## 1 Background
In their study, the authors investigated the impact of different moving patterns of the population in different types of buildings (homes, schools, offices, and malls) on the spread of pandemics and on the optimal configuration of both spatial and temporal factors, such as mask-wearing and vaccination. Nevertheless, the authors assumed that the population is well-mixed in each room. As a result, their model produced noisy results for the case of home-type buildings, where the population density is low. On other hand, for the case of spatial models, it is assumed that the space is continuous and people move in the space over time, representing a more physically accurate representation of movement. For instance, [PERSON] and [PERSON] (2008) proposed an _SIR_-based model where susceptible individuals move away from the previous location of the infection, and all individuals move away from overcrowded regions [40]. [PERSON] and [PERSON] (2020) developed a stochastic _SIR_ model with global and local infective contacts [41]. [PERSON] and [PERSON] (2017) presented an _SIR_-based model where individuals are assumed to move stochastically within a small fixed radius rather than a random walk [42]. The authors proposed continuous and discrete _SIR_-based models that show spatial distributions. They show that the propagation speed and size of an epidemic depend on the population density and the infectious radius. In the context of airborne pandemics, one can focus on the infection that occurs in a room. To do so, one can track the airflow with the pathogen particles it carries between the individuals in the room [43]. [PERSON] and [PERSON] (2016) reviewed the release, transport, and exposure of expiratory droplets because of respiratory activities in the context of a pandemic indoors [44]. The authors concluded that droplets or droplet nuclei are transported by airflow, which is sometimes affected by the human body plume. They suggested that the usage of a face mask, as well as room air ventilation, can reduce the infection rate. Air flow (or air movement) dynamics are vastly explored in multiple contexts such as healthcare [45], mechanics [46], agriculture [47], and epidemiology [43]. In particular, [PERSON] et al. (2020) explored the pandemic spread of the airborne COVID-19 pathogen in indoor settings using a combination of the box and Wells-Riley models [48; 49]. The authors derived an expression for the number of secondary infections. However, their model does not include a detailed and accurate representation of the room's geometry. Moreover, the box model used by the authors does not correctly represent common cases such as rooms where clear directional flow or infection due to overlapping breathing zones. In this work, we aim to tackle these challenges using more detailed airflow dynamics. There are various modeling approaches to predict the airflow within buildings such as multi-zone models and zonal models. However, Computational Fluid Dynamics (CFD) models are considered to be the most accurate for a single room [50; 51]. The CFD models are numerical methods of solving fluid flow using the Navier-Stokes (NS) equations [52]. These models use numerical algorithms to integrate the NS equations over a given mesh by converting the integral equations to algebraic equations (e.g., discretization) and then solving them iteratively [53]. In the context of building airflow or even room-level airflow, the CFD modeling approach subdivides an individual room into many space segments, and each one is treated as an atomic segment in which the NS equations are computed [50]. There are three main types of CFD implementation for indoor airflow dynamics: Reynolds-Averaged Navier-Stokes (RANS), Large Eddy Simulation (LES), and Direct Numerical Simulation (DNS) [54]. The RANS equations take advantage of the Reynolds decomposition technique whereby an instantaneous quantity is decomposed into fluctuating quantities and the average value of some time duration.