Epidemic spreading on preferred degree adaptive networks.

Area of Science:

• Epidemiology
• Network Science
• Statistical Mechanics

Background:

• The standard SIS (Susceptible-Infected-Susceptible) model is foundational for understanding epidemic dynamics.
• Real-world networks exhibit fluctuating connectivity, deviating from static models.
• Behavioral responses to perceived epidemic severity can influence disease spread.

Purpose of the Study:

• To investigate epidemic spreading on networks with fluctuating preferred degrees.
• To model and analyze the effects of adaptive behaviors (blind and selective) on epidemic dynamics.
• To develop a mean-field theory for selective adaptive SIS models.

Main Methods:

• Simulation of the SIS model on networks with preferred degree distributions.
• Implementation of 'blind' and 'selective' link adaptation rules based on epidemic perception.
• Derivation of a mean-field theory for selective adaptation scenarios.

Main Results:

• Networks with simple preferred degree rules show unique statistical properties.
• Blind adaptations do not alter the epidemic threshold but affect infection levels.
• Selective adaptations significantly alter both epidemic thresholds and infection levels, depending on adaptation implementation and frequency.

Conclusions:

• Network structure and adaptive behaviors are crucial for realistic epidemic modeling.
• Selective adaptations offer a powerful mechanism to control or exacerbate epidemic spread.
• The proposed mean-field theory provides a valuable framework for understanding selective adaptive dynamics.