Modelling during an emergency: the 2009 H1N1 influenza pandemic.
Identifieur interne : 000A00 ( PubMed/Corpus ); précédent : 000999; suivant : 000A01Modelling during an emergency: the 2009 H1N1 influenza pandemic.
Auteurs : B Y Lee ; L A Haidari ; M S LeeSource :
- Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases [ 1469-0691 ] ; 2013.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Influenza, Human.
- isolation & purification : Influenza A Virus, H1N1 Subtype.
- transmission : Influenza, Human.
- virology : Influenza, Human.
- Decision Support Techniques, Epidemiological Monitoring, Humans, Models, Theoretical, Pandemics.
Abstract
During the 2009 H1N1 pandemic, decision-makers had access to mathematical and computational models that were not available in previous pandemics in 1918, 1957, and 1968. How did models contribute to policy and action during the 2009 H1N1 pandemic? Modelling encountered six primary challenges: (i) expectations of modelling were not clearly defined; (ii) appropriate real-time data were not readily available; (iii) modelling results were not generated, shared, or disseminated in time; (iv) decision-makers could not always decipher the structure and assumptions of the models; (v) modelling studies varied in intervention representations and reported results; and (vi) modelling studies did not always present the results or outcomes that are useful to decision-makers. However, there were also seven general successes: (i) modelling characterized the role of social distancing measures such as school closure; (ii) modelling helped to guide data collection; (iii) modelling helped to justify the value of the vaccination programme; (iv) modelling helped to prioritize target populations for vaccination; (v) modelling addressed the use of antiviral medications; (vi) modelling helped with health system preparedness planning; and (vii) modellers and decision-makers gained a better understanding of how to work with each other. In many ways, the 2009 pandemic served as practice and a learning opportunity for both modellers and decision-makers. Modellers can continue working with decision-makers and other stakeholders to help overcome these challenges, to be better prepared when the next emergency inevitably arrives.
DOI: 10.1111/1469-0691.12284
PubMed: 23800220
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pubmed:23800220Le document en format XML
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How did models contribute to policy and action during the 2009 H1N1 pandemic? Modelling encountered six primary challenges: (i) expectations of modelling were not clearly defined; (ii) appropriate real-time data were not readily available; (iii) modelling results were not generated, shared, or disseminated in time; (iv) decision-makers could not always decipher the structure and assumptions of the models; (v) modelling studies varied in intervention representations and reported results; and (vi) modelling studies did not always present the results or outcomes that are useful to decision-makers. However, there were also seven general successes: (i) modelling characterized the role of social distancing measures such as school closure; (ii) modelling helped to guide data collection; (iii) modelling helped to justify the value of the vaccination programme; (iv) modelling helped to prioritize target populations for vaccination; (v) modelling addressed the use of antiviral medications; (vi) modelling helped with health system preparedness planning; and (vii) modellers and decision-makers gained a better understanding of how to work with each other. In many ways, the 2009 pandemic served as practice and a learning opportunity for both modellers and decision-makers. Modellers can continue working with decision-makers and other stakeholders to help overcome these challenges, to be better prepared when the next emergency inevitably arrives. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">23800220</PMID><DateCompleted><Year>2014</Year><Month>05</Month><Day>06</Day></DateCompleted><DateRevised><Year>2013</Year><Month>10</Month><Day>14</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1469-0691</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>11</Issue><PubDate><Year>2013</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases</Title><ISOAbbreviation>Clin. Microbiol. 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Modelling encountered six primary challenges: (i) expectations of modelling were not clearly defined; (ii) appropriate real-time data were not readily available; (iii) modelling results were not generated, shared, or disseminated in time; (iv) decision-makers could not always decipher the structure and assumptions of the models; (v) modelling studies varied in intervention representations and reported results; and (vi) modelling studies did not always present the results or outcomes that are useful to decision-makers. However, there were also seven general successes: (i) modelling characterized the role of social distancing measures such as school closure; (ii) modelling helped to guide data collection; (iii) modelling helped to justify the value of the vaccination programme; (iv) modelling helped to prioritize target populations for vaccination; (v) modelling addressed the use of antiviral medications; (vi) modelling helped with health system preparedness planning; and (vii) modellers and decision-makers gained a better understanding of how to work with each other. In many ways, the 2009 pandemic served as practice and a learning opportunity for both modellers and decision-makers. Modellers can continue working with decision-makers and other stakeholders to help overcome these challenges, to be better prepared when the next emergency inevitably arrives. </AbstractText><CopyrightInformation>© 2013 The Authors Clinical Microbiology and Infection © 2013 European Society of Clinical Microbiology and Infectious Diseases.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Lee</LastName><ForeName>B Y</ForeName><Initials>BY</Initials><AffiliationInfo><Affiliation>Public Health Computational and Operational Research (PHICOR) Group, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Haidari</LastName><ForeName>L A</ForeName><Initials>LA</Initials></Author><Author ValidYN="Y"><LastName>Lee</LastName><ForeName>M S</ForeName><Initials>MS</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D016454">Review</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>06</Month><Day>25</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Clin Microbiol Infect</MedlineTA><NlmUniqueID>9516420</NlmUniqueID><ISSNLinking>1198-743X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D003661" MajorTopicYN="Y">Decision Support Techniques</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D062665" MajorTopicYN="Y">Epidemiological Monitoring</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D053118" MajorTopicYN="N">Influenza A Virus, H1N1 Subtype</DescriptorName><QualifierName UI="Q000302" MajorTopicYN="Y">isolation & purification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000635" MajorTopicYN="Y">transmission</QualifierName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="Y">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="Y">Pandemics</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Emergency</Keyword><Keyword MajorTopicYN="N">influenza</Keyword><Keyword MajorTopicYN="N">modelling</Keyword><Keyword MajorTopicYN="N">pandemic</Keyword><Keyword MajorTopicYN="N">policy-making</Keyword><Keyword MajorTopicYN="N">simulation</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2013</Year><Month>6</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2013</Year><Month>6</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>5</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">23800220</ArticleId><ArticleId IdType="doi">10.1111/1469-0691.12284</ArticleId><ArticleId IdType="pii">S1198-743X(14)63003-2</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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