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Modeling Epidemic Risk Perception in Networks with Community Structure

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Book cover Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS 2012)

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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.

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Acknowledgments

We acknowledge funding from the 7th Framework Programme of the European Union under grant agreement n\(^\circ \) 257756 and n\(^\circ \) 257906.

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Authors and Affiliations

  1. Department of Energy, University of Florence, Florence, Italy

    Franco Bagnoli

  2. Center for the Study of Complex Systems, University of Florence, Florence, Italy

    Franco Bagnoli

  3. National Institute for Nuclear Physics, Florence Section, Florence, Italy

    Franco Bagnoli

  4. Communication Systems Group, ETH Zurich, Zurich, Switzerland

    Daniel Borkmann

  5. National Research Council, Institute for Informatics and Telematics, Pisa, Italy

    Andrea Guazzini

  6. Department of Psychology, University of Florence, Florence, Italy

    Andrea Guazzini

  7. Department of Computer Science and Systems, University of Florence, Florence, Italy

    Emanuele Massaro

  8. Organic Computing Group, University of Augsburg, Augsburg, Germany

    Stefan Rudolph

Authors
  1. Franco Bagnoli
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  2. Daniel Borkmann
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  3. Andrea Guazzini
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  4. Emanuele Massaro
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  5. Stefan Rudolph
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Corresponding author

Correspondence to Franco Bagnoli .

Editor information

Editors and Affiliations

  1. Dalle Molle Institute for Artificial Intelligence (IDSIA), Manno-Lugano, Switzerland

    Gianni A. Di Caro

  2. CNRS UMR 5169, Research Center on Animal Cognition, Université Paul Sabatier, Toulouse, France

    Guy Theraulaz

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

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Bagnoli, F., Borkmann, D., Guazzini, A., Massaro, E., Rudolph, S. (2014). Modeling Epidemic Risk Perception in Networks with Community Structure. In: Di Caro, G., Theraulaz, G. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-319-06944-9_20

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  • DOI: https://doi.org/10.1007/978-3-319-06944-9_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06943-2

  • Online ISBN: 978-3-319-06944-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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