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The recent outbreak of respiratory infectious diseases such as Middle East Respiratory Syndrome (MERS) and Coronavirus Disease-19 (COVID-19) highlights the critical role of Air Infection Isolation (AII) rooms in preventing the spread of disease. However, the risk of contaminant leakage from the AII room during medical staff interactions remains a significant challenge. This study advances our understanding by employing Computational Fluid Dynamics (CFD) simulations with a novel approach. While CFD simulations mitigate some limitations of field experiments, such as measurement resolution and variable changes, their accuracy is often limited by the oversimplification of models, particularly in representing human models and movement. Unlike prior studies, we incorporate minimal simplification, featuring a human model with Personal Protective Equipment (PPE) and realistic walking motions. This study explores four distinct scenarios with varied human movements, offering a comprehensive analysis of airflow and contaminant dispersion in AII rooms. The results indicated significant differences in airflow patterns and contaminant distribution when realistic walking motion was implemented in the human model, as opposed to simplified movement models. The contaminant leakage from the AII room ranged from 20.6 % to 28.6 %, suggesting that prior studies may underestimate infection risks due to oversimplified human models and motions. These findings underscore the importance of implementing realistic human models and motions in CFD simulations for accurate infection risk assessment in AII rooms. Additionally, our study opens pathways for future research to explore more complex human interactions in clinical settings, enhancing infection control strategies.
Jo et al. (Wed,) studied this question.