An infection risk model for estimating infection probability and occupancy time: A CFD approach with aerosol measurements

Many infection risk models rely on the well-mixed assumption, neglecting variations in aerosol concentration and particle size distribution. Additionally, these models lack real-time aerosol data integration, limiting their ability to assess infection risk and safe occupancy time dynamically. While CFD-based models provide spatiotemporal aerosol distribution, they often use predefined emission rates that do not account for particle size effects. This study presents a novel radius-resolved infection risk model that estimates infection probability and maximum occupancy time based on measured aerosol concentrations. The model directly utilizes the pathogen concentration derived from in-situ aerosol measurements to estimate real-time infection risk at the sensor location. Furthermore, CFD simulations incorporating the particle size distribution of exhaled aerosols in a stale air classroom environment are applied to generate spatiotemporal infection risk and safe occupancy time maps. The effects of the infected occupant’s position, particle radius, activity level, and age on infection risk are investigated. Results highlight significant spatial variability, with our model estimating infection risk 20% lower than the well-mixed model at the sensor but over three times higher near the infector, underscoring the limitations of well-mixed models. Maximum occupancy time maps reveal that, in certain locations, nearly half of the occupants remain uninfected after 25 min, whereas, in other locations, only three occupants remain uninfected. This demonstrates that infection probability is an inadequate safety metric, while maximum safe occupancy time is more reliable. The developed model can serve as an estimator of infection risk and safe occupancy time for adaptive ventilation and filtration strategies. • Developed an infection risk model to estimate infection risk in a classroom. • Derived pathogen concentration directly from measured aerosol concentrations. • Generated spatiotemporal infection risk and occupancy time maps using CFD results. • Results showed spatial variability, exposing limits of well-mixed infection models.