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

TL;DR

This paper introduces a high-resolution 3D spatio-temporal model combining airflow dynamics and epidemiological factors to predict airborne pandemic spread within individual rooms, highlighting the impact of room topology and ventilation.

Contribution

It presents a novel high-resolution CFD-based model for indoor airborne pandemic spread, integrating LiDAR data and SEI epidemiological modeling at the room scale.

Findings

Room topology significantly affects infection spread.

Ventilation can reduce infection risk in shared spaces.

Short exposure durations can still lead to notable infections.

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.