Assessment of infection probability indices for airborne diseases in confined spaces: combination of CFD and analytical modelling

Abstract

The paper presents a framework for probabilistic assessment of likelihood of infection from airborne diseases in confined spaces which are continuously occupied for relatively long periods (e.g., school classrooms). The proposed approach is based on a combination of computational fluid dynamics (CFD) pre-calculations and analytical post-processing, to define relevant indices of infection probability. The practical applicability of the method is demonstrated through a case study, where different ventilation scenarios are considered for a school classroom. Corresponding infection probability indices are determined globally for the group of occupants. Furthermore, since the method does not rely on the well-mixing assumption, local probability indices are determined for each occupied location. The obtained results confirm the intuition that an increase of ventilation and/or air filtration reduces the overall likelihood of infection, though the observed positive effect is not uniform within the space. The presented methodology can also be considered as complementary to simpler approaches.