Edge-based epidemic spreading in degree-correlated complex networks

Area of Science:

• Epidemiology
• Network Science
• Mathematical Biology

Background:

• Growing networks can develop degree-degree correlations, influencing disease spread.
• Static network assumptions are insufficient for modeling epidemics on growing populations.
• Understanding epidemic dynamics in complex networks is crucial for public health.

Purpose of the Study:

• To formulate a novel edge-based SIR epidemic model for degree-correlated networks.
• To capture epidemic dynamics (growth, peak, final size) in static and growing networks.
• To provide a framework for predicting and controlling disease spread in biased-mixing populations.

Main Methods:

• Developed an edge-based SIR epidemic model applicable to degree-correlated networks.
• Derived rate equations to compute node degree correlations in growing networks.
• Validated model predictions against stochastic SIR simulations on degree-correlated networks.

Main Results:

• The model accurately predicts epidemic growth phases, peak, and final size.
• Theoretically derived basic reproduction number (R0) and final epidemic size.
• Demonstrated model robustness on degree-correlated networks with clustering.

Conclusions:

• The proposed model effectively traces epidemic spread on dynamic, degree-correlated networks.
• Offers improved accuracy for predicting and controlling diseases in growing populations.
• Provides a valuable tool for understanding epidemic behavior in complex, real-world networks.