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Authors: Christoph Schinko 1 ; 2 ; Lin Shao 1 ; 2 ; Johannes Mueller-Roemer 3 ; Daniel Weber 3 ; Xingzi Zhang 4 ; Eugene Lee 4 ; Bastian Sander 5 ; 6 ; Alexander Steinhardt 1 ; Volker Settgast 1 ; 2 ; Kan Chen 4 ; Marius Erdt 4 and Eva Eggeling 1 ; 2

Affiliations: 1 Fraunhofer Austria Center for Data Driven Design, Graz, Austria ; 2 Graz University of Technology, Graz, Austria ; 3 Fraunhofer Institute for Computer Graphics Research IGD & Technische Universität Darmstadt, Darmstadt, Germany ; 4 Fraunhofer Singapore & Nanyang Technological University, Fraunhofer IDM@NTU, Singapore ; 5 Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg, Germany ; 6 University of Vienna, Vienna, Austria

Keyword(s): Agent-based Modeling, Clustering, Computational Fluid Dynamics, Airborne Disease Transmission Modeling.

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.

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Paper citation in several formats:
Schinko, C.; Shao, L.; Mueller-Roemer, J.; Weber, D.; Zhang, X.; Lee, E.; Sander, B.; Steinhardt, A.; Settgast, V.; Chen, K.; Erdt, M. and 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 (VISIGRAPP 2022) - GRAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 278-285. DOI: 10.5220/0010904500003124

@conference{grapp22,
author={Christoph Schinko. and Lin Shao. and Johannes Mueller{-}Roemer. 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.},
title={Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - GRAPP},
year={2022},
pages={278-285},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010904500003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - GRAPP
TI - Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics
SN - 978-989-758-555-5
IS - 2184-4321
AU - Schinko, C.
AU - Shao, L.
AU - Mueller-Roemer, J.
AU - Weber, D.
AU - Zhang, X.
AU - Lee, E.
AU - Sander, B.
AU - Steinhardt, A.
AU - Settgast, V.
AU - Chen, K.
AU - Erdt, M.
AU - Eggeling, E.
PY - 2022
SP - 278
EP - 285
DO - 10.5220/0010904500003124
PB - SciTePress