SrasV1, Pmc, Corpus, bibRecord, 000D40

A Bayesian dynamic model for influenza surveillance

Identifieur interne : 000D40 ( Pmc/Corpus ); précédent : 000D39; suivant : 000D41

A Bayesian dynamic model for influenza surveillance

Auteurs : Paola Sebastiani ; Kenneth D. Mandl ; Peter Szolovits ; Isaac S. Kohane ; Marco F. Ramoni

Source :

Statistics in medicine [ 0277-6715 ] ; 2006.

RBID : PMC:4128871

Abstract

SUMMARY

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:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4128871

DOI: 10.1002/sim.2566
PubMed: 16645996
PubMed Central: 4128871

Links to Exploration step

PMC:4128871

Le 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>
</affiliation>
</author>
<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>
</affiliation>
</author>
<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>
</affiliation>
</author>
<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>
</affiliation>
</author>
<author><name sortKey="Ramoni, Marco F" sort="Ramoni, Marco F" uniqKey="Ramoni M" first="Marco F." last="Ramoni">Marco F. Ramoni</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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<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>
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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>
</affiliation>
</author>
<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>
</affiliation>
</author>
<author><name sortKey="Ramoni, Marco F" sort="Ramoni, Marco F" uniqKey="Ramoni M" first="Marco F." last="Ramoni">Marco F. Ramoni</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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<series><title level="j">Statistics in medicine</title>
<idno type="ISSN">0277-6715</idno>
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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>
</TEI>
<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>
  <pmc-dir>properties manuscript</pmc-dir>
  <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>
<journal-title-group><journal-title>Statistics in medicine</journal-title>
</journal-title-group>
<issn pub-type="ppub">0277-6715</issn>
<issn pub-type="epub">1097-0258</issn>
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<article-meta><article-id pub-id-type="pmid">16645996</article-id>
<article-id pub-id-type="pmc">4128871</article-id>
<article-id pub-id-type="doi">10.1002/sim.2566</article-id>
<article-id pub-id-type="manuscript">NIHMS491687</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>A Bayesian dynamic model for influenza surveillance</article-title>
</title-group>
<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>
</contrib>
<contrib contrib-type="author"><name><surname>Mandl</surname>
<given-names>Kenneth D.</given-names>
</name>
<xref ref-type="aff" rid="A2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Szolovits</surname>
<given-names>Peter</given-names>
</name>
<xref ref-type="aff" rid="A3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kohane</surname>
<given-names>Isaac S.</given-names>
</name>
<xref ref-type="aff" rid="A2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ramoni</surname>
<given-names>Marco F.</given-names>
</name>
<xref ref-type="aff" rid="A2">2</xref>
</contrib>
</contrib-group>
<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>
</pmc>
</record>

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A Bayesian dynamic model for influenza surveillance

A Bayesian dynamic model for influenza surveillance

Source :

Abstract

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Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

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