A Bayesian dynamic model for influenza surveillance
Identifieur interne : 000858 ( Pmc/Corpus ); précédent : 000857; suivant : 000859A Bayesian dynamic model for influenza surveillance
Auteurs : Paola Sebastiani ; Kenneth D. Mandl ; Peter Szolovits ; Isaac S. Kohane ; Marco F. RamoniSource :
- Statistics in medicine [ 0277-6715 ] ; 2006.
Abstract
The severe acute respiratory syndrome (SARS) epidemic, the growing fear of an influenza pandemic and the recent shortage of flu vaccine highlight the need for surveillance systems able to provide early, quantitative predictions of epidemic events. We use dynamic Bayesian networks to discover the interplay among four data sources that are monitored for influenza surveillance. By integrating these different data sources into a dynamic model, we identify in children and infants presenting to the pediatric emergency department with respiratory syndromes an early indicator of impending influenza morbidity and mortality. Our findings show the importance of modelling the complex dynamics of data collected for influenza surveillance, and suggest that dynamic Bayesian networks could be suitable modelling tools for developing epidemic surveillance systems.
Url:
DOI: 10.1002/sim.2566
PubMed: 16645996
PubMed Central: 4128871
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PMC:4128871Le document en format XML
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<author><name sortKey="Sebastiani, Paola" sort="Sebastiani, Paola" uniqKey="Sebastiani P" first="Paola" last="Sebastiani">Paola Sebastiani</name>
<affiliation><nlm:aff id="A1">Department of Biostatistics, Boston University, Boston, MA, U.S.A</nlm:aff>
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<author><name sortKey="Mandl, Kenneth D" sort="Mandl, Kenneth D" uniqKey="Mandl K" first="Kenneth D." last="Mandl">Kenneth D. Mandl</name>
<affiliation><nlm:aff id="A2">Children’s Hospital Informatics Program, Harvard Medical School, Boston, MA, U.S.A</nlm:aff>
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<author><name sortKey="Szolovits, Peter" sort="Szolovits, Peter" uniqKey="Szolovits P" first="Peter" last="Szolovits">Peter Szolovits</name>
<affiliation><nlm:aff id="A3">Computer Science and Artificial Intelligence Laboratory, M.I.T., Cambridge, MA, U.S.A</nlm:aff>
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<author><name sortKey="Kohane, Isaac S" sort="Kohane, Isaac S" uniqKey="Kohane I" first="Isaac S." last="Kohane">Isaac S. Kohane</name>
<affiliation><nlm:aff id="A2">Children’s Hospital Informatics Program, Harvard Medical School, Boston, MA, U.S.A</nlm:aff>
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<author><name sortKey="Ramoni, Marco F" sort="Ramoni, Marco F" uniqKey="Ramoni M" first="Marco F." last="Ramoni">Marco F. Ramoni</name>
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<front><div type="abstract" xml:lang="en"><title>SUMMARY</title>
<p id="P2">The severe acute respiratory syndrome (SARS) epidemic, the growing fear of an influenza pandemic and the recent shortage of flu vaccine highlight the need for surveillance systems able to provide early, quantitative predictions of epidemic events. We use dynamic Bayesian networks to discover the interplay among four data sources that are monitored for influenza surveillance. By integrating these different data sources into a dynamic model, we identify in children and infants presenting to the pediatric emergency department with respiratory syndromes an early indicator of impending influenza morbidity and mortality. Our findings show the importance of modelling the complex dynamics of data collected for influenza surveillance, and suggest that dynamic Bayesian networks could be suitable modelling tools for developing epidemic surveillance systems.</p>
</div>
</front>
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<pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
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<front><journal-meta><journal-id journal-id-type="nlm-journal-id">8215016</journal-id>
<journal-id journal-id-type="pubmed-jr-id">7188</journal-id>
<journal-id journal-id-type="nlm-ta">Stat Med</journal-id>
<journal-id journal-id-type="iso-abbrev">Stat Med</journal-id>
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<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
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<title-group><article-title>A Bayesian dynamic model for influenza surveillance</article-title>
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<contrib-group><contrib contrib-type="author"><name><surname>Sebastiani</surname>
<given-names>Paola</given-names>
</name>
<xref ref-type="aff" rid="A1">1</xref>
<xref rid="FN1" ref-type="author-notes">*</xref>
<xref rid="FN2" ref-type="author-notes">†</xref>
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<contrib contrib-type="author"><name><surname>Mandl</surname>
<given-names>Kenneth D.</given-names>
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<xref ref-type="aff" rid="A2">2</xref>
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<contrib contrib-type="author"><name><surname>Szolovits</surname>
<given-names>Peter</given-names>
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<given-names>Isaac S.</given-names>
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<xref ref-type="aff" rid="A2">2</xref>
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<contrib contrib-type="author"><name><surname>Ramoni</surname>
<given-names>Marco F.</given-names>
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<xref ref-type="aff" rid="A2">2</xref>
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<aff id="A1"><label>1</label>
Department of Biostatistics, Boston University, Boston, MA, U.S.A</aff>
<aff id="A2"><label>2</label>
Children’s Hospital Informatics Program, Harvard Medical School, Boston, MA, U.S.A</aff>
<aff id="A3"><label>3</label>
Computer Science and Artificial Intelligence Laboratory, M.I.T., Cambridge, MA, U.S.A</aff>
<author-notes><corresp id="FN1"><label>*</label>
Correspondence to: Paola Sebastiani, Department of Biostatistics, Boston University, Boston, MA, U.S.A</corresp>
<fn id="FN2"><label>†</label>
<p><email>sebas@bu.edu</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="nihms-submitted"><day>4</day>
<month>7</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="ppub"><day>15</day>
<month>6</month>
<year>2006</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>11</day>
<month>8</month>
<year>2014</year>
</pub-date>
<volume>25</volume>
<issue>11</issue>
<fpage>1803</fpage>
<lpage>1825</lpage>
<pmc-comment>elocation-id from pubmed: 10.1002/sim.2566</pmc-comment>
<permissions><copyright-statement>Copyright © 2006 John Wiley & Sons, Ltd.</copyright-statement>
<copyright-year>2006</copyright-year>
</permissions>
<abstract><title>SUMMARY</title>
<p id="P2">The severe acute respiratory syndrome (SARS) epidemic, the growing fear of an influenza pandemic and the recent shortage of flu vaccine highlight the need for surveillance systems able to provide early, quantitative predictions of epidemic events. We use dynamic Bayesian networks to discover the interplay among four data sources that are monitored for influenza surveillance. By integrating these different data sources into a dynamic model, we identify in children and infants presenting to the pediatric emergency department with respiratory syndromes an early indicator of impending influenza morbidity and mortality. Our findings show the importance of modelling the complex dynamics of data collected for influenza surveillance, and suggest that dynamic Bayesian networks could be suitable modelling tools for developing epidemic surveillance systems.</p>
</abstract>
<kwd-group><kwd>dynamic Bayesian networks</kwd>
<kwd>influenza surveillance</kwd>
<kwd>syndromic data</kwd>
</kwd-group>
</article-meta>
</front>
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