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Epidemic prediction and control in weighted networks

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Epidemic prediction and control in weighted networks. / Eames, Ken T. D.; Read, Jonathan M.; Edmunds, W. John.

In: Epidemics, Vol. 1, No. 1, 03.2009, p. 70-76.

Research output: Contribution to journalJournal articlepeer-review

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Eames, KTD, Read, JM & Edmunds, WJ 2009, 'Epidemic prediction and control in weighted networks', Epidemics, vol. 1, no. 1, pp. 70-76. https://doi.org/10.1016/j.epidem.2008.12.001

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Eames, Ken T. D. ; Read, Jonathan M. ; Edmunds, W. John. / Epidemic prediction and control in weighted networks. In: Epidemics. 2009 ; Vol. 1, No. 1. pp. 70-76.

Bibtex

@article{b967dabb959245a29aee680df85b341e,
title = "Epidemic prediction and control in weighted networks",
abstract = "Contact networks are often used in epidemiological studies to describe the patterns of interactions within a population. Often, such networks merely indicate which individuals interact, without giving any indication of the strength or intensity of interactions. Here, we use weighted networks, in which every connection has an associated weight, to explore the influence of heterogeneous contact strengths on the effectiveness of control measures. We show that, by using contact weights to evaluate an individual's influence on an epidemic, individual infection risk can be estimated and targeted interventions such as preventative vaccination can be applied effectively. We use a diary study of social mixing behaviour to indicate the patterns of contact weights displayed by a real population in a range of different contexts, including physical interactions; we use these data to show that considerations of link weight can in some cases lead to improved interventions in the case of infections that spread through close contact interactions. However, we also see that simpler measures, such as an individual's total number of social contacts or even just their number of contacts during a single day, can lead to great improvements on random vaccination. We therefore conclude that, for many infections, enhanced social contact data can be simply used to improve disease control but that it is not necessary to have full social mixing information in order to enhance interventions. (C) 2009 Elsevier Inc. All rights reserved.",
keywords = "Social network, Mathematical model, Contact diary, Vaccination",
author = "Eames, {Ken T. D.} and Read, {Jonathan M.} and Edmunds, {W. John}",
year = "2009",
month = mar,
doi = "10.1016/j.epidem.2008.12.001",
language = "English",
volume = "1",
pages = "70--76",
journal = "Epidemics",
issn = "1755-4365",
publisher = "ELSEVIER SCIENCE BV",
number = "1",

}

RIS

TY - JOUR

T1 - Epidemic prediction and control in weighted networks

AU - Eames, Ken T. D.

AU - Read, Jonathan M.

AU - Edmunds, W. John

PY - 2009/3

Y1 - 2009/3

N2 - Contact networks are often used in epidemiological studies to describe the patterns of interactions within a population. Often, such networks merely indicate which individuals interact, without giving any indication of the strength or intensity of interactions. Here, we use weighted networks, in which every connection has an associated weight, to explore the influence of heterogeneous contact strengths on the effectiveness of control measures. We show that, by using contact weights to evaluate an individual's influence on an epidemic, individual infection risk can be estimated and targeted interventions such as preventative vaccination can be applied effectively. We use a diary study of social mixing behaviour to indicate the patterns of contact weights displayed by a real population in a range of different contexts, including physical interactions; we use these data to show that considerations of link weight can in some cases lead to improved interventions in the case of infections that spread through close contact interactions. However, we also see that simpler measures, such as an individual's total number of social contacts or even just their number of contacts during a single day, can lead to great improvements on random vaccination. We therefore conclude that, for many infections, enhanced social contact data can be simply used to improve disease control but that it is not necessary to have full social mixing information in order to enhance interventions. (C) 2009 Elsevier Inc. All rights reserved.

AB - Contact networks are often used in epidemiological studies to describe the patterns of interactions within a population. Often, such networks merely indicate which individuals interact, without giving any indication of the strength or intensity of interactions. Here, we use weighted networks, in which every connection has an associated weight, to explore the influence of heterogeneous contact strengths on the effectiveness of control measures. We show that, by using contact weights to evaluate an individual's influence on an epidemic, individual infection risk can be estimated and targeted interventions such as preventative vaccination can be applied effectively. We use a diary study of social mixing behaviour to indicate the patterns of contact weights displayed by a real population in a range of different contexts, including physical interactions; we use these data to show that considerations of link weight can in some cases lead to improved interventions in the case of infections that spread through close contact interactions. However, we also see that simpler measures, such as an individual's total number of social contacts or even just their number of contacts during a single day, can lead to great improvements on random vaccination. We therefore conclude that, for many infections, enhanced social contact data can be simply used to improve disease control but that it is not necessary to have full social mixing information in order to enhance interventions. (C) 2009 Elsevier Inc. All rights reserved.

KW - Social network

KW - Mathematical model

KW - Contact diary

KW - Vaccination

U2 - 10.1016/j.epidem.2008.12.001

DO - 10.1016/j.epidem.2008.12.001

M3 - Journal article

VL - 1

SP - 70

EP - 76

JO - Epidemics

JF - Epidemics

SN - 1755-4365

IS - 1

ER -