quantified for many pathogens. In this study we address these shortcomings by developing a novel spatiotemporally resolved Wells-Riley model for prediction of the transmission risk of different COVID-19 variants in indoor environments. This modelling framework properly accounts for airborne infection risk by incorporating the latest clinical data regarding viral shedding by COVID-19 patients and SARS-CoV-2 infecting human cells. The spatiotemporal distribution of airborne pathogens is determined via computational fluid dynamics (CFD) simulations of airflow and aerosol transport, leading to an integrated model of infection risk associated with the exposure to SARS-CoV-2, which can produce quantitative 3D infection risk map for a specific SARS-CoV-2 variant in a given indoor space. Application of this model to airborne COVID-19 transmission within a hospital ward demonstrates the impact of different virus variants and respiratory PPE upon transmission risk. With the emergence of highly contagious SARS-CoV-2 variants such as the Delta and Omicron strains, respiratory PPE alone may not provide effective protection. These findings suggest a combination of optimal ventilation and respiratory PPE must be developed to effectively control the transmission of COVID-19 in healthcare settings and indoor spaces in general. This generalised risk estimation framework has the flexibility to incorporate further clinical data as such becomes available, and can be readily applied to consider a wide range of factors that impact transmission risk, including location and movement of infectious persons, virus variant and stage of infection, level of PPE and vaccination of infectious and susceptible individuals, impacts of coughing, sneezing, talking and breathing, and natural and mechanised ventilation and filtration. Elsevier B.V. 2022-03-15 2021-12-23 /pmc/articles/PMC8695516/ /pubmed/34954184 http://dx.doi.org/10.1016/j.scitotenv.2021.152592 Text en © 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Li, Xiangdong
Lester, Daniel
Rosengarten, Gary
Aboltins, Craig
Patel, Milan
Cole, Ivan
A spatiotemporally resolved infection risk model for airborne transmission of COVID-19 variants in indoor spaces
title A spatiotemporally resolved infection risk model for airborne transmission of COVID-19 variants in indoor spaces
title_full A spatiotemporally resolved infection risk model for airborne transmission of COVID-19 variants in indoor spaces
title_fullStr A spatiotemporally resolved infection risk model for airborne transmission of COVID-19 variants in indoor spaces
title_full_unstemmed A spatiotemporally resolved infection risk model for airborne transmission of COVID-19 variants in indoor spaces
title_short A spatiotemporally resolved infection risk model for airborne transmission of COVID-19 variants in indoor spaces
title_sort spatiotemporally resolved infection risk model for airborne transmission of covid-19 variants in indoor spaces
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8695516/
https://www.ncbi.nlm.nih.gov/pubmed/34954184
http://dx.doi.org/10.1016/j.scitotenv.2021.152592
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