Data-Driven Distributed Mitigation Strategies and Analysis of Mutating Epidemic Processes

TL;DR

This paper analyzes a time-varying SIS epidemic model on networks, providing conditions for the stability of the healthy state and insights into mitigation strategies amid changing infection parameters.

Contribution

It introduces stability conditions for a dynamic SIS model with evolving parameters and network structures, advancing understanding of epidemic control in complex, changing environments.

Findings

Stable healthy state under homogeneous infection rates on symmetric graphs

Conditions for epidemic eradication in heterogeneous, directed networks

Insights into mitigation strategies for mutating epidemic processes

Abstract

In this paper we study a discrete-time SIS (susceptible-infected-susceptible) model, where the infection and healing parameters and the underlying network may change over time. We provide conditions for the model to be well-defined and study its stability. For systems with homogeneous infection rates over symmetric graphs,we provide a sufficient condition for global exponential stability (GES) of the healthy state, that is, where the virus is eradicated. For systems with heterogeneous virus spread over directed graphs, provided that the variation is not too fast, a sufficient condition for GES of the healthy state is established.