High Resolution Spatio-Temporal Model for Room-Level Airborne Pandemic Spread

TL;DR

This work tackles the problem of modeling airborne pandemic spread at the smallest indoor scale—an individual room—by integrating high-resolution 3D geometry obtained via LiDAR with a CFD-based airflow model and a spatio-temporal SEI epidemiological framework. The proposed approach, implemented as an agent-based simulation, couples Reynolds-Averaged Navier–Stokes airflow with pathogen transport and decay to track breathing-zone exposure and infection risk for a fixed population, starting from a single infected individual. The study demonstrates that room topology and the spatial distribution of occupants drive transmission dynamics and that mask-wearing policies outperform artificial air ventilation in reducing infections across four room types; it also provides guidance for room-type-specific intervention strategies. While offering detailed insights, the work acknowledges substantial computational costs and the absence of direct empirical validation, suggesting future work to relax static-behavior assumptions and pursue validation via surrogate or machine-assisted experiments and scalable approximations for larger buildings.

Abstract

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.

Figures (5)

• Figure 1: A schematic view of the model's dynamics. $F_{out}$ and $F_{in}$ are the force of air getting out and in (with the pathogen's particles) during an individual's breathing process.
• Figure 2: A schematic view of the simulator's process, including the integration between the agent-based with breathing dynamic.
• Figure 3: Schematic 3D projection views of the four room types.
• Figure 4: The SEI over time for each type of room, shown as mean $\pm$ standard deviation. The results are computed as a result of 100 realisations for each room and five rooms for each room-type ($n = 500$).
• Figure 5: The influence of the masks-wearing and air-ventilation PIPs on the number of infected individuals over a period of 90 minutes (an hour and a half). The results are shown as the average value of all the rooms of the same type.