A computational model for predicting changes in infection dynamics due to leakage through N95 respirators

Abstract In the absence of fit-testing, leakage of aerosolized pathogens through the gaps between the face and N95 respirators could compromise the effectiveness of the device and increase the risk of infection for the exposed population. To address this issue, we have developed a model to estimate...

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Autores principales:	Prasanna Hariharan, Neha Sharma, Suvajyoti Guha, Rupak K. Banerjee, Gavin D’Souza, Matthew R. Myers
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/99ad9058a158414394a2748c325b69a5
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spelling	oai:doaj.org-article:99ad9058a158414394a2748c325b69a52021-12-02T14:58:53ZA computational model for predicting changes in infection dynamics due to leakage through N95 respirators10.1038/s41598-021-89604-72045-2322https://doaj.org/article/99ad9058a158414394a2748c325b69a52021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89604-7https://doaj.org/toc/2045-2322Abstract In the absence of fit-testing, leakage of aerosolized pathogens through the gaps between the face and N95 respirators could compromise the effectiveness of the device and increase the risk of infection for the exposed population. To address this issue, we have developed a model to estimate the increase in risk of infection resulting from aerosols leaking through gaps between the face and N95 respirators. The gaps between anthropometric face-geometry and N95 respirators were scanned using computed tomography. The gap profiles were subsequently input into CFD models. The amount of aerosol leakage was predicted by the CFD simulations. Leakage levels were validated using experimental data obtained using manikins. The computed amounts of aerosol transmitted to the respiratory system, with and without leaks, were then linked to a risk-assessment model to predict the infection risk for a sample population. An influenza outbreak in which 50% of the population deployed respirators was considered for risk assessment. Our results showed that the leakage predicted by the CFD model matched the experimental data within about 13%. Depending upon the fit between the headform and the respirator, the inward leakage for the aerosols ranged between 30 and 95%. In addition, the non-fit-tested respirator lowered the infection rate from 97% (for no protection) to between 42 and 80%, but not to the same level as the fit-tested respirators (12%). The CFD-based leakage model, combined with the risk-assessment model, can be useful in optimizing protection strategies for a given population exposed to a pathogenic aerosol.Prasanna HariharanNeha SharmaSuvajyoti GuhaRupak K. BanerjeeGavin D’SouzaMatthew R. MyersNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Prasanna Hariharan Neha Sharma Suvajyoti Guha Rupak K. Banerjee Gavin D’Souza Matthew R. Myers A computational model for predicting changes in infection dynamics due to leakage through N95 respirators
description	Abstract In the absence of fit-testing, leakage of aerosolized pathogens through the gaps between the face and N95 respirators could compromise the effectiveness of the device and increase the risk of infection for the exposed population. To address this issue, we have developed a model to estimate the increase in risk of infection resulting from aerosols leaking through gaps between the face and N95 respirators. The gaps between anthropometric face-geometry and N95 respirators were scanned using computed tomography. The gap profiles were subsequently input into CFD models. The amount of aerosol leakage was predicted by the CFD simulations. Leakage levels were validated using experimental data obtained using manikins. The computed amounts of aerosol transmitted to the respiratory system, with and without leaks, were then linked to a risk-assessment model to predict the infection risk for a sample population. An influenza outbreak in which 50% of the population deployed respirators was considered for risk assessment. Our results showed that the leakage predicted by the CFD model matched the experimental data within about 13%. Depending upon the fit between the headform and the respirator, the inward leakage for the aerosols ranged between 30 and 95%. In addition, the non-fit-tested respirator lowered the infection rate from 97% (for no protection) to between 42 and 80%, but not to the same level as the fit-tested respirators (12%). The CFD-based leakage model, combined with the risk-assessment model, can be useful in optimizing protection strategies for a given population exposed to a pathogenic aerosol.
format	article
author	Prasanna Hariharan Neha Sharma Suvajyoti Guha Rupak K. Banerjee Gavin D’Souza Matthew R. Myers
author_facet	Prasanna Hariharan Neha Sharma Suvajyoti Guha Rupak K. Banerjee Gavin D’Souza Matthew R. Myers
author_sort	Prasanna Hariharan
title	A computational model for predicting changes in infection dynamics due to leakage through N95 respirators
title_short	A computational model for predicting changes in infection dynamics due to leakage through N95 respirators
title_full	A computational model for predicting changes in infection dynamics due to leakage through N95 respirators
title_fullStr	A computational model for predicting changes in infection dynamics due to leakage through N95 respirators
title_full_unstemmed	A computational model for predicting changes in infection dynamics due to leakage through N95 respirators
title_sort	computational model for predicting changes in infection dynamics due to leakage through n95 respirators
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/99ad9058a158414394a2748c325b69a5
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A computational model for predicting changes in infection dynamics due to leakage through N95 respirators

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