Epidemic processes over time-varying networks

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

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


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 languageEnglish (US)
Article number7931651
Pages (from-to)1322-1334
Number of pages13
JournalIEEE Transactions on Control of Network Systems
Issue number3
StatePublished - Sep 2018


  • 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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