SrasV1, Pmc, Corpus, bibRecord, 000C04

Modelling respiratory infection control measure effects

Identifieur interne : 000C04 ( Pmc/Corpus ); précédent : 000C03; suivant : 000C05

Modelling respiratory infection control measure effects

Auteurs : C. M. Liao ; S. C. Chen ; C. F. Chang

Source :

Epidemiology and Infection [ 0950-2688 ] ; 2007.

RBID : PMC:2870817

Abstract

SUMMARY

One of the most pressing issues in facing emerging and re-emerging respiratory infections is how to bring them under control with current public health measures. Approaches such as the Wells–Riley equation, competing-risks model, and Von Foerster equation are used to prioritize control-measure efforts. Here we formulate how to integrate those three different types of functional relationship to construct easy-to-use and easy-to-interpret critical-control lines that help determine optimally the intervention strategies for containing airborne infections. We show that a combination of assigned effective public health interventions and enhanced engineering control measures would have a high probability for containing airborne infection. We suggest that integrated analysis to enhance modelling the impact of potential control measures against airborne infections presents an opportunity to assess risks and benefits. We demonstrate the approach with examples of optimal control measures to prioritize respiratory infections of severe acute respiratory syndrome (SARS), influenza, measles, and chickenpox.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2870817

DOI: 10.1017/S0950268807008631
PubMed: 17475088
PubMed Central: 2870817

Links to Exploration step

PMC:2870817

Le document en format XML

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<author><name sortKey="Liao, C M" sort="Liao, C M" uniqKey="Liao C" first="C. M." last="Liao">C. M. Liao</name>
<affiliation><nlm:aff id="aff001">Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan 10617, ROC</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Chen, S C" sort="Chen, S C" uniqKey="Chen S" first="S. C." last="Chen">S. C. Chen</name>
<affiliation><nlm:aff id="aff001">Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan 10617, ROC</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Chang, C F" sort="Chang, C F" uniqKey="Chang C" first="C. F." last="Chang">C. F. Chang</name>
<affiliation><nlm:aff id="aff001">Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan 10617, ROC</nlm:aff>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Modelling respiratory infection control measure effects</title>
<author><name sortKey="Liao, C M" sort="Liao, C M" uniqKey="Liao C" first="C. M." last="Liao">C. M. Liao</name>
<affiliation><nlm:aff id="aff001">Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan 10617, ROC</nlm:aff>
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<author><name sortKey="Chen, S C" sort="Chen, S C" uniqKey="Chen S" first="S. C." last="Chen">S. C. Chen</name>
<affiliation><nlm:aff id="aff001">Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan 10617, ROC</nlm:aff>
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<author><name sortKey="Chang, C F" sort="Chang, C F" uniqKey="Chang C" first="C. F." last="Chang">C. F. Chang</name>
<affiliation><nlm:aff id="aff001">Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan 10617, ROC</nlm:aff>
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<series><title level="j">Epidemiology and Infection</title>
<idno type="ISSN">0950-2688</idno>
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<front><div type="abstract" xml:lang="en"><title>SUMMARY</title>
<p>One of the most pressing issues in facing emerging and re-emerging respiratory infections is how to bring them under control with current public health measures. Approaches such as the Wells–Riley equation, competing-risks model, and Von Foerster equation are used to prioritize control-measure efforts. Here we formulate how to integrate those three different types of functional relationship to construct easy-to-use and easy-to-interpret critical-control lines that help determine optimally the intervention strategies for containing airborne infections. We show that a combination of assigned effective public health interventions and enhanced engineering control measures would have a high probability for containing airborne infection. We suggest that integrated analysis to enhance modelling the impact of potential control measures against airborne infections presents an opportunity to assess risks and benefits. We demonstrate the approach with examples of optimal control measures to prioritize respiratory infections of severe acute respiratory syndrome (SARS), influenza, measles, and chickenpox.</p>
</div>
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<pmc article-type="research-article" xml:lang="EN"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
  <front><journal-meta><journal-id journal-id-type="nlm-ta">Epidemiol Infect</journal-id>
<journal-id journal-id-type="publisher-id">HYG</journal-id>
<journal-title>Epidemiology and Infection</journal-title>
<issn pub-type="ppub">0950-2688</issn>
<issn pub-type="epub">1469-4409</issn>
<publisher><publisher-name>Cambridge University Press</publisher-name>
<publisher-loc>Cambridge, UK</publisher-loc>
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<article-meta><article-id pub-id-type="pmid">17475088</article-id>
<article-id pub-id-type="pmc">2870817</article-id>
<article-id pub-id-type="doi">10.1017/S0950268807008631</article-id>
<article-id pub-id-type="pii">S0950268807008631</article-id>
<article-id pub-id-type="publisher-id">00863</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Original Papers</subject>
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</article-categories>
<title-group><article-title>Modelling respiratory infection control measure effects</article-title>
<alt-title alt-title-type="left-running">C. M. Liao, S. C. Chen and C. F. Chang</alt-title>
<alt-title alt-title-type="right-running">Modelling respiratory infection control measures</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>LIAO</surname>
<given-names>C. M.</given-names>
</name>
<xref ref-type="aff" rid="aff001"></xref>
<xref ref-type="corresp" rid="cor001">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>CHEN</surname>
<given-names>S. C.</given-names>
</name>
<xref ref-type="aff" rid="aff001"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>CHANG</surname>
<given-names>C. F.</given-names>
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<xref ref-type="aff" rid="aff001"></xref>
</contrib>
</contrib-group>
<aff id="aff001">Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan 10617, ROC</aff>
<author-notes><corresp id="cor001"><label>*</label>
Author for corresponding: Dr Chung-Min Liao, Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan 10617, ROC. (Email: <email xlink:href="cmliao@ntu.edu.tw">cmliao@ntu.edu.tw</email>
)</corresp>
</author-notes>
<pub-date pub-type="ppub"><month>3</month>
<year>2008</year>
</pub-date>
<pub-date pub-type="epub"><day>03</day>
<month>5</month>
<year>2007</year>
</pub-date>
<volume>136</volume>
<issue>3</issue>
<fpage>299</fpage>
<lpage>308</lpage>
<history><date date-type="accepted"><day>30</day>
<month>3</month>
<year>2007</year>
</date>
</history>
<copyright-statement>© Cambridge University Press 2007</copyright-statement>
<copyright-year>2007</copyright-year>
<self-uri xlink:title="pdf" xlink:type="simple" xlink:href="S0950268807008631a.pdf"></self-uri>
<abstract><title>SUMMARY</title>
<p>One of the most pressing issues in facing emerging and re-emerging respiratory infections is how to bring them under control with current public health measures. Approaches such as the Wells–Riley equation, competing-risks model, and Von Foerster equation are used to prioritize control-measure efforts. Here we formulate how to integrate those three different types of functional relationship to construct easy-to-use and easy-to-interpret critical-control lines that help determine optimally the intervention strategies for containing airborne infections. We show that a combination of assigned effective public health interventions and enhanced engineering control measures would have a high probability for containing airborne infection. We suggest that integrated analysis to enhance modelling the impact of potential control measures against airborne infections presents an opportunity to assess risks and benefits. We demonstrate the approach with examples of optimal control measures to prioritize respiratory infections of severe acute respiratory syndrome (SARS), influenza, measles, and chickenpox.</p>
</abstract>
<counts><page-count count="10"></page-count>
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</article-meta>
</front>
</pmc>
</record>

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Modelling respiratory infection control measure effects

Modelling respiratory infection control measure effects

Source :

Abstract

Links to Exploration step

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