Use of Modelica to predict risk of Covid-19 infection in indoor environments

Abstract

The understanding of routs of disease transmission is crucial for getting the outbreak of the novel Sars-CoV-2 virus under control. At the beginning of the year 2020 much attention has been paid on person-to-person transmission, whereas now there is more debate and also evidence of airborne transmission. The dispersion process of respiratory droplets released by potentially infected persons has been investigated in many studies using highly reliable but time consuming CFD methods. With such simulations social distancing, wearing masks and shifts in ventilation systems could be justified. This work focuses on the same topic but uses validated zonal models instead of CFD simulations. The Indoor Environment Simulation Suite (IESS) is a toolbox of different sub models for the fast simulation of the indoor environment on a coarse grid. It is implemented in Modelica and at its heart is the Velocity Propagating Zonal model (VEPZO) which in many cases is a superior alternative to complex CFD simulations in terms of the trade-off between effort and detail of the result. Based on the temperature and airflow distribution, this model can be used to predict the dispersion of aerosols in enclosed spaces and thus to infer the risk of Covid-19 infection. Therefore an example of an outbreak in a restaurant in Guangzhou is being investigated. With the installed ventilation system the infection of nine people could be reconstructed. Furthermore different variants were investigated as to how the dispersion area of the infectious aerosols could have been kept more locally concentrated. The local age of the air and the area of increased load with infectious aerosols were evaluated.