Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an...

Area of Science:

• Epidemiology
• Computational Science
• Public Health

Background:

• The COVID-19 pandemic heightened awareness of infectious disease transmission in public spaces.
• International airports are critical locations for monitoring and mitigating disease spread.
• Computational modeling offers a powerful approach to understanding and predicting disease dynamics.

Purpose of the Study:

• To develop and apply a detailed computational model for simulating infectious disease spread in airport environments.
• To assess the impact of virus variant contagiousness on infection dynamics.
• To evaluate the utility of agent-based models combined with temporal components for risk assessment.

Main Methods:

• An agent-based social force model was developed to simulate virus spread via aerosol traces and airborne virus load.
• The model incorporates high geometric detail and accounts for temporal infection dynamics over several days.
• Simulations were conducted in a realistic airport setting, testing multiple virus variants with varying contagiousness.

Main Results:

• A virus variant requires at least 12 times greater contagiousness than the control to achieve an infection level exceeding 30%.
• The model successfully simulated disease spread scenarios in a complex airport environment.
• The findings underscore the significant impact of increased viral contagiousness on potential outbreaks.

Conclusions:

• Agent-based models integrated with temporal components are valuable for assessing infection risks associated with specific viruses and their variants.
• This modeling approach can inform public health strategies for disease control in high-traffic public spaces like airports.
• Understanding the interplay between viral properties and environmental factors is crucial for effective pandemic preparedness.