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Sampling for Global Epidemic Models and the Topology of an International Airport Network

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Sampling for Global Epidemic Models and the Topology of an International Airport Network

Auteurs : Georgiy Bobashev ; Robert J. Morris ; D. Michael Goedecke

Source :

PLoS ONE [ 1932-6203 ] ; 2008.

RBID : PMC:2522280

Abstract

Mathematical models that describe the global spread of infectious diseases such as influenza, severe acute respiratory syndrome (SARS), and tuberculosis (TB) often consider a sample of international airports as a network supporting disease spread. However, there is no consensus on how many cities should be selected or on how to select those cities. Using airport flight data that commercial airlines reported to the Official Airline Guide (OAG) in 2000, we have examined the network characteristics of network samples obtained under different selection rules. In addition, we have examined different size samples based on largest flight volume and largest metropolitan populations. We have shown that although the bias in network characteristics increases with the reduction of the sample size, a relatively small number of areas that includes the largest airports, the largest cities, the most-connected cities, and the most central cities is enough to describe the dynamics of the global spread of influenza. The analysis suggests that a relatively small number of cities (around 200 or 300 out of almost 3000) can capture enough network information to adequately describe the global spread of a disease such as influenza. Weak traffic flows between small airports can contribute to noise and mask other means of spread such as the ground transportation.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2522280

DOI: 10.1371/journal.pone.0003154
PubMed: 18776932
PubMed Central: 2522280

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<front><div type="abstract" xml:lang="en"><p>Mathematical models that describe the global spread of infectious diseases such as influenza, severe acute respiratory syndrome (SARS), and tuberculosis (TB) often consider a sample of international airports as a network supporting disease spread. However, there is no consensus on how many cities should be selected or on how to select those cities. Using airport flight data that commercial airlines reported to the Official Airline Guide (OAG) in 2000, we have examined the network characteristics of network samples obtained under different selection rules. In addition, we have examined different size samples based on largest flight volume and largest metropolitan populations. We have shown that although the bias in network characteristics increases with the reduction of the sample size, a relatively small number of areas that includes the largest airports, the largest cities, the most-connected cities, and the most central cities is enough to describe the dynamics of the global spread of influenza. The analysis suggests that a relatively small number of cities (around 200 or 300 out of almost 3000) can capture enough network information to adequately describe the global spread of a disease such as influenza. Weak traffic flows between small airports can contribute to noise and mask other means of spread such as the ground transportation.</p>
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  <front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id>
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<article-id pub-id-type="pmc">2522280</article-id>
<article-id pub-id-type="publisher-id">08-PONE-RA-04015R1</article-id>
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<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
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<subject>Public Health and Epidemiology/Health Services Research and Economics</subject>
<subject>Public Health and Epidemiology/Infectious Diseases</subject>
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<title-group><article-title>Sampling for Global Epidemic Models and the Topology of an International Airport Network</article-title>
<alt-title alt-title-type="running-head">Global Epidemic Models</alt-title>
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<contrib-group><contrib contrib-type="author"><name><surname>Bobashev</surname>
<given-names>Georgiy</given-names>
</name>
<xref ref-type="aff" rid="aff1"></xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
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<contrib contrib-type="author"><name><surname>Morris</surname>
<given-names>Robert J.</given-names>
</name>
<xref ref-type="aff" rid="aff1"></xref>
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<contrib contrib-type="author"><name><surname>Goedecke</surname>
<given-names>D. Michael</given-names>
</name>
<xref ref-type="aff" rid="aff1"></xref>
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<aff id="aff1"><addr-line>RTI International, Research Triangle Park, North Carolina, United States of America</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Monk</surname>
<given-names>Nick</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
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<aff id="edit1">University of Nottingham, United Kingdom</aff>
<author-notes><corresp id="cor1">* E-mail: <email>bobashev@rti.org</email>
</corresp>
<fn fn-type="con"><p>Conceived and designed the experiments: GVB. Performed the experiments: GVB RJM DMG. Analyzed the data: GVB DMG. Wrote the paper: GVB RJM DMG.</p>
</fn>
</author-notes>
<pub-date pub-type="collection"><year>2008</year>
</pub-date>
<pub-date pub-type="epub"><day>8</day>
<month>9</month>
<year>2008</year>
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<volume>3</volume>
<issue>9</issue>
<elocation-id>e3154</elocation-id>
<history><date date-type="received"><day>19</day>
<month>3</month>
<year>2008</year>
</date>
<date date-type="accepted"><day>7</day>
<month>8</month>
<year>2008</year>
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<permissions><copyright-statement>Bobashev et al.</copyright-statement>
<copyright-year>2008</copyright-year>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.</license-p>
</license>
</permissions>
<abstract><p>Mathematical models that describe the global spread of infectious diseases such as influenza, severe acute respiratory syndrome (SARS), and tuberculosis (TB) often consider a sample of international airports as a network supporting disease spread. However, there is no consensus on how many cities should be selected or on how to select those cities. Using airport flight data that commercial airlines reported to the Official Airline Guide (OAG) in 2000, we have examined the network characteristics of network samples obtained under different selection rules. In addition, we have examined different size samples based on largest flight volume and largest metropolitan populations. We have shown that although the bias in network characteristics increases with the reduction of the sample size, a relatively small number of areas that includes the largest airports, the largest cities, the most-connected cities, and the most central cities is enough to describe the dynamics of the global spread of influenza. The analysis suggests that a relatively small number of cities (around 200 or 300 out of almost 3000) can capture enough network information to adequately describe the global spread of a disease such as influenza. Weak traffic flows between small airports can contribute to noise and mask other means of spread such as the ground transportation.</p>
</abstract>
<counts><page-count count="8"></page-count>
</counts>
</article-meta>
</front>
</pmc>
<affiliations><list></list>
<tree><noCountry><name sortKey="Bobashev, Georgiy" sort="Bobashev, Georgiy" uniqKey="Bobashev G" first="Georgiy" last="Bobashev">Georgiy Bobashev</name>
<name sortKey="Goedecke, D Michael" sort="Goedecke, D Michael" uniqKey="Goedecke D" first="D. Michael" last="Goedecke">D. Michael Goedecke</name>
<name sortKey="Morris, Robert J" sort="Morris, Robert J" uniqKey="Morris R" first="Robert J." last="Morris">Robert J. Morris</name>
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</record>

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Sampling for Global Epidemic Models and the Topology of an International Airport Network

Sampling for Global Epidemic Models and the Topology of an International Airport Network

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Abstract

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