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Contrasting effects of strong ties on SIR and SIS processes in temporal networks

  • Kaiyuan Sun
  • Andrea Baronchelli
  • Nicola Perra
Open Access
Regular Article
Part of the following topical collections:
  1. Topical issue: Temporal Network Theory and Applications

Abstract

Most real networks are characterized by connectivity patterns that evolve in time following complex, non-Markovian, dynamics. Here we investigate the impact of this ubiquitous feature by studying the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected-Susceptible (SIS) epidemic models on activity driven networks with and without memory (i.e., Markovian and non-Markovian). We find that memory inhibits the spreading process in SIR models by shifting the epidemic threshold to larger values and reducing the final fraction of recovered nodes. On the contrary, in SIS processes memory reduces the epidemic threshold and, for a wide range of disease parameters, increases the fraction of nodes affected by the disease in the endemic state. The heterogeneity in tie strengths, and the frequent repetition of strong ties it entails, allows in fact less virulent SIS-like diseases to survive in tightly connected local clusters that serve as reservoir for the virus. We validate this picture by studying both processes on two real temporal networks.

Keywords

Epidemic Model Temporal Network Infected Node Large Connected Component Epidemic Threshold 
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.

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Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Kaiyuan Sun
    • 1
  • Andrea Baronchelli
    • 2
  • Nicola Perra
    • 3
    • 1
  1. 1.Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern UniversityBostonUSA
  2. 2.Department of MathematicsCity University LondonLondonUK
  3. 3.Centre for Business Network Analysis, University of GreenwichLondonUK

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