Epidemic Processes Over Time-Varying Networks

The spread of viruses in biological networks, computer networks, and human contact networks can have devastating effects; developing and analyzing mathematical models of these systems can provide insights that lead to long-term societal benefits. Prior research has focused mainly on network models w...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on control of network systems Vol. 5; no. 3; pp. 1322 - 1334
Main Authors Pare, Philip E., Beck, Carolyn L., Nedic, Angelia
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.09.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Asymptotic stability
Computer networks
Computer simulation
Computer viruses
Control theory
Convergence
Epidemic processes
Epidemics
Markov processes
Mathematical model
Mathematical models
Networked systems
Silicon
Stability analysis
Stochastic systems
Time-varying systems
Online AccessGet full text

Cover

More Information
Summary:The spread of viruses in biological networks, computer networks, and human contact networks can have devastating effects; developing and analyzing mathematical models of these systems can provide insights that lead to long-term societal benefits. Prior research has focused mainly on network models with static graph structures; however, the systems being modeled typically have dynamic graph structures. In this paper, we consider virus spread models over networks with dynamic graph structures, and we investigate the behavior of these systems. We perform a stability analysis of epidemic processes over time-varying networks, providing sufficient conditions for convergence to the disease-free equilibrium (the origin, or healthy state), in both the deterministic and stochastic cases. We present simulation results and discuss quarantine control via simulation.
ISSN:2325-5870
2372-2533
DOI:10.1109/TCNS.2017.2706138