Cargando…

Network theory and SARS: predicting outbreak diversity

Many infectious diseases spread through populations via the networks formed by physical contacts among individuals. The patterns of these contacts tend to be highly heterogeneous. Traditional “compartmental” modeling in epidemiology, however, assumes that population groups are fully mixed, that is,...

Descripción completa

Detalles Bibliográficos
Autores principales: Meyers, Lauren Ancel, Pourbohloul, Babak, Newman, M.E.J., Skowronski, Danuta M., Brunham, Robert C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7094100/
https://www.ncbi.nlm.nih.gov/pubmed/15498594
http://dx.doi.org/10.1016/j.jtbi.2004.07.026
_version_ 1783510400173604864
author Meyers, Lauren Ancel
Pourbohloul, Babak
Newman, M.E.J.
Skowronski, Danuta M.
Brunham, Robert C.
author_facet Meyers, Lauren Ancel
Pourbohloul, Babak
Newman, M.E.J.
Skowronski, Danuta M.
Brunham, Robert C.
author_sort Meyers, Lauren Ancel
collection PubMed
description Many infectious diseases spread through populations via the networks formed by physical contacts among individuals. The patterns of these contacts tend to be highly heterogeneous. Traditional “compartmental” modeling in epidemiology, however, assumes that population groups are fully mixed, that is, every individual has an equal chance of spreading the disease to every other. Applications of compartmental models to Severe Acute Respiratory Syndrome (SARS) resulted in estimates of the fundamental quantity called the basic reproductive number [Formula: see text] —the number of new cases of SARS resulting from a single initial case—above one, implying that, without public health intervention, most outbreaks should spark large-scale epidemics. Here we compare these predictions to the early epidemiology of SARS. We apply the methods of contact network epidemiology to illustrate that for a single value of [Formula: see text] , any two outbreaks, even in the same setting, may have very different epidemiological outcomes. We offer quantitative insight into the heterogeneity of SARS outbreaks worldwide, and illustrate the utility of this approach for assessing public health strategies.
format Online
Article
Text
id pubmed-7094100
institution National Center for Biotechnology Information
language English
publishDate 2005
publisher Elsevier Ltd.
record_format MEDLINE/PubMed
spelling pubmed-70941002020-03-25 Network theory and SARS: predicting outbreak diversity Meyers, Lauren Ancel Pourbohloul, Babak Newman, M.E.J. Skowronski, Danuta M. Brunham, Robert C. J Theor Biol Article Many infectious diseases spread through populations via the networks formed by physical contacts among individuals. The patterns of these contacts tend to be highly heterogeneous. Traditional “compartmental” modeling in epidemiology, however, assumes that population groups are fully mixed, that is, every individual has an equal chance of spreading the disease to every other. Applications of compartmental models to Severe Acute Respiratory Syndrome (SARS) resulted in estimates of the fundamental quantity called the basic reproductive number [Formula: see text] —the number of new cases of SARS resulting from a single initial case—above one, implying that, without public health intervention, most outbreaks should spark large-scale epidemics. Here we compare these predictions to the early epidemiology of SARS. We apply the methods of contact network epidemiology to illustrate that for a single value of [Formula: see text] , any two outbreaks, even in the same setting, may have very different epidemiological outcomes. We offer quantitative insight into the heterogeneity of SARS outbreaks worldwide, and illustrate the utility of this approach for assessing public health strategies. Elsevier Ltd. 2005-01-07 2004-09-23 /pmc/articles/PMC7094100/ /pubmed/15498594 http://dx.doi.org/10.1016/j.jtbi.2004.07.026 Text en Copyright © 2004 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Meyers, Lauren Ancel
Pourbohloul, Babak
Newman, M.E.J.
Skowronski, Danuta M.
Brunham, Robert C.
Network theory and SARS: predicting outbreak diversity
title Network theory and SARS: predicting outbreak diversity
title_full Network theory and SARS: predicting outbreak diversity
title_fullStr Network theory and SARS: predicting outbreak diversity
title_full_unstemmed Network theory and SARS: predicting outbreak diversity
title_short Network theory and SARS: predicting outbreak diversity
title_sort network theory and sars: predicting outbreak diversity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7094100/
https://www.ncbi.nlm.nih.gov/pubmed/15498594
http://dx.doi.org/10.1016/j.jtbi.2004.07.026
work_keys_str_mv AT meyerslaurenancel networktheoryandsarspredictingoutbreakdiversity
AT pourbohloulbabak networktheoryandsarspredictingoutbreakdiversity
AT newmanmej networktheoryandsarspredictingoutbreakdiversity
AT skowronskidanutam networktheoryandsarspredictingoutbreakdiversity
AT brunhamrobertc networktheoryandsarspredictingoutbreakdiversity