Epidemic processes over time-varying networks

Philip E. Pare; Carolyn L. Beck; Angelia Nedich

doi:10.1109/TCNS.2017.2706138

Epidemic processes over time-varying networks

Philip E. Pare, Carolyn L. Beck, Angelia Nedich

Research output: Contribution to journal › Article › peer-review

84 Scopus citations

Abstract

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.

Original language	English (US)
Article number	7931651
Pages (from-to)	1322-1334
Number of pages	13
Journal	IEEE Transactions on Control of Network Systems
Volume	5
Issue number	3
DOIs	https://doi.org/10.1109/TCNS.2017.2706138
State	Published - Sep 2018

Keywords

Epidemic processes
Networked systems
Stochastic systems
Time-varying systems

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications
Control and Optimization

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10.1109/TCNS.2017.2706138

Cite this

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title = "Epidemic processes over time-varying networks",

abstract = "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.",

keywords = "Epidemic processes, Networked systems, Stochastic systems, Time-varying systems",

author = "Pare, {Philip E.} and Beck, {Carolyn L.} and Angelia Nedich",

note = "Funding Information: Manuscript received January 6, 2017; revised March 22, 2017 and April 28, 2017; accepted May 4, 2017. Date of publication May 18, 2017; date of current version September 17, 2018. This work was supported by the National Science Foundation (NSF) under Grants ECCS 15-09302, CCF 0904619, and DMS 13-12907. All material in this paper represents the position of the authors and not necessarily that of the NSF. Recommended by Associate Editor Q. Zhao. (Corresponding author: Philip E. Par{\'e}.) P. E. Par{\'e} and C. L. Beck are with the Coordinated Science Laboratory, University of Illinois at Urbana–Champaign, Urbana, IL 61801 USA (e-mail:, philip.e.pare@gmail.com; beck3@illinois.edu). Funding Information: This work was supported by the National Science Foundation (NSF) under Grants ECCS 15-09302, CCF 0904619, and DMS 13-12907 Publisher Copyright: {\textcopyright} 2014 IEEE.",

year = "2018",

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doi = "10.1109/TCNS.2017.2706138",

language = "English (US)",

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Epidemic processes over time-varying networks

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