Contagion dynamics in self-organized systems of self-propelled agents

Area of Science:

• Complex systems
• Epidemiology
• Statistical physics

Background:

• Understanding contagion dynamics is crucial for predicting disease spread.
• Self-propelled particle systems exhibit emergent spatial structures.
• Polar alignment influences collective behavior and spatial organization.

Purpose of the Study:

• To investigate how spatial organization affects contagion dynamics in self-propelled particles.
• To analyze the interplay between spatial and epidemic parameters on outbreak spread.
• To identify conditions favoring or inhibiting disease transmission in these systems.

Main Methods:

• Agent-based simulations were employed to model susceptible-infectious-recovered (SIR) dynamics.
• Systematic variation of spatial parameters (alignment strength, Peclet number) and epidemic parameters (transmissibility, infectious period duration).
• Analysis of outbreak size and spread patterns under different parameter combinations.

Main Results:

• Ordered homogeneous states showed limited spreading, requiring long infectious periods for large outbreaks.
• Disordered homogeneous states exhibited low contagion, needing high epidemic parameter values for significant spread.
• Inhomogeneous ordered states, characterized by bands and clusters, facilitated high outbreak levels across a wide parameter range.
• Emergent spatial features, such as bands and clusters, were found to be critical drivers of contagion.

Conclusions:

• Self-organized spatiotemporal features play a vital role in contagion processes.
• Understanding these emergent properties is key for predicting and controlling spread in biological systems.
• The findings offer insights into epidemic control strategies in collective biological systems.