Modeling uncertainties in workforce disruptions from influenza pandemics using dynamic input-output analysis.
Identifieur interne : 001725 ( Ncbi/Merge ); précédent : 001724; suivant : 001726Modeling uncertainties in workforce disruptions from influenza pandemics using dynamic input-output analysis.
Auteurs : Amine El Haimar [États-Unis] ; Joost R. SantosSource :
- Risk analysis : an official publication of the Society for Risk Analysis [ 1539-6924 ] ; 2014.
Descripteurs français
- KwdFr :
- MESH :
- isolement et purification : Sous-type H1N1 du virus de la grippe A.
- virologie : Grippe humaine.
- épidémiologie : Grippe humaine.
- Emploi, Flambées de maladies, Humains, Incertitude, Modèles théoriques.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Influenza, Human.
- isolation & purification : Influenza A Virus, H1N1 Subtype.
- statistics & numerical data : Employment.
- virology : Influenza, Human.
- Disease Outbreaks, Humans, Models, Theoretical, Uncertainty.
Abstract
Influenza pandemic is a serious disaster that can pose significant disruptions to the workforce and associated economic sectors. This article examines the impact of influenza pandemic on workforce availability within an interdependent set of economic sectors. We introduce a simulation model based on the dynamic input-output model to capture the propagation of pandemic consequences through the National Capital Region (NCR). The analysis conducted in this article is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on the economic sectors: (i) inoperability, which measures the percentage gap between the as-planned output and the actual output of a sector, and (ii) economic loss, which quantifies the associated monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the critical economic sectors. Results show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and health-care providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant total production outputs in the NCR such as federal government agencies. Therefore, policy recommendations relating to potential mitigation and recovery strategies should take into account the balance between the inoperability and economic loss metrics.
DOI: 10.1111/risa.12113
PubMed: 24033717
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Santos</name></author></analytic><series><title level="j">Risk analysis : an official publication of the Society for Risk Analysis</title><idno type="eISSN">1539-6924</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Disease Outbreaks</term><term>Employment (statistics & numerical data)</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype (isolation & purification)</term><term>Influenza, Human (epidemiology)</term><term>Influenza, Human (virology)</term><term>Models, Theoretical</term><term>Uncertainty</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Emploi ()</term><term>Flambées de maladies</term><term>Grippe humaine (virologie)</term><term>Grippe humaine (épidémiologie)</term><term>Humains</term><term>Incertitude</term><term>Modèles théoriques</term><term>Sous-type H1N1 du virus de la grippe A (isolement et purification)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Influenza A Virus, H1N1 Subtype</term></keywords><keywords scheme="MESH" qualifier="isolement et purification" xml:lang="fr"><term>Sous-type H1N1 du virus de la grippe A</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Employment</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Disease Outbreaks</term><term>Humans</term><term>Models, Theoretical</term><term>Uncertainty</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Emploi</term><term>Flambées de maladies</term><term>Humains</term><term>Incertitude</term><term>Modèles théoriques</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Influenza pandemic is a serious disaster that can pose significant disruptions to the workforce and associated economic sectors. This article examines the impact of influenza pandemic on workforce availability within an interdependent set of economic sectors. We introduce a simulation model based on the dynamic input-output model to capture the propagation of pandemic consequences through the National Capital Region (NCR). The analysis conducted in this article is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on the economic sectors: (i) inoperability, which measures the percentage gap between the as-planned output and the actual output of a sector, and (ii) economic loss, which quantifies the associated monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the critical economic sectors. Results show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and health-care providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant total production outputs in the NCR such as federal government agencies. Therefore, policy recommendations relating to potential mitigation and recovery strategies should take into account the balance between the inoperability and economic loss metrics. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24033717</PMID><DateCompleted><Year>2014</Year><Month>11</Month><Day>24</Day></DateCompleted><DateRevised><Year>2014</Year><Month>03</Month><Day>24</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1539-6924</ISSN><JournalIssue CitedMedium="Internet"><Volume>34</Volume><Issue>3</Issue><PubDate><Year>2014</Year><Month>Mar</Month></PubDate></JournalIssue><Title>Risk analysis : an official publication of the Society for Risk Analysis</Title><ISOAbbreviation>Risk Anal.</ISOAbbreviation></Journal><ArticleTitle>Modeling uncertainties in workforce disruptions from influenza pandemics using dynamic input-output analysis.</ArticleTitle><Pagination><MedlinePgn>401-15</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1111/risa.12113</ELocationID><Abstract><AbstractText>Influenza pandemic is a serious disaster that can pose significant disruptions to the workforce and associated economic sectors. This article examines the impact of influenza pandemic on workforce availability within an interdependent set of economic sectors. We introduce a simulation model based on the dynamic input-output model to capture the propagation of pandemic consequences through the National Capital Region (NCR). The analysis conducted in this article is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on the economic sectors: (i) inoperability, which measures the percentage gap between the as-planned output and the actual output of a sector, and (ii) economic loss, which quantifies the associated monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the critical economic sectors. Results show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and health-care providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant total production outputs in the NCR such as federal government agencies. Therefore, policy recommendations relating to potential mitigation and recovery strategies should take into account the balance between the inoperability and economic loss metrics. </AbstractText><CopyrightInformation>© 2013 Society for Risk Analysis.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>El Haimar</LastName><ForeName>Amine</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Engineering Management and Systems Engineering, The George Washington University, Washington, DC, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Santos</LastName><ForeName>Joost R</ForeName><Initials>JR</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>09</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Risk Anal</MedlineTA><NlmUniqueID>8109978</NlmUniqueID><ISSNLinking>0272-4332</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="Y">Disease Outbreaks</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004651" MajorTopicYN="N">Employment</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></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="N">isolation & purification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="N">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D035501" MajorTopicYN="Y">Uncertainty</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Disaster preparedness</Keyword><Keyword MajorTopicYN="N">FluWorkLoss</Keyword><Keyword MajorTopicYN="N">input-output analysis</Keyword><Keyword MajorTopicYN="N">pandemics</Keyword><Keyword MajorTopicYN="N">uncertainty modeling</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2013</Year><Month>9</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2013</Year><Month>9</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>12</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">24033717</ArticleId><ArticleId IdType="doi">10.1111/risa.12113</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>District de Columbia</li></region></list><tree><noCountry><name sortKey="Santos, Joost R" sort="Santos, Joost R" uniqKey="Santos J" first="Joost R" last="Santos">Joost R. Santos</name></noCountry><country name="États-Unis"><region name="District de Columbia"><name sortKey="El Haimar, Amine" sort="El Haimar, Amine" uniqKey="El Haimar A" first="Amine" last="El Haimar">Amine El Haimar</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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