Towards optimal design of patient isolation units in emergency rooms to prevent airborne virus transmission: From computational fluid dynamics to data-driven modeling
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...
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| Vydané v: | Computers in biology and medicine Ročník 173; s. 108309 |
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| Hlavní autori: | , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
Elsevier Ltd
01.05.2024
Elsevier Limited |
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| ISSN: | 0010-4825, 1879-0534, 1879-0534 |
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| Abstract | 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.
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.
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.
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.
[Display omitted]
•Machine-learning models trained with computational fluid dynamics simulation data•Data-driven models inform important variables for patient isolation unit design•Data-driven models can expedite the design process of patient isolation units•Physical isolation alone is insufficient to prevent small particle dispersion•Properly installed fan filter unit enhanced the effectiveness of physical isolation |
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| AbstractList | 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.BACKGROUNDPatient 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.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.METHODUsing 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.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.RESULTSPhysical 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.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.CONCLUSIONData-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. 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. 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. 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. 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. [Display omitted] •Machine-learning models trained with computational fluid dynamics simulation data•Data-driven models inform important variables for patient isolation unit design•Data-driven models can expedite the design process of patient isolation units•Physical isolation alone is insufficient to prevent small particle dispersion•Properly installed fan filter unit enhanced the effectiveness of physical isolation BackgroundPatient 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.MethodUsing 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.ResultsPhysical 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.ConclusionData-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. 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. 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. 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. 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. AbstractBackgroundPatient 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. MethodUsing 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. ResultsPhysical 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. ConclusionData-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. |
| ArticleNumber | 108309 |
| Author | Park, Sungwoo Lee, Kyung-Min Jeon, Byoungjun Choi, Dong Hyun Shin, Sang Do Cho, Seung Yeon Lim, Min Hyuk Shim, Jae Woo Kim, Young Gyun Yeo, Myoung-Souk Zo, Hangman Kim, Sungwan Baek, Changhoon Lee, Jong Hyeon Yoon, Dan Kim, Byeong Soo Cho, Minwoo |
| Author_xml | – sequence: 1 givenname: Jong Hyeon surname: Lee fullname: Lee, Jong Hyeon organization: Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea – sequence: 2 givenname: Jae Woo surname: Shim fullname: Shim, Jae Woo organization: Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea – sequence: 3 givenname: Min Hyuk orcidid: 0000-0003-1547-2804 surname: Lim fullname: Lim, Min Hyuk organization: Graduate School of Health Science and Technology, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, Republic of Korea – sequence: 4 givenname: Changhoon surname: Baek fullname: Baek, Changhoon organization: Department of Transdisciplinary Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea – sequence: 5 givenname: Byoungjun orcidid: 0000-0003-0127-6490 surname: Jeon fullname: Jeon, Byoungjun organization: Innovative Medical Technology Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea – sequence: 6 givenname: Minwoo surname: Cho fullname: Cho, Minwoo organization: Department of Transdisciplinary Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea – sequence: 7 givenname: Sungwoo orcidid: 0000-0002-0027-5085 surname: Park fullname: Park, Sungwoo organization: Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea – sequence: 8 givenname: Dong Hyun orcidid: 0000-0001-6036-1404 surname: Choi fullname: Choi, Dong Hyun organization: Department of Biomedical Engineering, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea – sequence: 9 givenname: Byeong Soo orcidid: 0000-0001-8767-9842 surname: Kim fullname: Kim, Byeong Soo organization: Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea – sequence: 10 givenname: Dan orcidid: 0000-0002-5657-5984 surname: Yoon fullname: Yoon, Dan organization: Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea – sequence: 11 givenname: Young Gyun orcidid: 0000-0003-1231-9097 surname: Kim fullname: Kim, Young Gyun organization: Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea – sequence: 12 givenname: Seung Yeon orcidid: 0000-0002-8756-1331 surname: Cho fullname: Cho, Seung Yeon organization: Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea – sequence: 13 givenname: Kyung-Min orcidid: 0000-0002-1373-5407 surname: Lee fullname: Lee, Kyung-Min organization: International Vaccine Institute, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea – sequence: 14 givenname: Myoung-Souk surname: Yeo fullname: Yeo, Myoung-Souk organization: Department of Architecture and Architectural Engineering, Seoul National University College of Engineering, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea – sequence: 15 givenname: Hangman surname: Zo fullname: Zo, Hangman organization: Department of Architecture and Architectural Engineering, Seoul National University College of Engineering, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea – sequence: 16 givenname: Sang Do surname: Shin fullname: Shin, Sang Do organization: Laboratory of Emergency Medical Services, Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea – sequence: 17 givenname: Sungwan orcidid: 0000-0002-9318-849X surname: Kim fullname: Kim, Sungwan email: sungwan@snu.ac.kr organization: Department of Biomedical Engineering, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38520923$$D View this record in MEDLINE/PubMed |
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| Keywords | COVID-19 Patient isolation unit Computational fluid dynamics Data-driven machine-learning-based modeling Airborne virus |
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| Snippet | Patient isolation units (PIUs) can be an effective method for effective infection control. Computational fluid dynamics (CFD) is commonly used for PIU design;... AbstractBackgroundPatient isolation units (PIUs) can be an effective method for effective infection control. Computational fluid dynamics (CFD) is commonly... BackgroundPatient isolation units (PIUs) can be an effective method for effective infection control. Computational fluid dynamics (CFD) is commonly used for... |
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| SubjectTerms | Air flow Airborne virus Computational fluid dynamics Computer applications Computer Simulation COVID-19 Data-driven machine-learning-based modeling Design optimization Emergency medical care Emergency medical services Emergency Service, Hospital Humans Hydrodynamics Infection Control - methods Infectious diseases Internal Medicine Isolation units Machine learning Mathematical models Other Pandemics Parameter identification Patient Isolation Patient isolation unit Simulation Ventilation |
| Title | Towards optimal design of patient isolation units in emergency rooms to prevent airborne virus transmission: From computational fluid dynamics to data-driven modeling |
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