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Social encounter networks: collective properties and disease transmission

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Social encounter networks: collective properties and disease transmission. / Danon, Leon; House, Thomas A.; Read, Jonathan M. et al.
In: Interface, Vol. 9, No. 76, 07.11.2012, p. 2826-2833.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Danon, L, House, TA, Read, JM & Keeling, MJ 2012, 'Social encounter networks: collective properties and disease transmission', Interface, vol. 9, no. 76, pp. 2826-2833. https://doi.org/10.1098/rsif.2012.0357

APA

Vancouver

Danon L, House TA, Read JM, Keeling MJ. Social encounter networks: collective properties and disease transmission. Interface. 2012 Nov 7;9(76):2826-2833. Epub 2012 Jun 20. doi: 10.1098/rsif.2012.0357

Author

Danon, Leon ; House, Thomas A. ; Read, Jonathan M. et al. / Social encounter networks : collective properties and disease transmission. In: Interface. 2012 ; Vol. 9, No. 76. pp. 2826-2833.

Bibtex

@article{de5eb56244f9408db409115a7427f08b,
title = "Social encounter networks: collective properties and disease transmission",
abstract = "A fundamental challenge of modern infectious disease epidemiology is to quantify the networks of social and physical contacts through which transmission can occur. Understanding the collective properties of these interactions is critical for both accurate prediction of the spread of infection and determining optimal control measures. However, even the basic properties of such networks are poorly quantified, forcing predictions to be made based on strong assumptions concerning network structure. Here, we report on the results of a large-scale survey of social encounters mainly conducted in Great Britain. First, we characterize the distribution of contacts, which possesses a lognormal body and a power-law tail with an exponent of -2.45; we provide a plausible mechanistic model that captures this form. Analysis of the high level of local clustering of contacts reveals additional structure within the network, implying that social contacts are degree assortative. Finally, we describe the epidemiological implications of this local network structure: these contradict the usual predictions from networks with heavy-tailed degree distributions and contain public-health messages about control. Our findings help us to determine the types of realistic network structure that should be assumed in future population level studies of infection transmission, leading to better interpretations of epidemiological data and more appropriate policy decisions.",
keywords = "social contact, epidemic, infectious disease, power law, survey, PANDEMIC INFLUENZA, CONTACT NETWORK, SPREAD, EPIDEMICS, OUTBREAKS, MODEL, POPULATIONS, EMERGENCE, DYNAMICS, PATTERNS",
author = "Leon Danon and House, {Thomas A.} and Read, {Jonathan M.} and Keeling, {Matt J.}",
year = "2012",
month = nov,
day = "7",
doi = "10.1098/rsif.2012.0357",
language = "English",
volume = "9",
pages = "2826--2833",
journal = "Interface",
issn = "1742-5689",
publisher = "Royal Society of London",
number = "76",

}

RIS

TY - JOUR

T1 - Social encounter networks

T2 - collective properties and disease transmission

AU - Danon, Leon

AU - House, Thomas A.

AU - Read, Jonathan M.

AU - Keeling, Matt J.

PY - 2012/11/7

Y1 - 2012/11/7

N2 - A fundamental challenge of modern infectious disease epidemiology is to quantify the networks of social and physical contacts through which transmission can occur. Understanding the collective properties of these interactions is critical for both accurate prediction of the spread of infection and determining optimal control measures. However, even the basic properties of such networks are poorly quantified, forcing predictions to be made based on strong assumptions concerning network structure. Here, we report on the results of a large-scale survey of social encounters mainly conducted in Great Britain. First, we characterize the distribution of contacts, which possesses a lognormal body and a power-law tail with an exponent of -2.45; we provide a plausible mechanistic model that captures this form. Analysis of the high level of local clustering of contacts reveals additional structure within the network, implying that social contacts are degree assortative. Finally, we describe the epidemiological implications of this local network structure: these contradict the usual predictions from networks with heavy-tailed degree distributions and contain public-health messages about control. Our findings help us to determine the types of realistic network structure that should be assumed in future population level studies of infection transmission, leading to better interpretations of epidemiological data and more appropriate policy decisions.

AB - A fundamental challenge of modern infectious disease epidemiology is to quantify the networks of social and physical contacts through which transmission can occur. Understanding the collective properties of these interactions is critical for both accurate prediction of the spread of infection and determining optimal control measures. However, even the basic properties of such networks are poorly quantified, forcing predictions to be made based on strong assumptions concerning network structure. Here, we report on the results of a large-scale survey of social encounters mainly conducted in Great Britain. First, we characterize the distribution of contacts, which possesses a lognormal body and a power-law tail with an exponent of -2.45; we provide a plausible mechanistic model that captures this form. Analysis of the high level of local clustering of contacts reveals additional structure within the network, implying that social contacts are degree assortative. Finally, we describe the epidemiological implications of this local network structure: these contradict the usual predictions from networks with heavy-tailed degree distributions and contain public-health messages about control. Our findings help us to determine the types of realistic network structure that should be assumed in future population level studies of infection transmission, leading to better interpretations of epidemiological data and more appropriate policy decisions.

KW - social contact

KW - epidemic

KW - infectious disease

KW - power law

KW - survey

KW - PANDEMIC INFLUENZA

KW - CONTACT NETWORK

KW - SPREAD

KW - EPIDEMICS

KW - OUTBREAKS

KW - MODEL

KW - POPULATIONS

KW - EMERGENCE

KW - DYNAMICS

KW - PATTERNS

U2 - 10.1098/rsif.2012.0357

DO - 10.1098/rsif.2012.0357

M3 - Journal article

VL - 9

SP - 2826

EP - 2833

JO - Interface

JF - Interface

SN - 1742-5689

IS - 76

ER -