Effect of the Interconnected Network Structure on the Epidemic Threshold

TL;DR

This paper models interconnected networks to analyze how their structure influences the epidemic threshold, providing analytical formulas and bounds for the critical point of disease spread in coupled systems.

Contribution

It introduces a mathematical framework for understanding epidemic thresholds in interconnected networks using eigenvalue analysis and perturbation approximations.

Findings

Epidemic threshold depends on the largest eigenvalue of combined adjacency matrices.

Derived bounds for the eigenvalue based on network structure and interconnections.

Validated analytical results with numerical simulations.

Abstract

Most real-world networks are not isolated. In order to function fully, they are interconnected with other networks, and this interconnection influences their dynamic processes. For example, when the spread of a disease involves two species, the dynamics of the spread within each species (the contact network) differs from that of the spread between the two species (the interconnected network). We model two generic interconnected networks using two adjacency matrices, A and B, in which A is a 2N*2N matrix that depicts the connectivity within each of two networks of size N, and B a 2N*2N matrix that depicts the interconnections between the two. Using an N-intertwined mean-field approximation, we determine that a critical susceptable-infected-susceptable (SIS) epidemic threshold in two interconnected networks is 1/{\lambda}1(A+\alpha B), where the infection rate is \beta within each of the two individual networks and \alpha\beta in the interconnected links between the two networks and {\lambda}1(A+\alpha B) is the largest eigenvalue of the matrix A+\alpha B. In order to determine how the epidemic threshold is dependent upon the structure of interconnected networks, we analytically derive {\lambda}1(A+\alpha B) using perturbation approximation for small and large \alpha, the lower and upper bound for any \alpha as a function of the adjacency matrix of the two individual networks, and the interconnections between the two and their largest eigenvalues/eigenvectors. We verify these approximation and boundary values for {\lambda}1(A+\alpha B) using numerical simulations, and determine how component network features affect {\lambda}1(A+\alpha B).