Beyond well‐mixed: A simple probabilistic model of airborne disease transmission in indoor spaces. (18th March 2022)

• Record Type: Journal Article
• Authors: Tan, Sijian; Zhang, Zhihang; Maki, Kevin; Fidkowski, Krzysztof J.; Capecelatro, Jesse
• Is Part Of: Indoor air. Volume 32:Number 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-18
• Subjects: aerosol -- CFD -- disease transmission -- probabilistic model -- risk -- well‐mixed; Indoor air pollution -- Periodicals; Sick building syndrome -- Periodicals; Ventilation -- Periodicals; 613.5
• DOI: 10.1111/ina.13015
• Languages: English
• ISSNs: 0905-6947

Abstract

Abstract: We develop a simple model for assessing risk of airborne disease transmission that accounts for non‐uniform mixing in indoor spaces and is compatible with existing epidemiological models. A database containing 174 high‐resolution simulations of airflow in classrooms, lecture halls, and buses is generated and used to quantify the spatial distribution of expiratory droplet nuclei for a wide range of ventilation rates, exposure times, and room configurations. Imperfect mixing due to obstructions, buoyancy, and turbulent dispersion results in concentration fields with significant variance. The spatial non‐uniformity is found to be accurately described by a shifted lognormal distribution. A well‐mixed mass balance model is used to predict the mean, and the standard deviation is parameterized based on ventilation rate and room geometry. When employed in a dose–response function risk model, infection probability can be estimated considering spatial heterogeneity that contributes to both short‐ and long‐range transmission.