Towards optimal design of patient isolation units in emergency rooms to prevent airborne virus transmission: From computational fluid dynamics to data-driven modeling.

Comput Biol Med

Department of Biomedical Engineering, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea; Institute of Bioengineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea; Artificial Intelligence Institute, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea. Electronic address:

Published: May 2024

AI Article Synopsis

  • Patient isolation units (PIUs) are effective for infection control, but optimizing their design typically requires extensive computational resources, which this study aims to address using data-driven models.
  • The study used computational fluid dynamics (CFD) to examine how various PIU settings and room conditions impact ventilation and isolation, focusing on airflow patterns and particle dispersion from coughing.
  • Key findings indicate that while physical isolation alone isn't enough to stop particle spread, the addition of a fan filter unit (FFU) significantly improves isolation performance, with its positioning being the most crucial factor influencing PIU effectiveness.

Article Abstract

Background: Patient isolation units (PIUs) can be an effective method for effective infection control. Computational fluid dynamics (CFD) is commonly used for PIU design; however, optimizing this design requires extensive computational resources. Our study aims to provide data-driven models to determine the PIU settings, thereby promoting a more rapid design process.

Method: Using CFD simulations, we evaluated various PIU parameters and room conditions to assess the impact of PIU installation on ventilation and isolation. We investigated particle dispersion from coughing subjects and airflow patterns. Machine-learning models were trained using CFD simulation data to estimate the performance and identify significant parameters.

Results: Physical isolation alone was insufficient to prevent the dispersion of smaller particles. However, a properly installed fan filter unit (FFU) generally enhanced the effectiveness of physical isolation. Ventilation and isolation performance under various conditions were predicted with a mean absolute percentage error of within 13%. The position of the FFU was found to be the most important factor affecting the PIU performance.

Conclusion: Data-driven modeling based on CFD simulations can expedite the PIU design process by offering predictive capabilities and clarifying important performance factors. Reducing the time required to design a PIU is critical when a rapid response is required.

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Source
http://dx.doi.org/10.1016/j.compbiomed.2024.108309DOI Listing

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