Epidemic spreading on complex networks with general degree and weight distributions

TL;DR

This paper introduces a novel edge-weight based compartmental model to accurately predict epidemic thresholds and sizes on complex networks with diverse degree and weight distributions, and proposes an effective edge removal strategy for epidemic control.

Contribution

It develops a new analytical approach for epidemic modeling on weighted networks with heterogeneous distributions, validated by numerical simulations, and suggests a targeted edge removal strategy for controlling spread.

Findings

The model accurately predicts epidemic thresholds and sizes.

Targeted removal of highly weighted edges effectively controls epidemics.

The approach works well even with highly heterogeneous network distributions.

Abstract

The spread of disease on complex networks has attracted widely attention in the physics community. Recent works have demonstrated that heterogeneous degree and weight distributions have a significant influence on the epidemic dynamics. In this study, a novel edge-weight based compartmental approach is developed to estimate the epidemic threshold and epidemic size (final infected density) on networks with general degree and weight distributions, and a remarkable agreement with numerics is obtained. Even in complex network with the strong heterogeneous degree and weight distributions, this approach is worked. We then propose an edge-weight based removal strategy with different biases, and find that such a strategy can effectively control the spread of epidemic when the highly weighted edges are preferentially removed, especially when the weight distribution of a network is extremely heterogenous. The theoretical results from the suggested method can accurately predict the above removal effectiveness.