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Network theory and SARS: predicting outbreak diversity

Identifieur interne : 001216 ( Pmc/Curation ); précédent : 001215; suivant : 001217

Network theory and SARS: predicting outbreak diversity

Auteurs : Lauren Ancel Meyers [États-Unis] ; Babak Pourbohloul [Canada] ; M. E. J. Newman [États-Unis] ; Danuta M. Skowronski [Canada] ; Robert C. Brunham [Canada]

Source :

RBID : PMC:7094100

Abstract

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 R0—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 R0, 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.


Url:
DOI: 10.1016/j.jtbi.2004.07.026
PubMed: 15498594
PubMed Central: 7094100

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PMC:7094100

Le document en format XML

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<publisher-name>Elsevier Ltd.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">15498594</article-id>
<article-id pub-id-type="pmc">7094100</article-id>
<article-id pub-id-type="publisher-id">S0022-5193(04)00351-0</article-id>
<article-id pub-id-type="doi">10.1016/j.jtbi.2004.07.026</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Network theory and SARS: predicting outbreak diversity</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Meyers</surname>
<given-names>Lauren Ancel</given-names>
</name>
<email>laurenmeyers@mail.utexas.edu</email>
<xref rid="aff1" ref-type="aff">a</xref>
<xref rid="aff2" ref-type="aff">b</xref>
<xref rid="cor1" ref-type="corresp">*</xref>
<xref rid="fn1" ref-type="fn">1</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Pourbohloul</surname>
<given-names>Babak</given-names>
</name>
<xref rid="aff3" ref-type="aff">c</xref>
<xref rid="fn1" ref-type="fn">1</xref>
<xref rid="fn2" ref-type="fn">2</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Newman</surname>
<given-names>M.E.J.</given-names>
</name>
<xref rid="aff2" ref-type="aff">b</xref>
<xref rid="aff4" ref-type="aff">d</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Skowronski</surname>
<given-names>Danuta M.</given-names>
</name>
<xref rid="aff3" ref-type="aff">c</xref>
<xref rid="fn2" ref-type="fn">2</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Brunham</surname>
<given-names>Robert C.</given-names>
</name>
<xref rid="aff3" ref-type="aff">c</xref>
<xref rid="fn2" ref-type="fn">2</xref>
</contrib>
</contrib-group>
<aff id="aff1">
<label>a</label>
Section of Integrative Biology and Institute for Cellular and Molecular Biology, University of Texas at Austin, 1 University Station C0930, Austin, TX 78712, USA</aff>
<aff id="aff2">
<label>b</label>
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA</aff>
<aff id="aff3">
<label>c</label>
University of British Columbia Centre for Disease Control, 655 West 12th Avenue, Vancouver, British Columbia, Canada V5Z 4R4</aff>
<aff id="aff4">
<label>d</label>
Center for the Study of Complex Systems, University of Michigan, Randall Laboratory, 500 E. University Ave., Ann Arbor, MI 48109-1120, USA</aff>
<author-notes>
<corresp id="cor1">
<label>*</label>
Corresponding author. Section of Integrative Biology, University of Texas, 1 University Station #C0930, Austin, TX 78712, USA. Tel.: +1-512-471-4950; fax: +1-512-471-3878.
<email>laurenmeyers@mail.utexas.edu</email>
</corresp>
<fn id="fn1">
<label>1</label>
<p>These authors contributed equally to the conception and delivery of this work.</p>
</fn>
<fn id="fn2">
<label>2</label>
<p>Department of Health Care and Epidemiology, Faculty of Medicine, Univeristy of British Columbia, 5804 Fairview Avenue, Vancouver, BC, Canada V6T 1Z3.</p>
</fn>
</author-notes>
<pub-date pub-type="pmc-release">
<day>23</day>
<month>9</month>
<year>2004</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="ppub">
<day>7</day>
<month>1</month>
<year>2005</year>
</pub-date>
<pub-date pub-type="epub">
<day>23</day>
<month>9</month>
<year>2004</year>
</pub-date>
<volume>232</volume>
<issue>1</issue>
<fpage>71</fpage>
<lpage>81</lpage>
<history>
<date date-type="received">
<day>3</day>
<month>5</month>
<year>2004</year>
</date>
<date date-type="rev-recd">
<day>6</day>
<month>7</month>
<year>2004</year>
</date>
<date date-type="accepted">
<day>22</day>
<month>7</month>
<year>2004</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright © 2004 Elsevier Ltd. All rights reserved.</copyright-statement>
<copyright-year>2004</copyright-year>
<copyright-holder>Elsevier Ltd</copyright-holder>
<license>
<license-p>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.</license-p>
</license>
</permissions>
<abstract>
<p>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
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—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
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, 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.</p>
</abstract>
<kwd-group>
<title>Keywords</title>
<kwd>SARS</kwd>
<kwd>Epidemiology</kwd>
<kwd>Intervention</kwd>
<kwd>Contact network</kwd>
<kwd>Mathematical model</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>

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