• DocumentCode
    728200
  • Title

    Disease spread over randomly switched large-scale networks

  • Author

    Ogura, Masaki ; Preciado, Victor M.

  • Author_Institution
    Dept. of Electr. & Syst. Eng., Univ. of Pennsylvania, Philadelphia, PA, USA
  • fYear
    2015
  • fDate
    1-3 July 2015
  • Firstpage
    1782
  • Lastpage
    1787
  • Abstract
    In this paper we study disease spread over a randomly switched network, which is modeled by a stochastic switched differential equation based on the so called N-intertwined model for disease spread over static networks. Assuming that all the edges of the network are independently switched, we present sufficient conditions for the convergence of infection probability to zero. Though the stability theory for switched linear systems can naively derive a sufficient condition for the convergence, the condition cannot be used for large-scale networks because, for a network with n agents, it requires computing the maximum real eigenvalue of a matrix of size exponential in n. On the other hand, our conditions that are based also on the spectral theory of random matrices can be checked by computing the maximum real eigenvalue of a matrix of size n.
  • Keywords
    Markov processes; convergence; diseases; eigenvalues and eigenfunctions; large-scale systems; linear systems; matrix algebra; network theory (graphs); probability; random processes; disease spread; infection probability; maximum real eigenvalue; n-intertwined model; random matrices; randomly switched large-scale networks; spectral theory; stability theory; static networks; stochastic switched differential equation; sufficient condition; switched linear systems; Diseases; Eigenvalues and eigenfunctions; Linear systems; Markov processes; Mathematical model; Stability analysis; Switches;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2015
  • Conference_Location
    Chicago, IL
  • Print_ISBN
    978-1-4799-8685-9
  • Type

    conf

  • DOI
    10.1109/ACC.2015.7170991
  • Filename
    7170991