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

^*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

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.

Originalsprache	englisch
Titel	Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1
Untertitel	GRAPP
Erscheinungsort	Portugal
Herausgeber (Verlag)	SciTePress
Seiten	278-285
Seitenumfang	8
Band	1
ISBN (elektronisch)	978-989-758-555-5
DOIs	https://doi.org/10.5220/0000156800003124
Publikationsstatus	Veröffentlicht - 6 Feb. 2022
Veranstaltung	17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: GRAPP 2022 - Virtuell Dauer: 6 Feb. 2022 → 8 Feb. 2022 Konferenznummer: 17 https://grapp.scitevents.org/

Konferenz

Konferenz	17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Kurztitel	GRAPP 2022
Ort	Virtuell
Zeitraum	6/02/22 → 8/02/22
Internetadresse	https://grapp.scitevents.org/

ASJC Scopus subject areas

Artificial intelligence
Angewandte Informatik
Computergrafik und computergestütztes Design
Software

Fields of Expertise

Information, Communication & Computing

Treatment code (Nähere Zuordnung)

Application

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

Zugriff auf Dokument

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

Dieses zitieren

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 (Band 1, S. 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. Band 1 Portugal: SciTePress, 2022. S. 278-285.

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

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. Bd. 1, SciTePress, Portugal, S. 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. Band 1. Portugal: SciTePress. 2022. S. 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. Band 1 Portugal : SciTePress, 2022. S. 278-285

@inproceedings{75f225e3dec4464cb98e92d3c5a4eb14,

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

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.",

keywords = "Agent-based Modeling, Clustering, Computational Fluid Dynamics, Airborne Disease Transmission Modeling",

author = "Christoph Schinko and Lin Shao and Mueller-Roemer, {J. S.} and Daniel Weber and Xingzi Zhang and Eugene Lee and Bastian Sander and Alexander Steinhardt and Volker Settgast and Kan Chen and Marius Erdt and Eva Eggeling",

year = "2022",

month = feb,

day = "6",

doi = "10.5220/0000156800003124",

language = "English",

volume = "1",

pages = "278--285",

booktitle = "Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1",

publisher = "SciTePress",

address = "Portugal",

note = "17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications : GRAPP 2022, GRAPP 2022 ; Conference date: 06-02-2022 Through 08-02-2022",

url = "https://grapp.scitevents.org/",

}

TY - GEN

T1 - Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics

AU - Schinko, Christoph

AU - Shao, Lin

AU - Mueller-Roemer, J. S.

AU - Weber, Daniel

AU - Zhang, Xingzi

AU - Lee, Eugene

AU - Sander, Bastian

AU - Steinhardt, Alexander

AU - Settgast, Volker

AU - Chen, Kan

AU - Erdt, Marius

AU - Eggeling, Eva

N1 - Conference code: 17

PY - 2022/2/6

Y1 - 2022/2/6

N2 - 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.

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.

KW - Agent-based Modeling

KW - Clustering

KW - Computational Fluid Dynamics

KW - Airborne Disease Transmission Modeling

U2 - 10.5220/0000156800003124

DO - 10.5220/0000156800003124

M3 - Conference paper

VL - 1

SP - 278

EP - 285

BT - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1

PB - SciTePress

CY - Portugal

T2 - 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

Y2 - 6 February 2022 through 8 February 2022

ER -