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

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

PLOS ONE, 2021, vol. 16, issue 11, 1-12

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0260237

DOI: 10.1371/journal.pone.0260237

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