Steady-state analysis of networked epidemic models

TL;DR

This paper introduces two positive feedback frameworks to analyze the steady-state behavior of complex networked epidemic models, providing bounds on susceptible populations and insights into disease penetration levels.

Contribution

It develops a unified approach for steady-state analysis of both group and networked epidemic models, including bounds based on the basic reproduction number.

Findings

The susceptible proportion is bounded by the reciprocal of the BRN.

Different epidemic scenarios correspond to distinct bounds on steady states.

The framework is validated on various existing epidemic models.

Abstract

Compartmental epidemic models with dynamics that evolve over a graph network have gained considerable importance in recent years but analysis of these models is in general difficult due to their complexity. In this paper, we develop two positive feedback frameworks that are applicable to the study of steady-state values in a wide range of compartmental epidemic models, including both group and networked processes. In the case of a group (resp. networked) model, we show that the convergence limit of the susceptible proportion of the population (resp. the susceptible proportion in at least one of the subgroups) is upper bounded by the reciprocal of the basic reproduction number (BRN) of the model. The BRN, when it is greater than unity, thus demonstrates the level of penetration into a subpopulation by the disease. Both non-strict and strict bounds on the convergence limits are derived and shown to correspond to substantially distinct scenarios in the epidemic processes, one in the presence of the endemic state and another without. Formulae for calculating the limits are provided in the latter case. We apply the developed framework to examining various group and networked epidemic models commonly seen in the literature to verify the validity of our conclusions.