title: Community structure in social networks: applications for epidemiological modelling.
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.
authors.affiliation: Computer Laboratory, University of Cambridge, Cambridge, United Kingdom. Stephan.Kitchovitch@cl.cam.ac.uk
authors.forename: Stephan
authors.initials: S
authors.lastname: Kitchovitch
authors.forename: Pietro
authors.initials: P
authors.lastname: Liò
mesh_terms: D003141:Communicable Diseases / Q000453:epidemiology / Q000635:transmission; D003198:Computer Simulation; D004196:Disease Outbreaks; D004198:Disease Susceptibility; D016021:Epidemiologic Studies; D006801:Humans; D008954:Models, Biological; D015995:Prevalence; D012111:Residence Characteristics; D012944:Social Support; D013997:Time Factors