Epidemic spreading and risk perception in multiplex networks: A self-organized percolation method

Area of Science:

• Complex systems
• Epidemiology
• Network science

Background:

• Epidemic models often simplify risk perception.
• Multiplex networks, with distinct layers for different interactions, are crucial for understanding real-world phenomena.
• Previous models did not fully capture the interplay between infection dynamics and perceived risk on diverse network structures.

Purpose of the Study:

• To investigate how risk perception affects epidemic spreading on multiplex networks.
• To analyze the role of network structure similarity in epidemic control.
• To develop a self-organized method for determining percolation thresholds in such systems.

Main Methods:

• Utilized a self-organized simulation approach to model epidemic spreading.
• Extended existing models to incorporate risk perception based on infected neighbors.
• Analyzed epidemic dynamics on multiplex networks with varying degrees of similarity between physical and information layers.

Main Results:

• Risk perception significantly alters effective infection probabilities.
• A self-organized method efficiently calculates percolation thresholds.
• Epidemic control is possible only when the physical and information network layers share sufficient similarity; divergence leads to unavoidable spread.

Conclusions:

• The interplay between epidemic dynamics and risk perception is critical in multiplex networks.
• Network structural similarity is a key factor in mitigating epidemic spread through precaution.
• Divergent network structures pose significant challenges for epidemic containment, even with high precaution levels.