Modeling latent infection transmissions through biosocial stochastic dynamics

Area of Science:

• Epidemiology
• Computational Biology
• Sociology

Background:

• The SARS-CoV-2 epidemic underscored the role of social factors and asymptomatic transmission.
• Passive infection routes via surfaces and delayed host contraction are critical but underexplored.
• Traditional models often overlook individual behaviors and virus characteristics.

Purpose of the Study:

• To develop an agent-based model simulating SARS-CoV-2 transmission dynamics.
• To incorporate individual characteristics, virus survival, and mutations into epidemic modeling.
• To analyze the impact of social activity and control measures on infection spread.

Main Methods:

• An agent-based model simulating infection transmission in an open system.
• A growing bipartite graph representing hosts and viral nodes.
• Analysis of temporal fluctuations in exposed, infected, and active virus counts at hourly resolution.

Main Results:

• Simulations highlight latent transmission and significant spread over extended periods.
• Social dynamics and lockdowns demonstrably influence infection curves.
• Reduced social activity with prolonged exposure can be detrimental; mutations can slow but not halt spread without social strategies.

Conclusions:

• Agent-based modeling provides a robust framework for understanding complex epidemic dynamics.
• Sociobiological factors and virus evolution are crucial for effective epidemic control.
• The model offers high temporal resolution and virus traceability for evaluating control strategies.