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A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID‐19

Aerosol transmission has played a significant role in the transmission of COVID‐19 disease worldwide. We developed a COVID‐19 aerosol transmission risk estimation model to better understand how key parameters associated with indoor spaces and infector emissions affect inhaled deposited dose of aeros...

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Autores principales: Parhizkar, Hooman, Van Den Wymelenberg, Kevin G., Haas, Charles N., Corsi, Richard L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8662138/
https://www.ncbi.nlm.nih.gov/pubmed/34713463
http://dx.doi.org/10.1111/risa.13844
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author Parhizkar, Hooman
Van Den Wymelenberg, Kevin G.
Haas, Charles N.
Corsi, Richard L.
author_facet Parhizkar, Hooman
Van Den Wymelenberg, Kevin G.
Haas, Charles N.
Corsi, Richard L.
author_sort Parhizkar, Hooman
collection PubMed
description Aerosol transmission has played a significant role in the transmission of COVID‐19 disease worldwide. We developed a COVID‐19 aerosol transmission risk estimation model to better understand how key parameters associated with indoor spaces and infector emissions affect inhaled deposited dose of aerosol particles that convey the SARS‐CoV‐2 virus. The model calculates the concentration of size‐resolved, virus‐laden aerosol particles in well‐mixed indoor air challenged by emissions from an index case(s). The model uses a mechanistic approach, accounting for particle emission dynamics, particle deposition to indoor surfaces, ventilation rate, and single‐zone filtration. The novelty of this model relates to the concept of “inhaled & deposited dose” in the respiratory system of receptors linked to a dose–response curve for human coronavirus HCoV‐229E. We estimated the volume of inhaled & deposited dose of particles in the 0.5–4 μm range expressed in picoliters (pL) in a well‐documented COVID‐19 outbreak in restaurant X in Guangzhou China. We anchored the attack rate with the dose–response curve of HCoV‐229E which provides a preliminary estimate of the average SARS‐CoV‐2 dose per person, expressed in plaque forming units (PFUs). For a reasonable emission scenario, we estimate approximately three PFU per pL deposited, yielding roughly 10 PFUs deposited in the respiratory system of those infected in restaurant X. To explore the model's utility, we tested it with four COVID‐19 outbreaks. The risk estimates from the model fit reasonably well with the reported number of confirmed cases given available metadata from the outbreaks and uncertainties associated with model assumptions.
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spelling pubmed-86621382021-12-10 A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID‐19 Parhizkar, Hooman Van Den Wymelenberg, Kevin G. Haas, Charles N. Corsi, Richard L. Risk Anal Original Research Articles Aerosol transmission has played a significant role in the transmission of COVID‐19 disease worldwide. We developed a COVID‐19 aerosol transmission risk estimation model to better understand how key parameters associated with indoor spaces and infector emissions affect inhaled deposited dose of aerosol particles that convey the SARS‐CoV‐2 virus. The model calculates the concentration of size‐resolved, virus‐laden aerosol particles in well‐mixed indoor air challenged by emissions from an index case(s). The model uses a mechanistic approach, accounting for particle emission dynamics, particle deposition to indoor surfaces, ventilation rate, and single‐zone filtration. The novelty of this model relates to the concept of “inhaled & deposited dose” in the respiratory system of receptors linked to a dose–response curve for human coronavirus HCoV‐229E. We estimated the volume of inhaled & deposited dose of particles in the 0.5–4 μm range expressed in picoliters (pL) in a well‐documented COVID‐19 outbreak in restaurant X in Guangzhou China. We anchored the attack rate with the dose–response curve of HCoV‐229E which provides a preliminary estimate of the average SARS‐CoV‐2 dose per person, expressed in plaque forming units (PFUs). For a reasonable emission scenario, we estimate approximately three PFU per pL deposited, yielding roughly 10 PFUs deposited in the respiratory system of those infected in restaurant X. To explore the model's utility, we tested it with four COVID‐19 outbreaks. The risk estimates from the model fit reasonably well with the reported number of confirmed cases given available metadata from the outbreaks and uncertainties associated with model assumptions. John Wiley and Sons Inc. 2021-10-28 /pmc/articles/PMC8662138/ /pubmed/34713463 http://dx.doi.org/10.1111/risa.13844 Text en © 2021 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Original Research Articles
Parhizkar, Hooman
Van Den Wymelenberg, Kevin G.
Haas, Charles N.
Corsi, Richard L.
A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID‐19
title A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID‐19
title_full A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID‐19
title_fullStr A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID‐19
title_full_unstemmed A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID‐19
title_short A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID‐19
title_sort quantitative risk estimation platform for indoor aerosol transmission of covid‐19
topic Original Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8662138/
https://www.ncbi.nlm.nih.gov/pubmed/34713463
http://dx.doi.org/10.1111/risa.13844
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