Predicting airborne SARS-CoV-2 Infection Risk with CFD

Abstract

The COVID-19 pandemic caused enormous human and financial losses in 2020 - 2023. In addition, the protective measures to contain the spread of infection have limited the freedom of the population in their daily lives. Although the immunization of the population through infection and vaccination has significantly reduced the burden of disease, new mutations or entirely new pathogens are likely to emerge that could trigger another pandemic. In order to implement targeted protective measures, the first step is to identify the hotspots of transmission. In a second step, technical devices or behavioral rules can then be used. Be-cause technical devices can be expensive and behavioral rules can restrict the freedom of those affected, the effectiveness of these measures should be evaluated before they are made mandatory for millions of people. Infection risk models can be used to compare the risk of infection in different situations, for example, in a bar and on a long-distance train, to identify hotspots of transmission. In addi-tion, infection risk models can be used to estimate the effectiveness of countermeasures. Therefore, we presented a direct infection risk model in Webner et al. (2024).