Accelerated Airborne Virus Spread Simulation: Coupling Agent-based
Modeling with GPU-accelerated Computational Fluid Dynamics
Christoph Schinko
1,5 a
, Lin Shao
1,5 b
, Johannes Mueller-Roemer
2 c
, Daniel Weber
2 d
Xingzi Zhang
3 e
, Eugene Lee
, Bastian Sander
4,6 f
, Alexander Steinhardt
, Volker Settgast
1,5 g
Kan Chen
3 h
, Marius Erdt
and Eva Eggeling
1,5 i
Fraunhofer Austria Center for Data Driven Design, Graz, Austria
Fraunhofer Institute for Computer Graphics Research IGD & Technische Universit
at Darmstadt, Darmstadt, Germany
Fraunhofer Singapore & Nanyang Technological University, Fraunhofer IDM@NTU, Singapore
Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg, Germany
Graz University of Technology, Graz, Austria
University of Vienna, Vienna, Austria
Agent-based Modeling, Clustering, Computational Fluid Dynamics, Airborne Disease Transmission
The Coronavirus Disease 2019 (COVID-19) has shown us the necessity to understand its transmission mecha-
nisms 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 intro-
duced 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 produc-
ing deeper insights. The proposed system is still work in progress; thus we focus on the system architecture
and show preliminary results.
The Coronavirus Disease 2019 (COVID-19) has
shown us, that a detailed, qualitative exploration of
epidemiological cases is necessary (Shao et al., 2021).
This means we have to understand its transmission
mechanisms in detail, especially to establish practice
in controlling such infectious diseases. COVID-19’s
Severe Acute Respiratory Syndrome Coronavirus 2
(SARS-CoV-2) is primarily spread through airborne
droplets and aerosols (Lu et al., 2020; Shen et al.,
2020). This transmission mechanism is more likely to
occur in indoor than outdoor settings, when individ-
uals are breathing, speaking, coughing, or perform-
ing any other form of exhalation (Qian et al., 2021;
Leclerc et al., 2020; Stadnytskyi et al., 2020). When
these particles are inhaled by other individuals (or
come into contact with mucosae), pathogen transmis-
sion can occur. To lower the infection risk for individ-
uals, recommendations for interventions have a focus
on reducing indoor transmission by use of face masks,
social distancing, and other measures. These general
intervention recommendations have been supported
by a number of mathematical models for indoor trans-
mission analysis. The role of small, aerosolized
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.
Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics.
DOI: 10.5220/0010904500003124
In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 1: GRAPP, pages
ISBN: 978-989-758-555-5; ISSN: 2184-4321
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
droplets in indoor transmission for different settings
has been determined by a number of models (Bha-
gat et al., 2020; Chen et al., 2020; Lelieveld et al.,
2020; Sun and Zhai, 2020). However, these models
do not account for the spatiotemporal heterogeneity
introduced by the movement and interaction of indi-
viduals with their surroundings.
On the other hand, related work in the fields of
computational fluid dynamics (CFD) and agent-based
modelling is either infeasible due to a huge amount
of computation needed for larger scale scenarios, or
is missing means to simulate virus transmission on a
micro- and mesostructure level. For a more detailed
investigation of transmission effects, a combination of
CFD simulations and the dynamics given by trajecto-
ries from an ABM seems to be a promising way.
Thus, we describe a novel system that is a com-
bination of an agent-based model (ABM) with CFD
simulations. For a given scenario, trajectories and
aerosol-generating actions (e.g., coughing) are pro-
duced by an ABM. However, the sheer number of
agents and the length of the produced trajectories
would lead to huge computational effort on the CFD
side, when the simulation is carried out for the en-
tire scenario. This problem is tackled by intelligent
clustering and selection of relevant situations, greatly
reducing the input complexity for simulation.
The system lies the foundations for assessing indi-
vidual infection risks of the simulated scenario. This
can be done by applying risk models to the simulation
results. The resulting infection risks can be applied
back to their respective clusters enabling risk assess-
ment for different infection scenarios and subsequent
derivation of recommendations for safety precautions.
A first contribution of this paper is the combina-
tion of an ABM with CFD simulation based on in-
telligent selection and clustering of situations. With
this approach, the complexity of a CFD simulation of
the entire scenario including movement of all agents
is reduced to small, but independent situations of rela-
tively short duration. An additional benefit is the pos-
sibility to apply simulation results to similar situations
of other scenarios. A second contribution is the de-
velopment of a GPU-optimized, higher-order lattice
Boltzmann fluid simulation for fast evaluation of the
different situations. Since the development of the pro-
posed system is still work in progress, we focus on the
system architecture and show preliminary results.
This section presents important work that relates to
our system architecture.
2.1 Agent-based Modeling
Generally, given two points on a model, we can find
the optimal path between them in three steps. The
first step is to construct a navigation mesh and turn
the model into a graph. In the graph, each polygon is
represented by a node and adjacent polygons are con-
nected by an edge. Then, graph search algorithms,
such as A* (Stout, 1996) and HPA* (Botea et al.,
2004), are applied to find a series of adjacent nodes
(polygons) on the graph that the optimal path will go
through with the first node and last node. After that,
path finding algorithms are used to find the optimal
path, which is normally the shortest path, on the de-
rived polygons. One of the commonly-used methods
in path finding is the funnel-based method, e.g., Mod-
ified Funnel Algorithm (Demyen and Buro, 2006).
The path finding algorithms can fulfill the first re-
quirement, leading the agents to their goals without
colliding with any obstacles. To avoid collision with
other agents, collision avoidance algorithms are re-
quired to adjust the agents’ velocity when there is
danger of collision. Much research has been done in
this area, e.g., Optimal Reciprocal Collision Avoid-
ance (ORCA) (Van den Berg et al., 2008) and Pedes-
trian Velocity Obstacles (PedVO) (Curtis, 2013), and
open-source libraries such as RVO2 are available.
To achieve collision-free agent paths, we used the
Urho3D game engine
and incorporate widely-used
crowd simulation algorithms, e.g., Recast & Detour
for navigation mesh generation and path planning,
and modified Reciprocal Velocity Obstacle (RVO)
with adaptive sampling for collision avoidance.
2.2 Trajectory Similarity Measures and
To compute the similarity between trajectories, usu-
ally multidimensional and spatiotemporal features,
e.g., position, speed, direction, and activity, have to
be considered. In (Magdy et al., 2015), similarity
measures for trajectories are roughly classified into
two classes: (1) spatial similarity measurements
based on similar geometric shapes; and (2) spatio-
temporal measurements that spatial and tempo-
ral dimensions into account. Well-known techniques
that consider spatial similarity are, for example,
the Euclidean Distance, Hausdorff Distance (Chen
Urho3D: (Accessed: 2021-10-28)
recastnavigation (Accessed: 2021-10-28)
Digesting Duck:
2010/10/rvo-sample-pattern.html (Accessed: 2021-10-28)
Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics
et al., 2011), and Fr
echet Distance (Eiter and Man-
nila, 1994). Examples for spatio-temporal measure-
ments include the Longest Common Sub-Sequence
(LCSS) (Vlachos et al., 2004) and Dynamic Time
Warping (DTW) (Chen et al., 2005).
For trajectory clustering efficient algorithms are
needed, which are scalable and flexible. Therefore,
well-known clustering algorithms are often extended
for trajectory data. Especially for clustering mov-
ing object data, further cluster properties such as
spatial information (Palma et al., 2008), time de-
pendency (Nanni and Pedreschi, 2006) or local seg-
ments (Lee et al., 2007), may be considered. Due to
the large volume of trajectory data, we have decided
to use a very fast partition-based algorithm that uses
a spatial similarity measure, called minimum average
direct-flip (MDF) distance (Garyfallidis et al., 2012).
2.3 Computational Fluid Dynamics
CFD simulations have proven to be an important tool
in the modeling of the spread of airborne pathogens
such as the novel SARS-CoV-2 virus (Vuorinen et al.,
2020). However, full-fledged CFD simulations are
computationally expensive. To simulate a large num-
ber of situations within an acceptable time frame, we
examined massively parallel and approximate CFD
simulation approaches. While the popular finite vol-
ume approach allows for unconditionally stable sim-
ulation (Stam, 1999), it requires the compute- and
memory-intensive solution of a large linear system,
limiting throughput and/or resolution even when ad-
vanced solvers are used (Weber et al., 2015). Further-
more, this approach suffers from significant numeri-
cal diffusion, limiting simulations to small Reynolds
numbers, while coughing and movement in a cluttered
environment result in highly turbulent flow (i.e., very
high Reynolds numbers).
Therefore, we focus on the lattice Boltzmann
method (LBM) using a D3Q27 discretization. Recent
improvements to the LBM allow for highly turbulent
flow with adaptive time steps for fast and stable simu-
lation, while decoupling obstacle geometry and fluid
grid resolution (Li et al., 2020). Due to its locality,
the LBM is ideally suited to massively parallel simu-
lation on GPUs. Especially when coupled with semi-
analytic inversion of the collision operator, very low
compute costs are achievable (Chen et al., 2021).
We combine the state-of-the-art LBM simulation,
with a particle-based aerosol transport model (Guha,
2008). The particles are modeled with one-way
coupling with the background fluid and no particle-
particle interaction, allowing for full use of the GPU’s
parallel computing power.
While the proposed system is not tailored towards
a specific scenario, we are currently implementing
its components with a typical supermarket scenario
as a first use case. It resembles a typical European
supermarket with one large customer area (approx.
19 × 52 m) with multiple shelves and counters. The
combined entrance and exit area is accessible through
a single door as it is common in many European su-
permarkets. Hence, customers entering the building
are near to others exiting. There are three counters
where customers need to check out before leaving the
supermarket. We use a simplified geometry for the
shelves and counters (see Figure 1).
Figure 1: A typical supermarket serves as an example. The
simplified geometry contains shelves (green) and check-out
counters (blue). Entrance and exit are located at the top left.
For the ABM, information about the accessible ar-
eas, the position of the entrance and exit as well as
target spots at the shelves and the check-out counters
is created manually. To generate a sufficient amount
of data, 88 agents are generated in front of the build-
ing with a random number of goals (items to shop).
To simulate the shopping process, agents move at a
typical pedestrian speed through the market, stopping
at their goals (locations at shelves) from time to time.
After all goals have been reached, each agent will go
to the check-out counter and then leave the market.
Also, there is a stationary agent at each of the three
check-out counters to represent a person working at
the supermarket.
The proposed system consists of three interacting
components: (1) The scenario is populated with
agents doing specific tasks while acting as similar as
possible (feasible) to real-world persons. The agent
simulation generates trajectories and aerosol events.
(2) Situations with agents are identified and clusters
are generated out of trajectory and aerosol event data.
Cluster representatives are identified. (3) The CFD
simulation is performed on selected representatives.
Particle trajectories are computed (spherical volume
GRAPP 2022 - 17th International Conference on Computer Graphics Theory and Applications
around mouth and nose of an agent).
A set of interfaces ensures the interoperability of
the system components. We differentiate between
generic (mainly scenario-specific) input as well as in-
put and output of specific components. The geometry
of the simulated scenario and the reference coordinate
system are input to all components.
ABM-specific input parameters are the number of
agents to simulate, the starting positions of agents
and parameters defining the agent’s goals: number
and positions of goal points, goal properties such as
max duration to stop for at each goal and a sequence,
which indicates the order of goals.
Next to the output produced by the ABM, clus-
tering and selection of situations is provided with a
target budget of situations to produce for the CFD
simulation. The generated output consists of identi-
fied situations including participating agents as well
as their respective trajectories and aerosol events. It is
defined by a bounding box and a time frame.
In addition to the underlying scene geometry and
the agent geometry, the mouth location/area must be
specified. Other parameters such as air density and
viscosity, aerosol particle drag factors, cough air ve-
locity profiles, and particle counts are chosen based
on the literature. Finally, the radius of the sphere
in which particles inhaled by “receiving” agents are
collected is required. Initially, we use a constant ra-
dius, but this could be extended to be based on the
inflow/breathing rate.
4.1 Agent-based Modeling
Normally in ABM, agent movement is steered by
goals. Given a supermarket scenario context, we de-
sign a goal sequence for the agents based on the com-
mon behaviors of in-store customers, which includes
points on the supermarket model are defined that go-
ing to (or leaving) these goal points means starting
(or finishing) this goal. To meet the requirements of
both randomness and universality in customer behav-
ior, the target points for the goal SHOP are randomly
chosen and iterated multiple times, since customers
are likely to buy more than one item in the supermar-
ket. All scenario-specific information is predefined
and provided as input for our modeling.
After generating the goal sequence, a series of
target points can be automatically selected for each
agent at run time. Then, a navigation mesh based on
the supermarket model can be build and the path find-
ing method from the Detour library can be used to find
the shortest path. However, scenarios which contain
narrow paths, e.g., a supermarket with narrow aisles,
often cause unrealistic behavior of agents such as get-
ting stuck, as shown in Figure 2. The reason for this is
that agents may have identical targets and go through
a narrow aisle from both directions at the same time.
Figure 2: Agents tend to get stuck in narrow aisles if the
effect of agent density is not considered in path finding.
To avoid such situations, we incorporate the
Density-Cost algorithm in (Van Toll et al., 2012) into
A* search (Stout, 1996) to find the optimal path be-
tween goal points. A density value is computed and
maintained for each polygon in the navigation mesh
that is proportional to the number of agents and in-
versely proportional to the area of a polygon.
Since agents move around, the density values may
vary constantly and cause re-planning agent paths on
the fly. Instead of considering all the density val-
ues along the way, incremental path finding is im-
plemented by considering nearby density informa-
tion and finding intermediate points for navigation
to avoid unrealistic look-ahead in crowd situations.
To prevent indecisive behaviors caused by the rapid
change in density, when an agent reaches/leaves a
polygon, we delay adding/removing the agent’s den-
sity contribution to this polygon. Furthermore, a
threshold for the maximum number of agents per goal
is used to control the density of agents in each poly-
gon and help agents avoid congested routes.
Besides movement simulation, we generate the
aerosol-emitting events breathing (default), coughing,
and talking for each agent. Here, coughing and talk-
ing events are randomly triggered for each agent. Em-
pirically, once generated, coughing and talking events
last for 5 to 10 seconds.
4.2 Clustering and Selection of
The purpose of this component is to perform a cluster-
ing of the multidimensional data received from agent-
based modeling and a selection of cluster representa-
tives to be handed over to the simulation of airflow
and aerosol distribution.
The clustering of the agent-based simulation data
is performed in multiple stages.
Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics
Situation Extraction: In a first step, all situations are
extracted from input trajectory data. In the context of
this work, a situation is defined as the continuous pe-
riod, where the distance between any two agents is
less than 2 meters. The resulting situations are as-
signed to 5 classes: very short, short, medium, long
and very long. Parameters for these classes are cho-
sen to ideally obtain an even distribution of situations.
A detailed overview of the situation classes for the su-
permarket scenario including number of trajectories
and duration is shown in Table 1.
Spatial Clustering: In a second step, the trajecto-
ries of each situation class are further clustered based
on the position and orientation to incorporate spatial
mappings of the supermarket. To consider subsequent
as well as oncoming trajectories, the QuickBundles
clustering algorithm (Garyfallidis et al., 2012) along
with the minimum average direct-flip (MDF) distance
is used. QuickBundles is a clustering algorithm for
sequences of points in 3D space that considers both
directions, original and flipped (swapped start and
end) versions of a path. For each situation, which
consists of two agents, the orientation of one agent
is rotated by 180 degrees to increase the weighting of
situations where agents meeting one another halfway.
Representative Selection: To choose a set of vary-
ing situations for the simulation of airflow and aerosol
distribution (cf. Section 4.3), different features like
aerosol events, spatial positions, and situation classes
need to be taken into consideration. Therefore, multi-
ple situations in each cluster and for each situation
class are computed as representatives. As criteria
for choosing the representatives, trajectories includ-
ing minimum, maximum, and the average number of
occurrences of the aerosol event talking and cough-
ing are considered. In the end, the set of represen-
tatives consists of situations with different trajectory
lengths (situation classes), spatial positions (clusters),
and aerosol events (criteria).
4.3 Simulation of Airflow and Aerosol
In order to assess the infection probabilities, a sim-
ulation of the airflow and the aerosol distribution is
conducted using a GPU-accelerated LBM.
Integration: A part of the scenario is selected by
specifying its bounding box and the relevant geome-
try is extracted within the solver and used as boundary
condition as shown in Fig. 3. In addition, the agent
geometry and movement data are imported to be able
to reproduce agent interactions. A simulation run then
computes the creation and the movement of aerosol
Figure 3: Specifying a bounding box, the corresponding ge-
ometry can be extracted and used as boundary conditions.
particles according to the input data (see Section be-
low). In order to assess infection probabilities, the
number and sizes of aerosol particles from the source
agent are counted (and deactivated) when they are in
proximity to another agent’s nose and mouth. The ac-
cumulated numbers can be used to approximate the
amount of inhaled aerosol and to eventually judge the
probability of infection. This mechanism allows to
select the infectious agents after the CFD simulation
to assess diverse infection scenarios.
CFD Simulation: We implemented a GPU-
optimized, higher-order LBM fluid simulation code
based on the works of (Li et al., 2020; Chen
et al., 2021). The LBM discretizes the microscopic
(particle-level) Boltzmann equation using a particle
distribution f (x, c,t) with discretized locations x
lattice nodes) and a discrete set of particle veloci-
ties c
. The developed code uses a D3Q27 discretiza-
tion, i.e., a three-dimensional lattice with 27 discrete
fluid velocities c
= (0, 0,0)
, c
= (±1, 0, 0)
, . . . ,
= (±1, ±1, 0)
, . . . , c
= (±1, ±1, ±1)
x = t = 1. These are advanced by alternating be-
tween a streaming (transport) kernel and a collision
(relaxation) kernel, which both operate locally, allow-
ing for full use of the GPUs massively parallel com-
pute power. Macroscopic Navier-Stokes variables can
be derived from the moments of the distribution.
The exterior boundary is modeled using extrap-
olating boundary conditions (Geier et al., 2015) for
the open boundary and slip boundary conditions for
the floor, implemented using the specular reflection
method (Li, 2011). Obstacles within the domain, in-
cluding the agents themselves, are modeled using the
immersed boundary method (Li et al., 2020). This
requires resampling geometry as a point cloud that
matches or exceeds the resolution of the grid using
Poisson disk sampling (Yuksel, 2015), as can be seen
in Fig. 3 on the left.
We combine the turbulent LBM fluid simulation
with aerosol particle simulation using Lagrangian par-
ticles following the equations of motion described
in (Guha, 2008). Coupling to the fluid is unidirec-
tional, and aerosol particles do not interact. These ap-
proximations are used to reduce computational load.
We model cough velocity profiles based on measure-
GRAPP 2022 - 17th International Conference on Computer Graphics Theory and Applications
ments from (Gupta et al., 2009), while particle size
distributions follow (Wang et al., 2020).
Since the computation of the drag force in Guha’s
transport model includes division by a factor τ that
can become arbitrarily small, the model can cause
aerosol transport to become unstable when τ < 1 in
lattice units, since the velocity difference u
is over-corrected. However, this only applies to
particles with a very small diameter, which we then
model as massless particles, i.e., u
. Al-
ternatively, time step subdivision could be used for
these particles. However, others have shown that the
behavior is sufficiently similar when using the mass-
less approximation (Vuorinen et al., 2020).
This section presents the results of preliminary exper-
iments with the proposed system.
For the supermarket scenario, the ABM compo-
nent described in Section 4.1 is used to simulate the
spawned agents. The agents spawn random in posi-
tion and interval in front of the entrance of the su-
permarket. Trajectories and aerosol-generating events
are recorded between entering and exiting the super-
market at an interval of 0.02 seconds. We define
the agent speed following a normal distribution with
mean 1.34 and standard deviation 0.16 to cover a typi-
cal range of walking speed of different ages. The cus-
tomer agents follow the same goal sequence, which is
ENTER, SHOP, QUEUE (optional), PAY and EXIT,
and the duration of each agent varies.
The situation extraction step, described in Sec-
tion 4.2, returns a total of 4494 situations, where two
agents are approaching each other with a distance of
less than 2 meters. All extracted situations are then
classified based on the trajectory lengths, see table 1.
The class very short contains situations of short en-
counters of agents within the range of 0.02–1.30 sec-
onds, whereas very long situations imply all encoun-
ters longer than 5.08 seconds (c.f. Table 1).
Figure 4: QuickBundles clustering results on trajectories of
the situation class very long.
The QB clustering is a partitioning-based algo-
rithm that relies on a distance threshold ζ, which
controls the heterogeneity of clusters inverse propor-
tional. In order to achieve a good spatial separation
of situations in the supermarket, a threshold ζ = 9m
is used, which subdivides the area of the supermarket
into 8 to 11 sub-areas (clusters) for the five different
situation classes. Figure 4 shows the spatial clustering
of situations with very long trajectories and Figure 5
the associated incidence of aerosol event 1 (talking)
and aerosol event 2 (coughing). One can see that the
overall area of the supermarket including meaning-
ful areas like entrance, checkout area and individual
supermarket sections, can be well covered by the 10
defined clusters and that aerosol events are normally
distributed in the supermarket.
Figure 5: Incidence plots of the aerosol event 1 talking (top)
and 2 coughing (bottom).
Table 1: Details of situation extraction and spatial cluster-
ing. This table shows for each situation class the number of
trajectories, the duration of situations in seconds, the num-
ber of computed clusters and the computation time.
Situation Traj. Duration (s) Clusters Time (s)
very short 1802 0.02–1.30 9 11.66
short 1804 1.31–2.10 8 13.59
medium 1786 2.11–3.03 9 13.66
long 1798 3.04–5.07 11 15.29
very long 1798 > 5.08 10 19.57
Finally, the situations identified by the QB cluster-
ing are imported into the airflow and aerosol simula-
tion described in Section 4.3, by extracting the bound-
ing box and trajectory of the encounter. Figure 6
shows excerpts from a simulation of a cough event
near the cash registers. The simulation was computed
with ParaView-compatible file output for the figures
only, as file writing takes up significantly more time
than the simulation itself (214s vs. 629s on a lap-
top equipped with an NVIDIA GeForce RTX 3070M
GPU). For the final simulations, no particle and veloc-
ity output is written to disk, only particle histograms
over time near the at-risk agent’s mouth. The simu-
lation shown in Fig. 6 was computed at a resolution
of 133×49×99, with 207736 surface samples for the
immersed environment geometry, and 7213 surface
samples on the agent geometry. The figure shows the
Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics
Figure 6: Aerosol transport simulation results for an agent standing at the cash registers. The left image visualizes the
velocity and the particle distribution 2.5s after a cough event. The right image shows a different viewpoint after 5s with
particles colored by diameter, showing that large particles drop down fairly close to the agent.
original geometry clipped to the situation bounding
box, not the surface samples (compare with Fig. 3).
Previous work on SARS-CoV-2 transmission focused
either on CFD, or on ABM alone (Shao et al., 2021;
Farthing and Lanzas, 2021). Thus, we presented a
novel system that combines ABM with CFD simula-
tions by intelligent clustering and selection of relevant
situations for a given scenario. A typical supermar-
ket scenario is used as a first test case. The combi-
nation of the sheer number of agents, the length of
the produced trajectories and the size of the scenario
would lead to huge computational effort on the CFD
side, if simulated as a whole. By selecting relevant
situations, the computational complexity for the CFD
simulation can be greatly reduced, but at the cost of
limited accuracy. Situations are simulated indepen-
dently from each other and only for selected cluster
representatives. A thorough investigation of the im-
pact on accuracy of our approach compared to a CFD
simulation of the entire scenario is planned for future
work (with ventilation added). Furthermore, we plan
to investigate situations with more than two partic-
ipating agents, which seems to be a non-trivial ex-
tension. Also we plan to add more variety into the
agent’s movement patterns (e.g., social forces), more
realistic behavior (such as sneezing or adding groups
of agents), and safety equipment (e.g., different face
While we showed preliminary results for the su-
permarket scenario, we are still in the process of sta-
bilizing our CFD code to produce airflow simula-
tion results. Also, this paper has presented neces-
sary groundwork to be applied to real scenarios, but
risk assessment has yet to be carried out. Both the
physics of relevant processes (e.g., advection, evapo-
ration, sedimentation) and the workflow are appropri-
ate to substantially assess infection risks. This can be
done with realistic spatial distributions of infectious
particles and inhalation rates (by changing respective
physical activity states).
In turn, spatially refined and agent-dependent in-
fection risks are key to infer individual safety precau-
tions. Within a simulation scenario the relative effect
of a single or combined safety precautions are to be
evaluated. The most effective and least invasive com-
bination of precautions, which conforms with pro-
cesses specific to the scenario, must be favored. The
precautions can be applied locally, i.e., within spatial
sub-domains with high infection risk, or globally for
the entire scenario.
This research has received funding from the Fraun-
hofer Anti-Corona Project AVATOR Anti-Virus-
Aerosol: Testing, Operation, Reduction”. We would
like to thank Stefanie Samtleben for valuable feed-
back and discussions.
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