Modeling Contaminant Dispersion to Mitigate Airborne Infection Spread in Healthcare Environments

Modeling Contaminant Dispersion to Mitigate Airborne Infection Spread in Healthcare Environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Modeling Contaminant Dispersion to Mitigate Airborne Infection Spread in Healthcare Environments Se Yoon Park, Gihoon Kim, Minki Sung, Jin Young Park This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7152859/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Airborne transmission of infections, such as tuberculosis, measles, and coronavirus disease (COVID-19), can spread quickly in healthcare settings, particularly in high-density environments like Korean hospitals. Transmission may happen in communal areas that are affected by ventilation and airflow. This study aimed to evaluate airborne and droplet transmission through numerical simulations based on real air supply and exhaust measurements in a hospital ward. Methods A hospital ward in the Gyeonggi Province was chosen for this investigation. The locations of the air supply and exhaust vents in the patient rooms and hallways were determined, and airflow was quantified. Four sites (two within the rooms and two in the corridor) were analyzed, considering the presence of an infected patient. Computational fluid dynamics software was used to simulate airflow and model the dispersion of exhaled gaseous contaminants from patients. Results Room A maintained a negative pressure, preventing air from escaping into the hallway, while Room B maintained a positive pressure, causing air to flow into the hallway and adjacent rooms. Contaminants in Room A did not spread outside; however, contaminants from hallway vents affected other spaces. The positive pressure in Room B blocked the entry of contaminants from the hallway. Conclusion Airflow analysis demonstrated that the pressure differences between rooms and hallways significantly affected the spread of contaminants. Numerical simulations based on real-world data can help trace infected contacts and optimize room assignments for patients with airborne or droplet-borne infections, aiding in prevention efforts. Airborne transmission Ventilation mechanical Hospital design and construction Models theoretical Cross infection Figures Figure 1 Figure 2 Background Airborne transmissions of infectious diseases present significant risks to patients and healthcare staff, highlighting the need for effective infection control strategies. Adequate ventilation and pressure regulation are essential to minimize cross-contamination, particularly in facilities that manage patients with airborne infectious diseases, such as COVID-19 and tuberculosis [ 1 , 2 ]. Negative-pressure rooms are a recognized method for containing infectious particles and preventing their dissemination to other areas. However, the availability of rooms with negative pressure is restricted in many healthcare institutions [ 3 ]. Most patient quarters exhibit varying levels of positive or negative pressure; however, these conditions are not always ascertainable without direct measurements. This complicates hospitals' efforts to comprehend and manage potential transmission pathways [ 1 ]. Effective airborne infection control necessitates a multi-faceted approach that integrates engineering controls, such as ventilation systems, with administrative and personal protective measures [ 4 ]. Healthcare facilities need to carefully evaluate factors, such as air exchange rates, filtration efficiency, and directional airflow, to establish environments that minimize the transmission of airborne pathogens. Despite the common belief that airflow is adequately regulated in hospital wards, infectious agents can still propagate between rooms and corridors due to subtle airflow and pressure variations. Particularly, unrecognized pressure conditions can pose risks when placing infectious or susceptible individuals inappropriately within a ward [ 1 , 2 ]. In the absence of clear insights into airflow dynamics, hospitals may encounter challenges in effectively preventing outbreaks. This study aimed to utilize computational fluid dynamics (CFD) modeling, in conjunction with authentic ventilation data, to replicate the airflow patterns and contaminant distribution within a hospital ward. Methods Study Design A hospital ward in the Gyeonggi Province, South Korea, was chosen for this investigation. Four sites (two in patient rooms and two in the corridor) were selected, assuming the presence of an index patient with an infectious illness at each location. Numerical simulations were employed to visualize the dispersion of gaseous pollutants (passive scalars) and airflow patterns during exhalation. Commercial CFD software was used for this analysis. Measurement of Supply and Exhaust Airflow in the Ward The positions and airflow rates of 67 air supplies and 80 exhaust vents in the selected wards were evaluated. Three supply and two exhaust vents were not directly measured due to site limitations; their airflow rates were approximated using design values and nearby exhaust vents. The pressure status (positive or negative) of each room was established according to the airflow rates of the supply and exhaust vents (Supplemental Fig. 1). CFD Modelling of the Ward For CFD analysis, a ward model was developed based on its architectural plans (Supplemental Fig. 2). The air supply and exhaust vents were modeled to replicate the actual installation angles, with the air supplied diagonally. All doors in the rooms and auxiliary spaces were considered closed, and the door leakage areas were modeled with an undercut height of 50 mm (Supplemental Fig. 3). The CFD analysis was conducted under steady-state conditions, excluding heat generation, and utilized the actual measured airflow data. Infectious particles were presumed to be airborne with a diameter of < 5 µm, modeled as passive scalars. The analysis assumes that the contaminant particles follow the airflow patterns. The passive scalar was set to a concentration of 1 to visualize the distribution of the infectious particles, as a direct measurement of the contaminant concentration was not feasible. Results Results of Supply and Exhaust Airflow Measurements in the Ward The total supply airflow in the ward was 16,514 m³/h, whereas the exhaust airflow was 17,720 m³/h, indicating that the exhaust airflow exceeded the supply airflow by 1,206 m³/h. This suggests that air was likely to flow into the study ward from adjacent wards. Room A had a higher exhaust airflow (supply: 225 m³/h, exhaust: 480 m³/h), thus creating negative pressure, while Room B had a higher supply airflow (supply: 865 m³/h, exhaust: 766 m³/h), resulting in positive pressure (Supplemental Fig. 1). CFD Analysis of the Ward CFD analysis confirms the airflow patterns in a negatively pressurized patient room (A) and the air supplied through the diffusers in a positively pressurized room (B). In Room A, the negative pressure prevented contaminants from escaping into the corridor or adjacent rooms. Therefore, if an infected patient is admitted to Room A and the door remains closed, the likelihood of infection spreading to the corridor or other rooms is low (Fig. 1 A). The airflow from the diffusers in the corridor showed that the air supplied from diffusers A and B flowed into Room A, which was negatively pressurized, whereas no air entered Room B, which was positively pressurized (Fig. 1 B). In terms of contaminant concentration distribution, Room A maintained a negative pressure, preventing the escape of contaminants into the corridor or adjacent rooms. Therefore, if an infected patient was in Room A with the door closed, the risk of spreading the infection was low (Fig. 2 A). Figure 2 B and 2 C illustrate the distribution of contaminant concentrations when contaminants were released from diffusers A and B into the corridor. The contaminants from the corridor diffusers entered Room A and other negatively pressurized rooms, while no contaminants entered Room B or other positively pressurized rooms. This indicates that if an infected patient or visitor releases contaminants into the corridor, there is a potential for contaminants to enter negatively pressurized rooms. Discussion This study highlights several key insights into the dynamics of airborne transmission in hospital wards and emphasizes the practical challenges faced by healthcare institutions in maintaining effective infection control. One of the most important findings was that pressure differentials between rooms, even within the same ward, can create conditions that allow airborne pathogens to spread. Negative-pressure rooms are intended to contain infectious agents; however, their availability is limited, and they can be contaminated from adjacent spaces, such as hallways, without appropriate precautions. Similarly, positive-pressure rooms can prevent contaminants from entering but may facilitate the outward spread of airborne pathogens if the source is within the room. These findings underscore the necessity for a nuanced understanding of airflow patterns to manage transmission risks effectively. This study’s findings indicate that healthcare facilities should take proactive measures to isolate patients suspected of having airborne infectious diseases to prevent transmission. Due to the variability in room pressure conditions, hospitals should not solely rely on design specifications but should also conduct real-time airflow assessments to ensure effective isolation. Moreover, as natural ventilation can occur at any time, ongoing airflow assessment is crucial in hospitals [ 5 ]. In cases where negative-pressure rooms are not accessible or are limited, hospitals should explore alternative strategies, such as repurposing rooms based on airflow measurements, to reduce transmission risks. This proactive approach is especially vital in high-risk settings where prompt decisions are necessary to avert outbreaks. Furthermore, this study emphasizes the significance of utilizing simulations, such as CFD modeling, to supplement regular infection control practices [ 6 ]. By simulating airflow dynamics, hospitals can forecast how airborne pathogens might disseminate under various scenarios and utilize this data to enhance patient placement, particularly for vulnerable populations [ 7 ]. For example, comprehending airflow patterns can aid hospitals in guaranteeing that immunocompromised patients are positioned in more secure surroundings, thereby lessening their exposure to potential airborne contaminants. A limitation of this study is its reliance on steady-state CFD simulations, which may not adequately reflect dynamic factors, such as door movements, staff activities, or ventilation fluctuations. These variables can affect real-world contaminant dispersion, making it challenging to directly apply the findings to diverse healthcare environments. In summary, this study emphasizes the significance of monitoring and controlling airflow patterns in hospital wards to reduce the risk of airborne transmission. Healthcare institutions should include routine airflow evaluations and simulations in infection control strategies to safeguard patients and staff. Subsequent research should investigate the creation of real-time monitoring systems to consistently evaluate airflow conditions and offer insights into infection prevention. These results establish a basis for hospitals to embrace more dynamic data-driven methods for infection control, guaranteeing that susceptible patients receive optimal protection. Abbreviations Coronavirus disease (COVID-19), Computational fluid dynamics (CFD) Declarations Ethics approval statement Not applicable. Conflict of interest statement The authors declare that they have no competing interests. Funding statement This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2023-KH135442) and the research fund of Hanyang University (grant number: HY-202500000001280). Authors' contributions S.Y.P. and G.K. contributed equally to this work as co-first authors. S.Y.P. conceptualized the study, conducted the literature review, analyzed the data, and wrote the original manuscript. G.K. assisted with the computational fluid dynamics modeling and numerical simulations. M.S. designed and supervised the computational fluid dynamics modeling, conducted airflow measurements, and led the experimental methodology. J.Y.P. supervised the overall project, provided critical review and revision of the manuscript, and served as the corresponding author. All authors reviewed and approved the final manuscript. All authors read and approved the final manuscript. Acknowledgments: Not applicable. References Park SY, Yu J, Bae S, Song JS, Lee SY, Kim JH, et al. Ventilation strategies based on an aerodynamic analysis during a large-scale SARS-CoV-2 outbreak in an acute-care hospital. J Clin Virol. 2023; doi :10.1016/j.jcv.2023.105502. Park SY, Kim TH, Lee E, Loeb M, Jeong YS, Kim JH, et al. A SARS-CoV-2 outbreak associated with vaccine breakthrough in an acute care hospital. Am J Infect Control. 2022:50:1006-12. doi: 10.1016/j.ajic.2022.05.010. Wang F, Permana I, Chaerasari C, Panigrahi B, Rakshit D. Infection Control Improvement of a Negative-Pressurized Pediatric Intensive Care Unit. Healthcare (Basel). 2021:9:1500. doi: 10.3390/healthcare9111500. Wee LE, Venkatachalam I, Sim XYJ, Tan KB, Wen R, Tham CK, et al. Containment of COVID-19 and reduction in healthcare-associated respiratory viral infections through a multi-tiered infection control strategy. Infect Dis Health. 2021:26:123-31. doi: 10.1016/j.idh.2020.11.004. Edwards AJ, King MF, López-García M, Peckham D, Noakes CJ. Assessing the effects of transient weather conditions on airborne transmission risk in naturally ventilated hospitals. J Hosp Infect. 2024;148:1-10. doi: 10.1016/j.jhin.2024.02.017. Jung M, Chung WJ, Sung M, Jo S, Hong J. Analysis of Infection Transmission Routes through Exhaled Breath and Cough Particle Dispersion in a General Hospital. Int J Environ Res Public Health. 2022:19:2512. doi: 10.3390/ijerph19052512. Horne J, Dunne N, Singh N, Safiuddin M, Esmaeili N, Erenler M, et al. Building parameters linked with indoor transmission of SARS-CoV-2. Environ Res. 2023:238:117156. doi: 10.1016/j.envres.2023.117156. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7152859","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":518395863,"identity":"4e3350e4-6e7d-4f54-a36f-82e329cee35e","order_by":0,"name":"Se Yoon Park","email":"","orcid":"","institution":"Hanyang University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Se","middleName":"Yoon","lastName":"Park","suffix":""},{"id":518395864,"identity":"97050cf4-78b4-43ae-a6fa-5089411e7f95","order_by":1,"name":"Gihoon Kim","email":"","orcid":"","institution":"Sejong 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Airflow of patient exhalation and supply air in the room.\u003c/p\u003e\n\u003cp\u003eB. Airflow supplied from diffusers in the corridor.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7152859/v1/37a03261bfeeec0abc31627e.png"},{"id":92089779,"identity":"a1117b30-aa60-4243-87bb-3ab7088b568e","added_by":"auto","created_at":"2025-09-24 13:25:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":933413,"visible":true,"origin":"","legend":"\u003cp\u003eA. Concentration distribution of contaminant from patients.\u003c/p\u003e\n\u003cp\u003eB. Concentration distribution of contaminant from A diffuser\u003c/p\u003e\n\u003cp\u003eC. Concentration distribution of contaminant from B diffuser\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7152859/v1/3fa46ad9b418f0acb33590bb.png"},{"id":99316800,"identity":"a3a6a0b6-77ee-4eee-b691-ae767b10ebb6","added_by":"auto","created_at":"2025-12-31 16:29:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1768457,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7152859/v1/05fde88d-c679-435f-bccc-f0c5b9cf3df6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling Contaminant Dispersion to Mitigate Airborne Infection Spread in Healthcare Environments","fulltext":[{"header":"Background","content":"\u003cp\u003eAirborne transmissions of infectious diseases present significant risks to patients and healthcare staff, highlighting the need for effective infection control strategies. Adequate ventilation and pressure regulation are essential to minimize cross-contamination, particularly in facilities that manage patients with airborne infectious diseases, such as COVID-19 and tuberculosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Negative-pressure rooms are a recognized method for containing infectious particles and preventing their dissemination to other areas. However, the availability of rooms with negative pressure is restricted in many healthcare institutions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Most patient quarters exhibit varying levels of positive or negative pressure; however, these conditions are not always ascertainable without direct measurements. This complicates hospitals' efforts to comprehend and manage potential transmission pathways [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEffective airborne infection control necessitates a multi-faceted approach that integrates engineering controls, such as ventilation systems, with administrative and personal protective measures [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Healthcare facilities need to carefully evaluate factors, such as air exchange rates, filtration efficiency, and directional airflow, to establish environments that minimize the transmission of airborne pathogens. Despite the common belief that airflow is adequately regulated in hospital wards, infectious agents can still propagate between rooms and corridors due to subtle airflow and pressure variations. Particularly, unrecognized pressure conditions can pose risks when placing infectious or susceptible individuals inappropriately within a ward [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the absence of clear insights into airflow dynamics, hospitals may encounter challenges in effectively preventing outbreaks. This study aimed to utilize computational fluid dynamics (CFD) modeling, in conjunction with authentic ventilation data, to replicate the airflow patterns and contaminant distribution within a hospital ward.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA hospital ward in the Gyeonggi Province, South Korea, was chosen for this investigation. Four sites (two in patient rooms and two in the corridor) were selected, assuming the presence of an index patient with an infectious illness at each location. Numerical simulations were employed to visualize the dispersion of gaseous pollutants (passive scalars) and airflow patterns during exhalation. Commercial CFD software was used for this analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurement of Supply and Exhaust Airflow in the Ward\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe positions and airflow rates of 67 air supplies and 80 exhaust vents in the selected wards were evaluated. Three supply and two exhaust vents were not directly measured due to site limitations; their airflow rates were approximated using design values and nearby exhaust vents. The pressure status (positive or negative) of each room was established according to the airflow rates of the supply and exhaust vents (Supplemental Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCFD Modelling of the Ward\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor CFD analysis, a ward model was developed based on its architectural plans (Supplemental Fig.\u0026nbsp;2). The air supply and exhaust vents were modeled to replicate the actual installation angles, with the air supplied diagonally. All doors in the rooms and auxiliary spaces were considered closed, and the door leakage areas were modeled with an undercut height of 50 mm (Supplemental Fig.\u0026nbsp;3). The CFD analysis was conducted under steady-state conditions, excluding heat generation, and utilized the actual measured airflow data. Infectious particles were presumed to be airborne with a diameter of \u0026lt; 5 µm, modeled as passive scalars. The analysis assumes that the contaminant particles follow the airflow patterns. The passive scalar was set to a concentration of 1 to visualize the distribution of the infectious particles, as a direct measurement of the contaminant concentration was not feasible.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eResults of Supply and Exhaust Airflow Measurements in the Ward\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe total supply airflow in the ward was 16,514 m\u0026sup3;/h, whereas the exhaust airflow was 17,720 m\u0026sup3;/h, indicating that the exhaust airflow exceeded the supply airflow by 1,206 m\u0026sup3;/h. This suggests that air was likely to flow into the study ward from adjacent wards. Room A had a higher exhaust airflow (supply: 225 m\u0026sup3;/h, exhaust: 480 m\u0026sup3;/h), thus creating negative pressure, while Room B had a higher supply airflow (supply: 865 m\u0026sup3;/h, exhaust: 766 m\u0026sup3;/h), resulting in positive pressure (Supplemental Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCFD Analysis of the Ward\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCFD analysis confirms the airflow patterns in a negatively pressurized patient room (A) and the air supplied through the diffusers in a positively pressurized room (B). In Room A, the negative pressure prevented contaminants from escaping into the corridor or adjacent rooms. Therefore, if an infected patient is admitted to Room A and the door remains closed, the likelihood of infection spreading to the corridor or other rooms is low (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The airflow from the diffusers in the corridor showed that the air supplied from diffusers A and B flowed into Room A, which was negatively pressurized, whereas no air entered Room B, which was positively pressurized (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn terms of contaminant concentration distribution, Room A maintained a negative pressure, preventing the escape of contaminants into the corridor or adjacent rooms. Therefore, if an infected patient was in Room A with the door closed, the risk of spreading the infection was low (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC illustrate the distribution of contaminant concentrations when contaminants were released from diffusers A and B into the corridor. The contaminants from the corridor diffusers entered Room A and other negatively pressurized rooms, while no contaminants entered Room B or other positively pressurized rooms. This indicates that if an infected patient or visitor releases contaminants into the corridor, there is a potential for contaminants to enter negatively pressurized rooms.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights several key insights into the dynamics of airborne transmission in hospital wards and emphasizes the practical challenges faced by healthcare institutions in maintaining effective infection control. One of the most important findings was that pressure differentials between rooms, even within the same ward, can create conditions that allow airborne pathogens to spread. Negative-pressure rooms are intended to contain infectious agents; however, their availability is limited, and they can be contaminated from adjacent spaces, such as hallways, without appropriate precautions. Similarly, positive-pressure rooms can prevent contaminants from entering but may facilitate the outward spread of airborne pathogens if the source is within the room. These findings underscore the necessity for a nuanced understanding of airflow patterns to manage transmission risks effectively.\u003c/p\u003e\u003cp\u003eThis study\u0026rsquo;s findings indicate that healthcare facilities should take proactive measures to isolate patients suspected of having airborne infectious diseases to prevent transmission. Due to the variability in room pressure conditions, hospitals should not solely rely on design specifications but should also conduct real-time airflow assessments to ensure effective isolation. Moreover, as natural ventilation can occur at any time, ongoing airflow assessment is crucial in hospitals [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In cases where negative-pressure rooms are not accessible or are limited, hospitals should explore alternative strategies, such as repurposing rooms based on airflow measurements, to reduce transmission risks. This proactive approach is especially vital in high-risk settings where prompt decisions are necessary to avert outbreaks.\u003c/p\u003e\u003cp\u003eFurthermore, this study emphasizes the significance of utilizing simulations, such as CFD modeling, to supplement regular infection control practices [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. By simulating airflow dynamics, hospitals can forecast how airborne pathogens might disseminate under various scenarios and utilize this data to enhance patient placement, particularly for vulnerable populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For example, comprehending airflow patterns can aid hospitals in guaranteeing that immunocompromised patients are positioned in more secure surroundings, thereby lessening their exposure to potential airborne contaminants.\u003c/p\u003e\u003cp\u003eA limitation of this study is its reliance on steady-state CFD simulations, which may not adequately reflect dynamic factors, such as door movements, staff activities, or ventilation fluctuations. These variables can affect real-world contaminant dispersion, making it challenging to directly apply the findings to diverse healthcare environments.\u003c/p\u003e\u003cp\u003eIn summary, this study emphasizes the significance of monitoring and controlling airflow patterns in hospital wards to reduce the risk of airborne transmission. Healthcare institutions should include routine airflow evaluations and simulations in infection control strategies to safeguard patients and staff. Subsequent research should investigate the creation of real-time monitoring systems to consistently evaluate airflow conditions and offer insights into infection prevention. These results establish a basis for hospitals to embrace more dynamic data-driven methods for infection control, guaranteeing that susceptible patients receive optimal protection.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCoronavirus disease (COVID-19), Computational fluid dynamics (CFD)\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by a grant of the Korea Health Technology R\u0026amp;D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health \u0026amp; Welfare, Republic of Korea (grant number: RS-2023-KH135442) and the research fund of Hanyang University (grant number: HY-202500000001280). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.Y.P. and G.K. contributed equally to this work as co-first authors. S.Y.P. conceptualized the study, conducted the literature review, analyzed the data, and wrote the original manuscript. G.K. assisted with the computational fluid dynamics modeling and numerical simulations. M.S. designed and supervised the computational fluid dynamics modeling, conducted airflow measurements, and led the experimental methodology. J.Y.P. supervised the overall project, provided critical review and revision of the manuscript, and served as the corresponding author. All authors reviewed and approved the final manuscript. All authors read and approved the final manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePark SY, Yu J, Bae S, Song JS, Lee SY, Kim JH, et al. Ventilation strategies based on an aerodynamic analysis during a large-scale SARS-CoV-2 outbreak in an acute-care hospital. J Clin Virol. 2023; doi :10.1016/j.jcv.2023.105502. \u003c/li\u003e\n\u003cli\u003ePark SY, Kim TH, Lee E, Loeb M, Jeong YS, Kim JH, et al. A SARS-CoV-2 outbreak associated with vaccine breakthrough in an acute care hospital. Am J Infect Control. 2022:50:1006-12. doi: 10.1016/j.ajic.2022.05.010. \u003c/li\u003e\n\u003cli\u003eWang F, Permana I, Chaerasari C, Panigrahi B, Rakshit D. Infection Control Improvement of a Negative-Pressurized Pediatric Intensive Care Unit. Healthcare (Basel). 2021:9:1500. doi: 10.3390/healthcare9111500. \u003c/li\u003e\n\u003cli\u003eWee LE, Venkatachalam I, Sim XYJ, Tan KB, Wen R, Tham CK, et al. Containment of COVID-19 and reduction in healthcare-associated respiratory viral infections through a multi-tiered infection control strategy. Infect Dis Health. 2021:26:123-31. doi: 10.1016/j.idh.2020.11.004.\u003c/li\u003e\n\u003cli\u003eEdwards AJ, King MF, L\u0026oacute;pez-Garc\u0026iacute;a M, Peckham D, Noakes CJ. Assessing the effects of transient weather conditions on airborne transmission risk in naturally ventilated hospitals. J Hosp Infect. 2024;148:1-10. doi: 10.1016/j.jhin.2024.02.017. \u003c/li\u003e\n\u003cli\u003eJung M, Chung WJ, Sung M, Jo S, Hong J. Analysis of Infection Transmission Routes through Exhaled Breath and Cough Particle Dispersion in a General Hospital. Int J Environ Res Public Health. 2022:19:2512. doi: 10.3390/ijerph19052512. \u003c/li\u003e\n\u003cli\u003eHorne J, Dunne N, Singh N, Safiuddin M, Esmaeili N, Erenler M, et al. Building parameters linked with indoor transmission of SARS-CoV-2. Environ Res. 2023:238:117156. doi: 10.1016/j.envres.2023.117156. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Airborne transmission, Ventilation, mechanical, Hospital design and construction, Models, theoretical, Cross infection","lastPublishedDoi":"10.21203/rs.3.rs-7152859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7152859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAirborne transmission of infections, such as tuberculosis, measles, and coronavirus disease (COVID-19), can spread quickly in healthcare settings, particularly in high-density environments like Korean hospitals. Transmission may happen in communal areas that are affected by ventilation and airflow. This study aimed to evaluate airborne and droplet transmission through numerical simulations based on real air supply and exhaust measurements in a hospital ward.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA hospital ward in the Gyeonggi Province was chosen for this investigation. The locations of the air supply and exhaust vents in the patient rooms and hallways were determined, and airflow was quantified. Four sites (two within the rooms and two in the corridor) were analyzed, considering the presence of an infected patient. Computational fluid dynamics software was used to simulate airflow and model the dispersion of exhaled gaseous contaminants from patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eRoom A maintained a negative pressure, preventing air from escaping into the hallway, while Room B maintained a positive pressure, causing air to flow into the hallway and adjacent rooms. Contaminants in Room A did not spread outside; however, contaminants from hallway vents affected other spaces. The positive pressure in Room B blocked the entry of contaminants from the hallway.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAirflow analysis demonstrated that the pressure differences between rooms and hallways significantly affected the spread of contaminants. Numerical simulations based on real-world data can help trace infected contacts and optimize room assignments for patients with airborne or droplet-borne infections, aiding in prevention efforts.\u003c/p\u003e","manuscriptTitle":"Modeling Contaminant Dispersion to Mitigate Airborne Infection Spread in Healthcare Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 13:25:31","doi":"10.21203/rs.3.rs-7152859/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8466ed8f-c16a-49fc-90e2-b9dcd3ddd486","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T03:24:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 13:25:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7152859","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7152859","identity":"rs-7152859","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}