## 4 Discussion  
We have explored the airborne pandemic spread for a small size population sharing a room. We used a combined CFD and _SEI_ model for airflow and epidemiological dynamics, respectively. We evaluated the pandemic spread in four types of rooms: classroom, conference room, movie theater, and restaurant, using high-resolution spatial data obtained by a LiDAR and by simulating the air movement dynamics with a relatively small step in time to accurately capture all temporal dynamics. We found that the spread dynamics between the four different room types are statistically different with a \(p\)-value of \((0.0014)\) using [PERSON]'s test [87]. Moreover, we computed a pair-wise [PERSON]'s \(t\)-test showing that each pair of room types has statistically different dynamics with a worse \(p\)-value of 0.046 [88]. Thus, the pandemic spread dynamics have a consistent relationship with the distribution of the population in the room and its geometrical configuration, as shown in Figure 4. In particular, the population distribution has more influence on the pandemic spread than the overall density of the population. This is limited to scenarios similar to the ones examined in this study. For instance, the  
Figure 6: A sensitivity analysis of the infection rate as a function of the mask’s efficiency and the number of pathogens needed to infect an individual. The results are shown as the average value of all the rooms in Table 2 with 10 realisations each. average restaurant's density is 0.054 [person/m\({}^{3}\)] with 0.033 standard deviation, while movie theaters' density is \(0.063\pm 0.046\) [person/m\({}^{3}\)], as shown in Table 2. Despite this, the pandemic spread in both cases is statistically significantly different with a \(p\)-value of 0.024 (obtained from a two-sided paired \(t\)-test). Hence, the well-mixed assumption does not hold for density-related approximations even for a short duration as had been believed before [89; 90]. Nevertheless, for the case of a relatively large population of a few hundred individuals found in the movie theater, our model predicts a similar pandemic spread rate as found by [48; 91]. This can be explained by the influence of breathing zone infection versus indirect infection [92]. One can see from Figure 3 that individuals in the conference room are facing each other and the intersection of the breathing zone is much larger compared to the movie theater where individuals are facing a screen, and there is enough distance between any two individuals. As a result, as presented in Figure 4, the portion of exposed individuals in the conference room is much higher on average compared to the movie theater for the same duration. That said, these results provide an upper boundary to the realistic pandemic spread in restaurants and movie theaters as these have ventilation systems that reduce the infection rate. When introducing mask-wearing or artificial air ventilation (AAV), we show that mask-wearing consistently outperforms AAV in reducing the pandemic spread over all four room types, as shown in Figure 5. Nonetheless, the mask-wearing PIP requires the participation of the population which is known to be harder to obtain over long periods of time [93; 94; 95]. On the other hand, even a low level of air ventilation, such as a replacement of 20% of the air in the room every 30 min, results in 7.25 percent improvement compared to a room without any air ventilation and can be controlled more easily than mask-wearing. These results agree with the outcomes proposed by [96]. A sensitivity analysis of the pathogen's infection rate, as indicated by the number of pathogens required to infect an individual, and the mask's efficiency as presented in Figure 6 and Equation (4) reveal a sub-linear connection between the pathogen's infection rate and the number of infected individuals on average. Similarly, there is mostly a linear correlation between mask efficiency and the number of infected individuals.