Synergistic epidemic spreading in correlated networks

Abstract

We investigate the effect of degree correlation on a susceptible-infected-susceptible (SIS) model with a nonlinear cooperative effect (synergy) in infectious transmissions. In a mean-field treatment of the synergistic SIS model on a bimodal network with tunable degree correlation, we identify a discontinuous transition that is independent of the degree correlation strength unless the synergy is absent or extremely weak. Regardless of synergy (absent or present), a positive and negative degree correlation in the model reduces and raises the epidemic threshold, respectively. For networks with a strongly positive degree correlation, the mean-field treatment predicts the emergence of two discontinuous jumps in the steady-state infected density. To test the mean-field treatment, we provide approximate master equations of the present model. We quantitatively confirm that the approximate master equations agree with not only all qualitative predictions of the mean-field treatment but also corresponding Monte-Carlo simulations.

11 pages, 9 figures

References in corpus (6)

• Statistical physics of social dynamics
• Mitigation of Malicious Attacks on Networks
• Thresholds for epidemic spreading in networks
• Percolation on correlated networks
• Cavity analysis on the robustness of random networks against targeted attacks: Influences of degree-degree correlations
• Robustness analysis of bimodal networks in the whole range of degree correlation