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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,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2005
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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 |
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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 |
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