## 3 Simulations and Results  
We evaluated the model on four types of rooms: classrooms, conference rooms, movie theaters, and restaurants. We picked these types of rooms for three reasons: First, most of the individuals in the room are not moving most of the time. We tried to avoid movement as it influences the pandemic spread significantly [31; 44; 67] and not in the scope of the proposed model. Second, the room types should have a unique and distinguished topology. Third, the rooms should involve at least several individuals in order to allow a pandemic spread signal to be noticeable. Since classrooms, conference rooms, movie theaters, and restaurants fulfill these conditions [68; 69], we used them. Furthermore, each one of the room types has a unique topology and distribution of the population inside the room. These differences are shown in a schematic 2D projection of each room type into a top, side, and front view of the 3D structure of the room in Figure 3. Other researchers also investigated pandemic spread inside buses [70], workshop rooms [70], homes [31], and gyms [71]. However, these do not satisfy at least one of our requirements as individuals are moving quite frequently in buses, workshop rooms, and gyms. The population size in homes is usually too small to efficiently evaluate pandemic spread. The rooms' topologies were obtained using a 3D LiDAR-based scanning (using the technology available in the iPhone 12) device with the _RoomScan LiDAR_ application. of the room and mapped it into its 3D geometry [72]. In particular, the LiDAR program captures a 3D cloud point which is later processed to a mesh [73]. The obtained mesh's resolution is one cubic centimeter. This mesh is used as the boundary condition of the geometry. For each room, we obtained the places where individuals are located (sit) based on the chairs' locations and added 0.8 meters on the z-axis for their faces' location. Each individual occupies a box in space with 170 cm height, 55 cm width, and 40 cm length. In  
Figure 2: A schematic view of the simulator’s process, including the integration of the agent-based with the breathing dynamic. addition, each individual starts the breathing cycle in a random time between \(t=0\) and \(t=F_{in}^{T}+F_{out}^{T}\). Moreover, for the epidemiological properties, we simulated the COVID-19 pandemic with data from 1 March 2020 to 1 September 2020 in Israel, obtained from the WHO. This date range was chosen since, except for a single lockdown, no significant PIPs were used, no vaccination was issued, and only one strain dominated the population. Thus, this period in Israel best suits the _SEI_ dynamics and, as such, yields as realistic an epidemiological value (e.g., exposure to infection duration) as available. We assume only one individual (e.g., the index individual) is infected at the beginning of each simulation. As such, the other individuals are susceptible. Formally, the initial condition of the simulator is assumed to be: \(S(0)=N-1,\ E(0)=0,\ I(0)=1\). Namely, our initial conditions consider only a single infected individual which is taken randomly from one realisation to another. A summary of the used model parameters' values is provided in Table 1. In addition, a summary of the room types with a qualitative description of the population and the size of the room is provided in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Notation** & **Value** & **Source** \\ \hline Population size [1] & \(N\) & DPS & - \\ \hline Exposed to infected transformation rate in minutes \([\frac{1}{T}]\) & \(\xi\) & \(1.01\cdot 10^{-4}\) & [31] \\ \hline Simulation’s step in time (in seconds) \([t]\) & \(\Delta t\) & 0.02 & [50] \\ \hline Number of simulation steps [1] & T & 270,000 & - \\ \hline Average decay rate of the pathogen particles in air in minutes \([\frac{1}{T}]\) & \(D\) & \(5.5\cdot 10^{-3}\) & [62] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The model’s parameters with their default values.