Community structure in social networks: applications for epidemiological modelling.
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 incl...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2011-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC3138783?pdf=render |
id |
doaj-b2d086531e234fbc86f60378da7e065c |
---|---|
record_format |
Article |
spelling |
doaj-b2d086531e234fbc86f60378da7e065c2020-11-25T01:38:53ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0167e2222010.1371/journal.pone.0022220Community structure in social networks: applications for epidemiological modelling.Stephan KitchovitchPietro LiòDuring 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.http://europepmc.org/articles/PMC3138783?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Stephan Kitchovitch Pietro Liò |
spellingShingle |
Stephan Kitchovitch Pietro Liò Community structure in social networks: applications for epidemiological modelling. PLoS ONE |
author_facet |
Stephan Kitchovitch Pietro Liò |
author_sort |
Stephan Kitchovitch |
title |
Community structure in social networks: applications for epidemiological modelling. |
title_short |
Community structure in social networks: applications for epidemiological modelling. |
title_full |
Community structure in social networks: applications for epidemiological modelling. |
title_fullStr |
Community structure in social networks: applications for epidemiological modelling. |
title_full_unstemmed |
Community structure in social networks: applications for epidemiological modelling. |
title_sort |
community structure in social networks: applications for epidemiological modelling. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2011-01-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC3138783?pdf=render |
work_keys_str_mv |
AT stephankitchovitch communitystructureinsocialnetworksapplicationsforepidemiologicalmodelling AT pietrolio communitystructureinsocialnetworksapplicationsforepidemiologicalmodelling |
_version_ |
1725051747681959936 |