Parameter Space Exploration in Pedestrian Queue Design to Mitigate Infectious Disease Spread.

Area of Science:

• Epidemiology
• Computational Social Science
• Public Health

Background:

• Infectious disease transmission is amplified in crowded environments due to pedestrian interactions.
• Existing disease models often overlook pedestrian movement dynamics in high-density settings.
• Understanding disease spread in queues is crucial for public health interventions.

Purpose of the Study:

• To develop and evaluate a multiscale model integrating pedestrian dynamics with epidemiological principles.
• To assess the impact of pedestrian movement on infectious disease transmission in localized outbreaks.
• To enhance the efficiency of parameter space exploration in complex, stochastic models.

Main Methods:

• Utilized a social force-based pedestrian-dynamics approach to model pedestrian interactions.
• Integrated pedestrian movement data with a stochastic epidemiological model for disease spread estimation.
• Employed a novel high-efficiency parameter sweep algorithm, specifically low-discrepancy sequence (LDS), to explore model uncertainties.
• Applied the multiscale model to a simulated airport security queue scenario.

Main Results:

• The integrated model effectively estimates infectious disease spread considering pedestrian movement.
• Low-discrepancy sequence (LDS) parameter sweeps significantly reduce computational requirements for model analysis.
• An order-of-magnitude reduction in computational cost was achieved using LDS for parameter exploration.
• The study demonstrates the feasibility of applying multiscale models to real-world public health challenges.

Conclusions:

• Integrating pedestrian dynamics into epidemiological models provides a more realistic assessment of disease spread.
• Efficient parameter sweep algorithms like LDS are crucial for overcoming computational limitations in multiscale modeling.
• This approach offers a valuable tool for understanding and mitigating infectious disease outbreaks in crowded public spaces.