Epidemic spreading on mixing group with face-to-face interaction

Area of Science:

• Epidemiology
• Network Science
• Computational Biology

Background:

• Mixing groups in enclosed spaces create complex contact networks impacting social dynamics.
• Epidemic spreading within these groups is intricate due to multiple interactions and transmissions.
• Limited research exists on face-to-face interactions in mixed groups and their effect on disease spread.

Purpose of the Study:

• To introduce a novel agent-based model for analyzing face-to-face interactions in mixing groups.
• To investigate the dynamics of disease transmission within mixed groups using a susceptible-infected-susceptible (SIS) model.
• To understand how group size and mixing patterns influence epidemic trajectories.

Main Methods:

• Utilized an agent-based modeling approach.
• Integrated a susceptible-infected-susceptible (SIS) process.
• Incorporated a temporal network within a defined time window to capture movement and spread dynamics.

Main Results:

• Relative group size and mixing patterns significantly impact disease spread.
• High inter-group contact preference limits spread when group sizes are disparate.
• Asymmetrical changes in intra-group preferences, especially by a minority group, can increase disease spread.

Conclusions:

• Group size and mixing behaviors are critical factors in controlling epidemic spread in mixed populations.
• Targeted interventions focusing on contact preferences within and between groups can modulate disease transmission.
• The model provides insights into the complex interplay between social structure and infectious disease dynamics.