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Modeling Epidemic Risk Perception in Networks with Community Structure

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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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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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Correspondence to Franco Bagnoli .

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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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