## 3 Simulations and Results
### Baseline Model Dynamics  
In order to evaluate the airborne pandemic spread dynamics in the different types of rooms, we computed the pandemic spread for 90 min for each room, each time picking  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Notation** & **Value** & **Source** \\ \hline CFD’s mesh’s single volume element size in cubic centimeters [m\({}^{3}\)] & \(\delta\) & \(1\) & [74] \\ \hline Inhaling duration in seconds [\(t\)] & \(F_{in}^{x}\) & \(1.42\pm 0.25\) & [75;76] \\ \hline Exhaling duration in seconds [\(t\)] & \(F_{out}^{x}\) & \(2.28\pm 0.47\) & [76;77] \\ \hline No breathing duration in seconds [\(t\)] & - & \(0.39\pm 0.04\) & [76;78] \\ \hline Inhaling volume in cubic centimeter [m\({}^{3}\)] & \(|F_{in}|/F_{in}^{x}\) & \(304\pm 71\) & [76] \\ \hline Exhaling volume in cubic centimeter [m\({}^{3}\)] & \(|F_{out}|/F_{out}^{x}\) & \(198\pm 41\) & [76] \\ \hline Average distance of pathogen particle influenced by & & & \\ inhaling in meters [m] & \(\gamma\) & \(0.34\) & [79] \\ \hline Average decay rate of pathogen particles in host in & & & \\ minutes [\(\frac{1}{1}\)] & \(\phi_{r}\) & \(6.6\cdot 10^{-4}\) & [79] \\ \hline Average number of pathogens particles needed to infected a & & & \\ susceptible individual [1] & \(\phi_{\delta}\) & \(10^{8}\) & [80] \\ \hline Average number of pathogens particles generated by & & & \\ infected individual at each exhaling [1] & \(\phi_{\delta}\) & \(1.3\cdot 10^{7}\) & [80] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Population Size** & **Room Size [m]** & **Density [1/m\({}^{3}\)]** \\ \hline Classroom 1 & \(N=33\) & \(L\,=\,10.00,W\,=\,6.00,H\,=\,2.60\) & \(0.211\) \\ \hline Classroom 2 & \(N=28\) & \(L\,=\,8.00,W\,=\,5.63,H\,=\,2.88\) & \(0.215\) \\ \hline Classroom 3 & \(N=27\) & \(L\,=\,7.80,W\,=\,7.70,H\,=\,2.61\) & \(0.173\) \\ \hline Classroom 4 & \(N=31\) & \(L\,=\,9.00,W\,=\,7.00,H\,=\,3.04\) & \(0.164\) \\ \hline Classroom 5 & \(N=36\) & \(L\,=\,8.77,W\,=\,14.25,H\,=\,3.20\) & \(0.090\) \\ \hline Restaurant 1 & \(N=19\) & \(L\,=\,12.12,W\,=\,8.25,H\,=\,2.75\) & \(0.069\) \\ \hline Restaurant 2 & \(N=23\) & \(L\,=\,25.00,W\,=\,14.00,H\,=\,2.83\) & \(0.023\) \\ \hline Restaurant 3 & \(N=37\) & \(L\,=\,26.25,W\,=\,20.00,H\,=\,3.63\) & \(0.019\) \\ \hline Restaurant 4 & \(N=48\) & \(L\,=\,21.88,W\,=\,16.00,H\,=\,3.00\) & \(0.046\) \\ \hline Restaurant 5 & \(N=67\) & \(L\,=\,13.87,W\,=\,10.95,H\,=\,4.40\) & \(0.100\) \\ \hline Movie theater 1 & \(N=240\) & \(L\,=\,22.89,W\,=\,18.00,H\,=\,11.25\) & \(0.052\) \\ \hline Movie theater 2 & \(N=112\) & \(L\,=\,12.72,W\,=\,10.00,H\,=7.00\) & \(0.126\) \\ \hline Movie theater 3 & \(N=270\) & \(L\,=\,24.72,W\,=\,19.44,H\,=\,12.15\) & \(0.046\) \\ \hline Movie theater 4 & \(N=400\) & \(L\,=\,18.94,W\,=14.80,H\,=\,9.30\) & \(0.153\) \\ \hline Movie theater 5 & \(N=720\) & \(L\,=\,36.00,W\,=\,28.30,H\,=\,17.68\) & \(0.040\) \\ \hline Conference 1 & \(N=9\) & \(L\,=\,6.50,W\,=\,4.00,H\,=\,3.12\) & \(0.110\) \\ \hline Conference 2 & \(N=11\) & \(L\,=\,7.98,W\,=\,3.76,H\,=\,2.79\) & \(0.131\) \\ \hline Conference 3 & \(N=16\) & \(L\,=\,10.00,W\,=\,4.09,H\,=\,2.90\) & \(0.135\) \\ \hline Conference 4 & \(N=17\) & \(L\,=\,11.84,W\,=\,5.20,H\,=\,2.91\) & \(0.095\) \\ \hline Conference 5 & \(N=24\) & \(L\,=\,11.21,W\,=\,6.70,H\,=\,2.81\) & \(0.114\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The different room types used in the experiments with a qualitative description of the population size and the room’s dimensions in meters.  
a different infected individual in a uniform distribution. We repeated the simulation 100 times. The results of the simulation are shown in Figure 4, where the x-axis is the time passed in minutes from the beginning of the pandemic, and the y-axis indicates the portion of the population in each epidemiological state where \(S\), \(E\), \(I\), and \(R\) stand for susceptible, exposed, infected, and recovered, respectively.