Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar.

Present day risk assessment on the spreading of airborne viruses is often based on the classical Wells-Riley model assuming immediate mixing of the aerosol into the studied environment. Here, we improve on this approach and the underlying assumptions by modeling the space-time dependency of the aero...

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Autores principales:	Henri Salmenjoki, Marko Korhonen, Antti Puisto, Ville Vuorinen, Mikko J Alava
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f261fa3da9f54b7a860c6b0a5dce9f65
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spelling	oai:doaj.org-article:f261fa3da9f54b7a860c6b0a5dce9f652021-12-02T20:16:17ZModelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar.1932-620310.1371/journal.pone.0260237https://doaj.org/article/f261fa3da9f54b7a860c6b0a5dce9f652021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0260237https://doaj.org/toc/1932-6203Present day risk assessment on the spreading of airborne viruses is often based on the classical Wells-Riley model assuming immediate mixing of the aerosol into the studied environment. Here, we improve on this approach and the underlying assumptions by modeling the space-time dependency of the aerosol concentration via a transport equation with a dynamic source term introduced by the infected individual(s). In the present agent-based methodology, we study the viral aerosol inhalation exposure risk in two scenarios including a low/high risk scenario of a "supermarket"/"bar". The model takes into account typical behavioral patterns for determining the rules of motion for the agents. We solve a diffusion model for aerosol concentration in the prescribed environments in order to account for local exposure to aerosol inhalation. We assess the infection risk using the Wells-Riley model formula using a space-time dependent aerosol concentration. The results are compared against the classical Wells-Riley model. The results indicate features that explain individual cases of high risk with repeated sampling of a heterogeneous environment occupied by non-equilibrium concentration clouds. An example is the relative frequency of cases that might be called superspreading events depending on the model parameters. A simple interpretation is that averages of infection risk are often misleading. They also point out and explain the qualitative and quantitative difference between the two cases-shopping is typically safer for a single individual person.Henri SalmenjokiMarko KorhonenAntti PuistoVille VuorinenMikko J AlavaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0260237 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Henri Salmenjoki Marko Korhonen Antti Puisto Ville Vuorinen Mikko J Alava Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar.
description	Present day risk assessment on the spreading of airborne viruses is often based on the classical Wells-Riley model assuming immediate mixing of the aerosol into the studied environment. Here, we improve on this approach and the underlying assumptions by modeling the space-time dependency of the aerosol concentration via a transport equation with a dynamic source term introduced by the infected individual(s). In the present agent-based methodology, we study the viral aerosol inhalation exposure risk in two scenarios including a low/high risk scenario of a "supermarket"/"bar". The model takes into account typical behavioral patterns for determining the rules of motion for the agents. We solve a diffusion model for aerosol concentration in the prescribed environments in order to account for local exposure to aerosol inhalation. We assess the infection risk using the Wells-Riley model formula using a space-time dependent aerosol concentration. The results are compared against the classical Wells-Riley model. The results indicate features that explain individual cases of high risk with repeated sampling of a heterogeneous environment occupied by non-equilibrium concentration clouds. An example is the relative frequency of cases that might be called superspreading events depending on the model parameters. A simple interpretation is that averages of infection risk are often misleading. They also point out and explain the qualitative and quantitative difference between the two cases-shopping is typically safer for a single individual person.
format	article
author	Henri Salmenjoki Marko Korhonen Antti Puisto Ville Vuorinen Mikko J Alava
author_facet	Henri Salmenjoki Marko Korhonen Antti Puisto Ville Vuorinen Mikko J Alava
author_sort	Henri Salmenjoki
title	Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar.
title_short	Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar.
title_full	Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar.
title_fullStr	Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar.
title_full_unstemmed	Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar.
title_sort	modelling aerosol-based exposure to sars-cov-2 by an agent based monte carlo method: risk estimates in a shop and bar.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f261fa3da9f54b7a860c6b0a5dce9f65
work_keys_str_mv	AT henrisalmenjoki modellingaerosolbasedexposuretosarscov2byanagentbasedmontecarlomethodriskestimatesinashopandbar AT markokorhonen modellingaerosolbasedexposuretosarscov2byanagentbasedmontecarlomethodriskestimatesinashopandbar AT anttipuisto modellingaerosolbasedexposuretosarscov2byanagentbasedmontecarlomethodriskestimatesinashopandbar AT villevuorinen modellingaerosolbasedexposuretosarscov2byanagentbasedmontecarlomethodriskestimatesinashopandbar AT mikkojalava modellingaerosolbasedexposuretosarscov2byanagentbasedmontecarlomethodriskestimatesinashopandbar
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Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar.

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