Modeling Social Distancing and Quantifying Epidemic Disease Exposure in a Built Environment.

Area of Science:

• Environmental Health
• Epidemiology
• Building Science

Background:

• Post-pandemic, estimating indoor airborne disease exposure risk is crucial.
• Existing methods lack comprehensive assessment of built environment factors.

Purpose of the Study:

• To develop a novel metric for quantifying social distancing and airborne disease exposure risk in indoor settings.
• To create a flexible modeling framework applicable to various airborne diseases, including COVID-19.

Main Methods:

• Developed a metric integrating distance, occupancy, particle dynamics, and individual risk factors (immunity, age, comorbidities, masks).
• Incorporated environmental parameters like virus half-life and ventilation rates.
• Validated the metric using simulations of physical environments, including movement and air-conditioning models.

Main Results:

• The metric effectively estimates exposure risk, scaling with distance and occupancy.
• A visualization tool was developed to identify high-risk areas within built environments.
• Simulations demonstrated the framework's ability to model complex indoor scenarios.

Conclusions:

• The developed metric and software framework offer a robust tool for managing built environments.
• Applications include optimizing scheduling, occupancy limits, ventilation, and mask policies for airborne disease prevention.
• The open-source framework supports real-time risk assessment and intervention strategies.