On the dynamic behavior of the network SIRS epidemic model

TL;DR

This paper analyzes the SIRS epidemic model on networks, identifying a key parameter R_0 that determines whether the disease dies out or persists, and provides a method to compute the endemic equilibrium.

Contribution

It generalizes existing results by characterizing the epidemic dynamics for heterogeneous recovery and immunity loss rates on general networks.

Findings

R_0 is the dominant eigenvalue of a normalized interaction matrix.

A transcritical bifurcation occurs at R_0=1, switching stability of equilibria.

A distributed iterative algorithm with convergence guarantees computes the endemic equilibrium.

Abstract

We study the Suscectible-Infected-Recovered-Susceptible (SIRS) epidemic model on deterministic networks. For connected but otherwise general interaction patterns and heterogeneous recovery and loss-of-immunity rates, we identify a fundamental parameter R_0 (the basic reproduction number), which fully characterizes the qualitative dynamic behavior of the system. This parameter is the dominant eigenvalue of a rescaled version of the interaction matrix, whose rows are normalized by the corresponding recovery rates. We prove that a transcritical bifurcation occurs as R_0 crosses the threshold value 1. Specifically, we show that, if R_0 does not exceed 1, then the disease-free equilibrium is globally asymptotically stable, whereas, if R_0 is larger than 1, then the disease-free equilibrium is unstable and there exists a unique endemic equilibrium, which is asymptotically stable. As a byproduct of our analysis, we also identify key monotonicity properties of the dependence of the endemic equilibrium on the model parameters (the interaction matrix as well as the recovery rates and the loss-of-immunity rates) and obtain a distributed iterative algorithm for its computation, with provable convergence guarantees. Our results extend existing ones available in the literature for network SIRS epidemic models with rank-one interaction matrices and homogeneous recovery rates (including the single homogeneous population SIRS epidemic model).