Phase transitions in contagion processes mediated by recurrent mobility patterns.

Area of Science:

• Complex systems
• Epidemiology
• Network science

Background:

• Human mobility and activity patterns are key drivers of contagion, affecting disease spread, rumor diffusion, and consensus formation.
• Traditional models often use random diffusive dynamics, which inadequately capture the recurrent flows and location-specific nature of human movement.

Purpose of the Study:

• To develop a theoretical framework for analyzing contagion within a network of locations considering individuals' geographic origins.
• To identify and characterize a phase transition in contagion dynamics based on mobility patterns.

Main Methods:

• Development of a theoretical framework to model contagion in a network of locations with origin recall.
• Analysis of stochastic contagion processes and their phase transition behavior.
• Validation using diffusion processes on real human commuting data.

Main Results:

• Identification of a phase transition between high and low contagion regimes.
• Discovery that continuous models fail to capture this transition due to stochasticity.
• Definition of an invasion threshold dependent on mobility parameters.

Conclusions:

• The study provides a novel framework for understanding contagion dynamics influenced by human mobility.
• The identified invasion threshold offers guidance for controlling contagion spread by strategically constraining mobility.
• The findings highlight the importance of incorporating realistic mobility patterns into contagion models.