  Airborne pandemics have caused millions of deaths worldwide, large-scale
economic losses, and catastrophic sociological shifts in human history.
Researchers have developed multiple mathematical models and computational
frameworks to investigate and predict the pandemic spread on various levels and
scales such as countries, cities, large social events, and even buildings.
However, modeling attempts of airborne pandemic dynamics on the smallest scale,
a single room, have been mostly neglected. As time indoors increases due to
global urbanization processes, more infections occur in shared rooms. In this
study, a high-resolution spatio-temporal epidemiological model with airflow
dynamics to evaluate airborne pandemic spread is proposed. The model is
implemented using high-resolution 3D data obtained using a light detection and
ranging (LiDAR) device and computing the model based on the Computational Fluid
Dynamics (CFD) model for the airflow and the Susceptible-Exposed-Infected (SEI)
model for the epidemiological dynamics. The pandemic spread is evaluated in
four types of rooms, showing significant differences even for a short exposure
duration. We show that the room's topology and individual distribution in the
room define the ability of air ventilation to reduce pandemic spread throughout
breathing zone infection.
