Epidemic spreading in weighted networks: an edge-based mean-field solution

Area of Science:

• Network Science
• Epidemiology
• Statistical Physics

Background:

• Epidemic spreading is significantly influenced by weight distributions in complex networks.
• Traditional mean-field approximations often fail to capture the nuances of epidemic dynamics on weighted networks.

Purpose of the Study:

• To investigate the impact of various weight distributions on epidemic prevalence in a susceptible-infected-susceptible (SIS) model.
• To develop and validate an improved analytical method for modeling epidemic dynamics on weighted networks.

Main Methods:

• Simulations of a susceptible-infected-susceptible model on regular random networks with diverse weight distributions.
• Development of an edge-based mean-field solution applicable to general weight distributions.

Main Results:

• Homogeneous weight distributions result in higher epidemic prevalence compared to heterogeneous ones.
• The proposed edge-based mean-field solution quantitatively reproduces simulation results, outperforming traditional approximations.
• The study highlights the limitations of standard mean-field theory in capturing epidemic behavior on weighted networks.

Conclusions:

• Weight distribution is a critical factor in epidemic spreading dynamics on networks.
• The edge-based mean-field approach provides a robust framework for analyzing nonequilibrium steady states in dynamical processes on weighted networks.
• This method offers a more accurate way to predict and understand disease transmission patterns in complex systems.