Community Structure in Social Networks: Applications for Epidemiological Modelling

Stephan Kitchovitch and Pietro Liò

PLOS ONE, 2011, vol. 6, issue 7, 1-17

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.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0022220

DOI: 10.1371/journal.pone.0022220

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