A CFD-informed barn-level swine disease dissemination model and its use for ventilation optimization
The airborne spread of infectious livestock diseases plays a crucial role in the propagation of epidemics, particularly in populations confined to densely populated facilities, such as commercial swine barns. In this study, we present a framework to study airborne disease dissemination within commer...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Epidemics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1755436525000234 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849387126204923904 |
|---|---|
| author | Maryam Safari Christian Fleming Jason A. Galvis Aniruddha Deka Felipe Sanchez Gustavo Machado Chi-An Yeh |
| author_facet | Maryam Safari Christian Fleming Jason A. Galvis Aniruddha Deka Felipe Sanchez Gustavo Machado Chi-An Yeh |
| author_sort | Maryam Safari |
| collection | DOAJ |
| description | The airborne spread of infectious livestock diseases plays a crucial role in the propagation of epidemics, particularly in populations confined to densely populated facilities, such as commercial swine barns. In this study, we present a framework to study airborne disease dissemination within commercial swine barns and facilitate the strategic design of control actions, including optimization of ventilation and placement of sick animals (sick pen). This framework is based on a susceptible–infected–recovered (SIR) model that accounts for the between-pen disease spread within swine barns. A pen-to-pen contact network is used to construct a transmission matrix according to the transport of airborne respiratory pathogens across pens in the barns, via our Reynolds-averaged Navier–Stokes computational fluid dynamics (CFD) solver. By employing this CFD-augmented SIR model, we demonstrated that the location of the sick pen and the barn ventilation configuration played crucial roles in modifying disease dissemination dynamics at the barn level. In addition, we examined the effect of natural ventilation through different curtain adjustments. We observed that curtain adjustments either suppress the disease spread by an average of 64.8% or exacerbate the outbreak potential by an average of 5.8%, compared to the scenario where side curtains are not raised. Furthermore, we optimize the ventilation configuration via the selection and placement of ventilation fans through the integration of the CFD-augmented framework with the genetic algorithm to minimize the dissemination of swine disease within barns. Compared to the original barn ventilation settings, our optimized ventilation system significantly reduced disease spread by an average of 20%. Our study demonstrates that the use of the proposed framework provides a detailed understanding of the flow physics and the transport of airborne pathogens, which facilitate the optimization of ventilation systems and strategic management of sick pens within the swine barns. |
| format | Article |
| id | doaj-art-22ddcc7423b9468f8be8b202c8a3dc4b |
| institution | Kabale University |
| issn | 1755-4365 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Epidemics |
| spelling | doaj-art-22ddcc7423b9468f8be8b202c8a3dc4b2025-08-20T03:55:22ZengElsevierEpidemics1755-43652025-06-015110083510.1016/j.epidem.2025.100835A CFD-informed barn-level swine disease dissemination model and its use for ventilation optimizationMaryam Safari0Christian Fleming1Jason A. Galvis2Aniruddha Deka3Felipe Sanchez4Gustavo Machado5Chi-An Yeh6Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USADepartment of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USADepartment of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USADepartment of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USADepartment of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USADepartment of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA; Corresponding author.Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USAThe airborne spread of infectious livestock diseases plays a crucial role in the propagation of epidemics, particularly in populations confined to densely populated facilities, such as commercial swine barns. In this study, we present a framework to study airborne disease dissemination within commercial swine barns and facilitate the strategic design of control actions, including optimization of ventilation and placement of sick animals (sick pen). This framework is based on a susceptible–infected–recovered (SIR) model that accounts for the between-pen disease spread within swine barns. A pen-to-pen contact network is used to construct a transmission matrix according to the transport of airborne respiratory pathogens across pens in the barns, via our Reynolds-averaged Navier–Stokes computational fluid dynamics (CFD) solver. By employing this CFD-augmented SIR model, we demonstrated that the location of the sick pen and the barn ventilation configuration played crucial roles in modifying disease dissemination dynamics at the barn level. In addition, we examined the effect of natural ventilation through different curtain adjustments. We observed that curtain adjustments either suppress the disease spread by an average of 64.8% or exacerbate the outbreak potential by an average of 5.8%, compared to the scenario where side curtains are not raised. Furthermore, we optimize the ventilation configuration via the selection and placement of ventilation fans through the integration of the CFD-augmented framework with the genetic algorithm to minimize the dissemination of swine disease within barns. Compared to the original barn ventilation settings, our optimized ventilation system significantly reduced disease spread by an average of 20%. Our study demonstrates that the use of the proposed framework provides a detailed understanding of the flow physics and the transport of airborne pathogens, which facilitate the optimization of ventilation systems and strategic management of sick pens within the swine barns.http://www.sciencedirect.com/science/article/pii/S1755436525000234Disease spreadSwine diseasePreventive actionsAirflowComputational fluid dynamicsLivestock disease dissemination |
| spellingShingle | Maryam Safari Christian Fleming Jason A. Galvis Aniruddha Deka Felipe Sanchez Gustavo Machado Chi-An Yeh A CFD-informed barn-level swine disease dissemination model and its use for ventilation optimization Epidemics Disease spread Swine disease Preventive actions Airflow Computational fluid dynamics Livestock disease dissemination |
| title | A CFD-informed barn-level swine disease dissemination model and its use for ventilation optimization |
| title_full | A CFD-informed barn-level swine disease dissemination model and its use for ventilation optimization |
| title_fullStr | A CFD-informed barn-level swine disease dissemination model and its use for ventilation optimization |
| title_full_unstemmed | A CFD-informed barn-level swine disease dissemination model and its use for ventilation optimization |
| title_short | A CFD-informed barn-level swine disease dissemination model and its use for ventilation optimization |
| title_sort | cfd informed barn level swine disease dissemination model and its use for ventilation optimization |
| topic | Disease spread Swine disease Preventive actions Airflow Computational fluid dynamics Livestock disease dissemination |
| url | http://www.sciencedirect.com/science/article/pii/S1755436525000234 |
| work_keys_str_mv | AT maryamsafari acfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT christianfleming acfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT jasonagalvis acfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT aniruddhadeka acfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT felipesanchez acfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT gustavomachado acfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT chianyeh acfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT maryamsafari cfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT christianfleming cfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT jasonagalvis cfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT aniruddhadeka cfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT felipesanchez cfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT gustavomachado cfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization AT chianyeh cfdinformedbarnlevelswinediseasedisseminationmodelanditsuseforventilationoptimization |