Modeling Epidemic Risk Perception in Networks with Community Structure

  • Franco Bagnoli
  • Daniel Borkmann
  • Andrea Guazzini
  • Emanuele Massaro
  • Stefan Rudolph
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 134)

Abstract

We study the influence of global, local and community-level risk perception on the extinction probability of a disease in several models of social networks. In particular, we study the infection progression as a susceptible-infected-susceptible (SIS) model on several modular networks, formed by a certain number of random and scale-free communities. We find that in the scale-free networks the progression is faster than in random ones with the same average connectivity degree. For what concerns the role of perception, we find that the knowledge of the infection level in one’s own neighborhood is the most effective property in stopping the spreading of a disease, but at the same time the more expensive one in terms of the quantity of required information, thus the cost/effectiveness optimum is a tradeoff between several parameters.

Keywords

Risk perception SIS model Complex networks 

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Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014

Authors and Affiliations

  • Franco Bagnoli
    • 1
    • 2
    • 3
  • Daniel Borkmann
    • 4
  • Andrea Guazzini
    • 5
    • 6
  • Emanuele Massaro
    • 7
  • Stefan Rudolph
    • 8
  1. 1.Department of EnergyUniversity of FlorenceFlorenceItaly
  2. 2.Center for the Study of Complex SystemsUniversity of FlorenceFlorenceItaly
  3. 3.National Institute for Nuclear PhysicsFlorence SectionFlorenceItaly
  4. 4.Communication Systems GroupETH ZurichZurichSwitzerland
  5. 5.National Research CouncilInstitute for Informatics and TelematicsPisaItaly
  6. 6.Department of PsychologyUniversity of FlorenceFlorenceItaly
  7. 7.Department of Computer Science and SystemsUniversity of FlorenceFlorenceItaly
  8. 8.Organic Computing GroupUniversity of AugsburgAugsburgGermany

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