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Beyond well‐mixed: A simple probabilistic model of airborne disease transmission in indoor spaces
Author(s) -
Tan Sijian,
Zhang Zhihang,
Maki Kevin,
Fidkowski Krzysztof J.,
Capecelatro Jesse
Publication year - 2022
Publication title -
indoor air
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.387
H-Index - 99
eISSN - 1600-0668
pISSN - 0905-6947
DOI - 10.1111/ina.13015
Subject(s) - range (aeronautics) , log normal distribution , standard deviation , environmental science , dispersion (optics) , ventilation (architecture) , meteorology , statistics , transmission (telecommunications) , mixing (physics) , atmospheric dispersion modeling , hvac , simulation , mathematics , computer science , materials science , engineering , physics , air pollution , optics , telecommunications , quantum mechanics , composite material , mechanical engineering , chemistry , organic chemistry , air conditioning
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.