Mean-Field and Pairwise Approaches for the SIRI Model on Poisson Networks

TL;DR

This paper explores conditions under which the complex network-based SIRI epidemic model can be accurately approximated by simpler mean-field equations, especially in the context of Poisson networks.

Contribution

It derives parameter relationships that ensure the SIRI model on networks closely follows mean-field ODE trajectories, extending previous SIR results.

Findings

SIRI dynamics on Poisson networks can be approximated by mean-field models when transmission is low.

The susceptible and infectious trajectories align under certain parameter conditions.

The study provides a theoretical foundation for simplifying network epidemic models.

Abstract

Compartmental epidemic models, grounded in mass-action kinetics, often assume homogeneous mixing. Although this neglects network structure, recent results show that for Poisson random graphs, the classical SIR model, especially the susceptible decay curve, matches the susceptible decay dynamics of its network counterpart. Motivated by this, we investigate whether the extended SIRI model with relapse from the recovered class admits a similar correspondence. SIRI dynamics arise in sevaral scenarios like spread of diseases with reactivation and behavioral contagion with relapse. We derive parameter relationships under which the pairwise SIRI model on a Poisson network closely follows the mass-action ODE trajectories. When transmission per contact is small relative to recovery, the susceptible and infectious trajectories of both systems align. This establishes conditions under which nonlinear SIRI dynamics on networks can be effectively approximated by tractable mean-field equations.