Epidemic Spread and Control Strategies: A Spatial-individual Agent-based Modeling and Optimization Approach

Area of Science:

• Epidemiology
• Computational modeling
• Public health

Background:

• Traditional disease prevention models struggle with individual behaviors and complex environments.
• High-density, poorly ventilated spaces like schools and hospitals are vulnerable to pathogen spread.
• The COVID-19 pandemic underscored the need for precise public health interventions.

Purpose of the Study:

• To develop and apply a spatial-individual agent-based model for disease transmission.
• To evaluate the impact of different testing and isolation strategies in a university dormitory setting.
• To provide data-driven insights for optimizing campus public health policies.

Main Methods:

• Developed a spatial-individual agent-based model integrating spatiotemporal dynamics.
• Quantified transmission risk based on contact distance and duration.
• Simulated various testing frequencies, scopes, and isolation intensities on a dormitory floor.

Main Results:

• Dormitory-wide isolation was more effective than individual restrictions.
• Every-three-day class-based testing reduced infection risk compared to daily testing by minimizing high-density interactions.
• Stricter isolation reduced outbreak duration but increased transmission rates in shared spaces.

Conclusions:

• Balancing testing frequency and isolation stringency is crucial for epidemic control in high-density areas.
• Frequent testing is vital under strict isolation; moderate reduction in testing can be beneficial in less restrictive settings.
• Findings offer guidance for optimizing campus public health strategies based on spatial constraints.