Community structure in social networks: applications for epidemiological modelling.
Public Library of Science (PLoS)
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Kitchovitch, S., & Lio, P. (2011). Community structure in social networks: applications for epidemiological modelling.. PLoS One, 6 (7), e22220. https://doi.org/10.1371/journal.pone.0022220
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.
Humans, Communicable Diseases, Disease Susceptibility, Prevalence, Epidemiologic Studies, Disease Outbreaks, Residence Characteristics, Models, Biological, Time Factors, Social Support, Computer Simulation
External DOI: https://doi.org/10.1371/journal.pone.0022220
This record's URL: https://www.repository.cam.ac.uk/handle/1810/286103
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/