Risk Perception and Epidemic Spreading in Multiplex Networks

  • Franco BagnoliEmail author
  • Emanuele Massaro
Part of the Emergence, Complexity and Computation book series (ECC, volume 14)


In this paper we study the interplay between epidemic spreading and risk perception on multiplex networks. The basic idea is that the effective infection probability is affected by the perception of the risk of being infected, which we assume to be related to the number of infected neighbours. We re-derive previous results using a self-organized method, that automatically gives the percolation threshold in just one simulation. We then extend the model to multiplex networks considering that people get infected by contacts in real life but often gather information from an information networks, that may be quite different from the real ones. The similarity between the real and information networks determine the possibility of stopping the infection for a sufficiently high precaution level: if the networks are too different there is no mean of avoiding the epidemics.


Risk Perception Percolation Threshold Information Network Virtual Network Contact Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pandemic Scares Throughout History. Health Magazine (2013)Google Scholar
  2. 2.
  3. 3.
    The “false” pandemic: Drug firms cashed in on scare over swine flu, claims Euro health chief. Daily Mail (2010)Google Scholar
  4. 4.
    Moore, C., Newman, M.E.J.: Epidemics and percolation in small-world networks. Phys. Rev. E 61, 5678–5682 (2000)CrossRefGoogle Scholar
  5. 5.
    Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86, 3200–3203 (2001)CrossRefGoogle Scholar
  6. 6.
    Newman, M.E.J.: Exact solutions of epidemic models on networks. Working Papers 01-12-073. Santa Fe Institute (December 2001)Google Scholar
  7. 7.
    May, R.M., Lloyd, A.L.: Infection dynamics on scale-free networks. Phys. Rev. E 64, 066112 (2001)Google Scholar
  8. 8.
    Pastor-Satorras, R., Vespignani, A.: Immunization of complex networks. Phys. Rev. E 65, 036104 (2002)Google Scholar
  9. 9.
    Bagnoli, F., Liò, P., Sguanci, L.: Risk perception in epidemic modeling. Phys. Rev. E 76, 061904 (2007)Google Scholar
  10. 10.
    Ginsberg, J., Mohebbi, M., Patel, R., Brammer, L., Smolinski, M., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014 (2009)CrossRefGoogle Scholar
  11. 11.
    Scanfeld, D., Scanfeld, V., Larson, E.L.: Dissemination of health information through social networks: Twitter and antibiotics. American Journal of Infection Control 38(3), 182–188 (2010)CrossRefGoogle Scholar
  12. 12.
    Chew, C., Eysenbach, G.: Pandemics in the age of twitter: Content analysis of tweets during the 2009 h1n1 outbreak. PLoS One 5(11), 014118 (2010)Google Scholar
  13. 13.
    The State of the News Media. The Pew Research Center’s project for Excellence in Journalism (2010)Google Scholar
  14. 14.
    Kurant, M., Thiran, P.: Layered complex networks. Phys. Rev. Lett. 96, 138701 (2006)CrossRefGoogle Scholar
  15. 15.
    Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.-P.: Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980), 876–878 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Szell, M., Lambiotte, R., Thurner, S.: Multirelational Organization of Large-scale Social Networks in an Online World (2010)Google Scholar
  17. 17.
    Arenas, A., Lozano, S., Rodriguez, X.-P.: Evolution of cooperation in multiplex networks. Scientific Reports 2 (2012)Google Scholar
  18. 18.
    Bianconi, G.: Statistical mechanics of multiplex networks: Entropy and overlap. Phys. Rev. E 87, 062806 (2013)Google Scholar
  19. 19.
    Buldyrev, S.V., Parshani, R., Paul, G., Stanley, H.E., Havlin, S.: Catastrophic cascade of failures in interdependent networks. Nature 464(7291), 1025–1028 (2010)CrossRefGoogle Scholar
  20. 20.
    Gao, J., Buldyrev, S.V., Stanley, H.E., Havlin, S.: Networks formed from interdependent networks. Nat. Phys. 8(1), 40–48 (2012)CrossRefGoogle Scholar
  21. 21.
    Granell, C., Gómez, S., Arenas, A.: Dynamical interplay between awareness and epidemic spreading in multiplex networks. Phys. Rev. Lett. 111, 128701 (2013)CrossRefGoogle Scholar
  22. 22.
    Bagnoli, F., Palmerini, P., Rechtman, R.: Algorithmic mapping from criticality to self-organized criticality. Phys. Rev. E 55, 3970–3976 (1997)CrossRefGoogle Scholar
  23. 23.
    Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of networks. Advances in Physics 51, 1079–1187 (2002)CrossRefGoogle Scholar
  24. 24.
    Pastor-Satorras, R., Vespignani, A.: Epidemic dynamics in finite size scale-free networks. Physical Review E 65(035108) (2002)Google Scholar
  25. 25.
    Domany, E., Kinzel, W.: Equivalence of cellular automata to ising models and directed percolation. Phys. Rev. Lett. 53, 311–314 (1984)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Dept. Physics and Astronomy and CSDCUniversity of FlorenceSesto FiorentinoItaly
  2. 2.Risk and Decision Science TeamUS Army Engineer Research and, Development CenterCOncordUSA
  3. 3.Department of Civil and Environmental EngineeringCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations