Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics

Christoph Schinko; Lin Shao; J. S. Mueller-Roemer; Daniel Weber; Xingzi Zhang; Eugene Lee; Bastian Sander; Alexander Steinhardt; Volker Settgast; Kan Chen; Marius Erdt; Eva Eggeling

doi:10.5220/0000156800003124

Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics

Christoph Schinko^*, Lin Shao, J. S. Mueller-Roemer, Daniel Weber, Xingzi Zhang, Eugene Lee, Bastian Sander, Alexander Steinhardt, Volker Settgast, Kan Chen, Marius Erdt, Eva Eggeling

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

The Coronavirus Disease 2019 (COVID-19) has shown us the necessity to understand its transmission mechanisms in detail in order to establish practice in controlling such infectious diseases. An important instrument in doing so are mathematical models. However, they do not account for the spatiotemporal heterogeneity introduced by the movement and interaction of individuals with their surroundings. Computational fluid dynamics (CFD) simulations can be used to analyze transmission on micro- and mesostructure level, however become
infeasible in larger scale scenarios. Agent-based modeling (ABM) on the other hand is missing means to simulate airborne virus transmission on a micro- and mesostructure level. Therefore, we present a system that combines CFD simulations with the dynamics given by trajectories from an ABM to form a basis for producing deeper insights. The proposed system is still work in progress; thus we focus on the system architecture and show preliminary results.

Original language	English
Title of host publication	Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1
Subtitle of host publication	GRAPP
Place of Publication	Portugal
Publisher	SciTePress
Pages	278-285
Number of pages	8
Volume	1
ISBN (Electronic)	978-989-758-555-5
DOIs	https://doi.org/10.5220/0000156800003124
Publication status	Published - 6 Feb 2022
Event	17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: GRAPP 2022 - Virtuell Duration: 6 Feb 2022 → 8 Feb 2022 Conference number: 17 https://grapp.scitevents.org/

Conference

Conference	17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Abbreviated title	GRAPP 2022
City	Virtuell
Period	6/02/22 → 8/02/22
Internet address	https://grapp.scitevents.org/

Keywords

Agent-based Modeling
Clustering
Computational Fluid Dynamics
Airborne Disease Transmission Modeling

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Graphics and Computer-Aided Design
Software

Fields of Expertise

Information, Communication & Computing

Treatment code (Nähere Zuordnung)

Application

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.5220/0000156800003124Licence: CC BY-NC-ND 4.0

Cite this

Schinko, C., Shao, L., Mueller-Roemer, J. S., Weber, D., Zhang, X., Lee, E., Sander, B., Steinhardt, A., Settgast, V., Chen, K., Erdt, M., & Eggeling, E. (2022). Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP (Vol. 1, pp. 278-285). SciTePress. https://doi.org/10.5220/0000156800003124

Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics. / Schinko, Christoph ; Shao, Lin; Mueller-Roemer, J. S. et al.
Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP. Vol. 1 Portugal: SciTePress, 2022. p. 278-285.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Schinko, C , Shao, L, Mueller-Roemer, JS, Weber, D, Zhang, X, Lee, E, Sander, B, Steinhardt, A, Settgast, V, Chen, K, Erdt, M & Eggeling, E 2022, Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics. in Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP. vol. 1, SciTePress, Portugal, pp. 278-285, 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Virtuell, 6/02/22. https://doi.org/10.5220/0000156800003124

Schinko C , Shao L, Mueller-Roemer JS, Weber D, Zhang X, Lee E et al. Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP. Vol. 1. Portugal: SciTePress. 2022. p. 278-285 doi: 10.5220/0000156800003124

Schinko, Christoph ; Shao, Lin ; Mueller-Roemer, J. S. et al. / Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics. Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP. Vol. 1 Portugal : SciTePress, 2022. pp. 278-285

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abstract = "The Coronavirus Disease 2019 (COVID-19) has shown us the necessity to understand its transmission mechanisms in detail in order to establish practice in controlling such infectious diseases. An important instrument in doing so are mathematical models. However, they do not account for the spatiotemporal heterogeneity introduced by the movement and interaction of individuals with their surroundings. Computational fluid dynamics (CFD) simulations can be used to analyze transmission on micro- and mesostructure level, however becomeinfeasible in larger scale scenarios. Agent-based modeling (ABM) on the other hand is missing means to simulate airborne virus transmission on a micro- and mesostructure level. Therefore, we present a system that combines CFD simulations with the dynamics given by trajectories from an ABM to form a basis for producing deeper insights. The proposed system is still work in progress; thus we focus on the system architecture and show preliminary results.",

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AB - The Coronavirus Disease 2019 (COVID-19) has shown us the necessity to understand its transmission mechanisms in detail in order to establish practice in controlling such infectious diseases. An important instrument in doing so are mathematical models. However, they do not account for the spatiotemporal heterogeneity introduced by the movement and interaction of individuals with their surroundings. Computational fluid dynamics (CFD) simulations can be used to analyze transmission on micro- and mesostructure level, however becomeinfeasible in larger scale scenarios. Agent-based modeling (ABM) on the other hand is missing means to simulate airborne virus transmission on a micro- and mesostructure level. Therefore, we present a system that combines CFD simulations with the dynamics given by trajectories from an ABM to form a basis for producing deeper insights. The proposed system is still work in progress; thus we focus on the system architecture and show preliminary results.

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Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics

Abstract

Conference

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

UN SDGs

Access to Document

Fingerprint

Cite this