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Social encounter networks: characterizing Great Britain

A major goal of infectious disease epidemiology is to understand and predict the spread of infections within human populations, with the intention of better informing decisions regarding control and intervention. However, the development of fully mechanistic models of transmission requires a quantit...

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Detalles Bibliográficos
Autores principales: Danon, Leon, Read, Jonathan M., House, Thomas A., Vernon, Matthew C., Keeling, Matt J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3712448/
https://www.ncbi.nlm.nih.gov/pubmed/23804621
http://dx.doi.org/10.1098/rspb.2013.1037
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author Danon, Leon
Read, Jonathan M.
House, Thomas A.
Vernon, Matthew C.
Keeling, Matt J.
author_facet Danon, Leon
Read, Jonathan M.
House, Thomas A.
Vernon, Matthew C.
Keeling, Matt J.
author_sort Danon, Leon
collection PubMed
description A major goal of infectious disease epidemiology is to understand and predict the spread of infections within human populations, with the intention of better informing decisions regarding control and intervention. However, the development of fully mechanistic models of transmission requires a quantitative understanding of social interactions and collective properties of social networks. We performed a cross-sectional study of the social contacts on given days for more than 5000 respondents in England, Scotland and Wales, through postal and online survey methods. The survey was designed to elicit detailed and previously unreported measures of the immediate social network of participants relevant to infection spread. Here, we describe individual-level contact patterns, focusing on the range of heterogeneity observed and discuss the correlations between contact patterns and other socio-demographic factors. We find that the distribution of the number of contacts approximates a power-law distribution, but postulate that total contact time (which has a shorter-tailed distribution) is more epidemiologically relevant. We observe that children, public-sector and healthcare workers have the highest number of total contact hours and are therefore most likely to catch and transmit infectious disease. Our study also quantifies the transitive connections made between an individual's contacts (or clustering); this is a key structural characteristic of social networks with important implications for disease transmission and control efficacy. Respondents' networks exhibit high levels of clustering, which varies across social settings and increases with duration, frequency of contact and distance from home. Finally, we discuss the implications of these findings for the transmission and control of pathogens spread through close contact.
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spelling pubmed-37124482013-08-22 Social encounter networks: characterizing Great Britain Danon, Leon Read, Jonathan M. House, Thomas A. Vernon, Matthew C. Keeling, Matt J. Proc Biol Sci Research Articles A major goal of infectious disease epidemiology is to understand and predict the spread of infections within human populations, with the intention of better informing decisions regarding control and intervention. However, the development of fully mechanistic models of transmission requires a quantitative understanding of social interactions and collective properties of social networks. We performed a cross-sectional study of the social contacts on given days for more than 5000 respondents in England, Scotland and Wales, through postal and online survey methods. The survey was designed to elicit detailed and previously unreported measures of the immediate social network of participants relevant to infection spread. Here, we describe individual-level contact patterns, focusing on the range of heterogeneity observed and discuss the correlations between contact patterns and other socio-demographic factors. We find that the distribution of the number of contacts approximates a power-law distribution, but postulate that total contact time (which has a shorter-tailed distribution) is more epidemiologically relevant. We observe that children, public-sector and healthcare workers have the highest number of total contact hours and are therefore most likely to catch and transmit infectious disease. Our study also quantifies the transitive connections made between an individual's contacts (or clustering); this is a key structural characteristic of social networks with important implications for disease transmission and control efficacy. Respondents' networks exhibit high levels of clustering, which varies across social settings and increases with duration, frequency of contact and distance from home. Finally, we discuss the implications of these findings for the transmission and control of pathogens spread through close contact. The Royal Society 2013-08-22 /pmc/articles/PMC3712448/ /pubmed/23804621 http://dx.doi.org/10.1098/rspb.2013.1037 Text en http://creativecommons.org/licenses/by/3.0/ © 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Research Articles
Danon, Leon
Read, Jonathan M.
House, Thomas A.
Vernon, Matthew C.
Keeling, Matt J.
Social encounter networks: characterizing Great Britain
title Social encounter networks: characterizing Great Britain
title_full Social encounter networks: characterizing Great Britain
title_fullStr Social encounter networks: characterizing Great Britain
title_full_unstemmed Social encounter networks: characterizing Great Britain
title_short Social encounter networks: characterizing Great Britain
title_sort social encounter networks: characterizing great britain
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3712448/
https://www.ncbi.nlm.nih.gov/pubmed/23804621
http://dx.doi.org/10.1098/rspb.2013.1037
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