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Community Structure in Social Networks: Applications for Epidemiological Modelling

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  • Stephan Kitchovitch
  • Pietro Liò

Abstract

During an infectious disease outbreak people will often change their behaviour to reduce their risk of infection. Furthermore, in a given population, the level of perceived risk of infection will vary greatly amongst individuals. The difference in perception could be due to a variety of factors including varying levels of information regarding the pathogen, quality of local healthcare, availability of preventative measures, etc. In this work we argue that we can split a social network, representing a population, into interacting communities with varying levels of awareness of the disease. We construct a theoretical population and study which such communities suffer most of the burden of the disease and how their awareness affects the spread of infection. We aim to gain a better understanding of the effects that community-structured networks and variations in awareness, or risk perception, have on the disease dynamics and to promote more community-resolved modelling in epidemiology.

Suggested Citation

  • Stephan Kitchovitch & Pietro Liò, 2011. "Community Structure in Social Networks: Applications for Epidemiological Modelling," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-17, July.
    • Handle: RePEc:plo:pone00:0022220
    DOI: 10.1371/journal.pone.0022220
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