Non-perturbative heterogeneous mean-field approach to epidemic spreading in complex networks

TL;DR

This paper introduces a non-perturbative heterogeneous mean-field method for modeling epidemic spreading on complex networks, improving accuracy and computational efficiency over traditional approaches especially far from the epidemic threshold.

Contribution

It develops a non-perturbative, fixed point iterative formulation of the heterogeneous mean-field approach that does not rely on linear approximations or proximity assumptions.

Findings

Close agreement with Monte Carlo simulations

Enhanced predictive power of epidemic prevalence

More efficient computational framework

Abstract

Since roughly a decade ago, network science has focused among others on the problem of how the spreading of diseases depends on structural patterns. Here, we contribute to further advance our understanding of epidemic spreading processes by proposing a non-perturbative formulation of the heterogeneous mean field approach that has been commonly used in the physics literature to deal with this kind of spreading phenomena. The non-perturbative equations we propose have no assumption about the proximity of the system to the epidemic threshold, nor any linear approximation of the dynamics. In particular, we first develop a probabilistic description at the node level of the epidemic propagation for the so-called susceptible-infected-susceptible family of models, and after we derive the corresponding heterogeneous mean-field approach. We propose to use the full extension of the approach instead of pruning the expansion to first order, which leads to a non-perturbative formulation that can be solved by fixed point iteration, and used with reliability far away from the epidemic threshold to assess the prevalence of the epidemics. Our results are in close agreement with Monte Carlo simulations thus enhancing the predictive power of the classical heterogeneous mean field approach, while providing a more effective framework in terms of computational time.