A framework for realistic estimation of indoor environments airborne infection risk: evaluating ASHRAE standard 241

This study critically evaluates common modeling assumptions in estimating respiratory infection transmission risk within indoor environments, using Computational Fluid Dynamics (CFD) simulations. It highlights the limitations of assuming a fixed number and predefined locations of infectious individuals. To address these limitations, the study introduces a computational method that integrates probability distributions with numerical simulation data, providing a more realistic assessment of infection risk. Using the proposed approach, this study introduces a novel method for estimating infection risk that accounts for dynamic variations driven by real-world epidemic conditions. By integrating epidemiological data, the approach quantifies how infection transmission risk changes significantly with societal infection prevalence, occupancy, and ventilation rate.

Comparisons of the proposed model with ASHRAE Standard 241—based on a modified Wells-Riley model—highlight that the latter tends to overestimate infection risk, potentially leading to unnecessary increases in ventilation and energy consumption. The analysis further shows that while both approaches converge under specific conditions (when the product of infection rate and population size is a whole number), only the proposed model reproduces the stepwise changes in risk that reflect realistic, discrete variations in the number of infectious individuals.

Ultimately, this study underscores the importance of accurate assumptions and probabilistic modeling in CFD-based assessments of indoor infection transmission. The proposed framework provides a more robust and realistic basis for ventilation system design, balancing infection control effectiveness with energy efficiency.