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

Henri Salmenjoki*, Marko Korhonen, Antti Puisto, Ville Vuorinen, Mikko J. Alava

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
115 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number0260237
Number of pages12
JournalPloS one
Volume16
Issue number11
DOIs
Publication statusPublished - 22 Nov 2021
MoE publication typeA1 Journal article-refereed

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