Epidemics with temporary link deactivation in scale-free networks.

Area of Science:

• Epidemiology
• Network Science
• Mathematical Modeling

Background:

• Individuals alter social contacts to prevent disease transmission during epidemics.
• Previous studies explored various social adaptation mechanisms.
• A novel mechanism involves susceptible individuals deactivating links to infected neighbors.

Purpose of the Study:

• To analyze the impact of a specific social adaptation mechanism on scale-free networks.
• To characterize the topology of the subnetwork formed by active links.
• To develop and validate improved mean-field equations for epidemic modeling under this adaptation.

Main Methods:

• Investigated a scale-free network model with a link deactivation/reactivation adaptation mechanism.
• Analyzed the topology of the subnetwork comprising only active links.
• Derived mean-field equations using moment closure approximations based on conditional degree distributions.
• Compared model predictions with numerical simulation results.

Main Results:

• The subnetwork of active links exhibits a topology fundamentally different from the original scale-free network.
• The scaling exponent of the active degree distribution was predicted.
• Improved mean-field equations demonstrated better agreement with simulations than standard equations.

Conclusions:

• Social adaptation significantly alters network topology during epidemics.
• The proposed mean-field approach accurately captures epidemic dynamics on networks with this adaptation.
• This work enhances the predictive power of epidemic models on complex networks.