Measuring the potential of individual airports for pandemic spread over the world airline network.
Identifieur interne : 000561 ( PubMed/Curation ); précédent : 000560; suivant : 000562Measuring the potential of individual airports for pandemic spread over the world airline network.
Auteurs : Glenn Lawyer [Allemagne]Source :
- BMC infectious diseases [ 1471-2334 ] ; 2016.
Descripteurs français
- KwdFr :
- MESH :
- normes : Aéroports.
- épidémiologie : Grippe humaine.
- Aéroports, Facteurs de risque, Flambées de maladies, Humains, Modèles théoriques, Mâle, Pandémies, Simulation numérique, Transmission de maladie infectieuse, Voyage aérien.
English descriptors
- KwdEn :
- Air Travel (statistics & numerical data), Airports (standards), Airports (statistics & numerical data), Computer Simulation, Disease Outbreaks (statistics & numerical data), Disease Transmission, Infectious (statistics & numerical data), Humans, Influenza, Human (epidemiology), Male, Models, Theoretical, Outcome Assessment, Health Care, Pandemics (statistics & numerical data), Risk Factors.
- MESH :
- epidemiology : Influenza, Human.
- standards : Airports.
- statistics & numerical data : Air Travel, Airports, Disease Outbreaks, Disease Transmission, Infectious, Pandemics.
- Computer Simulation, Humans, Male, Models, Theoretical, Outcome Assessment, Health Care, Risk Factors.
Abstract
Massive growth in human mobility has dramatically increased the risk and rate of pandemic spread. Macro-level descriptors of the topology of the World Airline Network (WAN) explains middle and late stage dynamics of pandemic spread mediated by this network, but necessarily regard early stage variation as stochastic. We propose that much of this early stage variation can be explained by appropriately characterizing the local network topology surrounding an outbreak's debut location.
DOI: 10.1186/s12879-016-1350-4
PubMed: 26861206
Links toward previous steps (curation, corpus...)
- to stream PubMed, to step Corpus: Pour aller vers cette notice dans l'étape Curation :000561
Links to Exploration step
pubmed:26861206Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Measuring the potential of individual airports for pandemic spread over the world airline network.</title>
<author><name sortKey="Lawyer, Glenn" sort="Lawyer, Glenn" uniqKey="Lawyer G" first="Glenn" last="Lawyer">Glenn Lawyer</name>
<affiliation wicri:level="1"><nlm:affiliation>Department of Computational Biology, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, Germany. lawyer@mpi-inf.mpg.de.</nlm:affiliation>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Department of Computational Biology, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken</wicri:regionArea>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2016">2016</date>
<idno type="RBID">pubmed:26861206</idno>
<idno type="pmid">26861206</idno>
<idno type="doi">10.1186/s12879-016-1350-4</idno>
<idno type="wicri:Area/PubMed/Corpus">000561</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000561</idno>
<idno type="wicri:Area/PubMed/Curation">000561</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000561</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">Measuring the potential of individual airports for pandemic spread over the world airline network.</title>
<author><name sortKey="Lawyer, Glenn" sort="Lawyer, Glenn" uniqKey="Lawyer G" first="Glenn" last="Lawyer">Glenn Lawyer</name>
<affiliation wicri:level="1"><nlm:affiliation>Department of Computational Biology, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, Germany. lawyer@mpi-inf.mpg.de.</nlm:affiliation>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Department of Computational Biology, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken</wicri:regionArea>
</affiliation>
</author>
</analytic>
<series><title level="j">BMC infectious diseases</title>
<idno type="eISSN">1471-2334</idno>
<imprint><date when="2016" type="published">2016</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Air Travel (statistics & numerical data)</term>
<term>Airports (standards)</term>
<term>Airports (statistics & numerical data)</term>
<term>Computer Simulation</term>
<term>Disease Outbreaks (statistics & numerical data)</term>
<term>Disease Transmission, Infectious (statistics & numerical data)</term>
<term>Humans</term>
<term>Influenza, Human (epidemiology)</term>
<term>Male</term>
<term>Models, Theoretical</term>
<term>Outcome Assessment, Health Care</term>
<term>Pandemics (statistics & numerical data)</term>
<term>Risk Factors</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Aéroports ()</term>
<term>Aéroports (normes)</term>
<term>Facteurs de risque</term>
<term>Flambées de maladies ()</term>
<term>Grippe humaine (épidémiologie)</term>
<term>Humains</term>
<term>Modèles théoriques</term>
<term>Mâle</term>
<term>Pandémies ()</term>
<term>Simulation numérique</term>
<term>Transmission de maladie infectieuse ()</term>
<term>Voyage aérien ()</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term>
</keywords>
<keywords scheme="MESH" qualifier="normes" xml:lang="fr"><term>Aéroports</term>
</keywords>
<keywords scheme="MESH" qualifier="standards" xml:lang="en"><term>Airports</term>
</keywords>
<keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Air Travel</term>
<term>Airports</term>
<term>Disease Outbreaks</term>
<term>Disease Transmission, Infectious</term>
<term>Pandemics</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Computer Simulation</term>
<term>Humans</term>
<term>Male</term>
<term>Models, Theoretical</term>
<term>Outcome Assessment, Health Care</term>
<term>Risk Factors</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Aéroports</term>
<term>Facteurs de risque</term>
<term>Flambées de maladies</term>
<term>Humains</term>
<term>Modèles théoriques</term>
<term>Mâle</term>
<term>Pandémies</term>
<term>Simulation numérique</term>
<term>Transmission de maladie infectieuse</term>
<term>Voyage aérien</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Massive growth in human mobility has dramatically increased the risk and rate of pandemic spread. Macro-level descriptors of the topology of the World Airline Network (WAN) explains middle and late stage dynamics of pandemic spread mediated by this network, but necessarily regard early stage variation as stochastic. We propose that much of this early stage variation can be explained by appropriately characterizing the local network topology surrounding an outbreak's debut location.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">26861206</PMID>
<DateCompleted><Year>2016</Year>
<Month>09</Month>
<Day>30</Day>
</DateCompleted>
<DateRevised><Year>2019</Year>
<Month>12</Month>
<Day>10</Day>
</DateRevised>
<Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2334</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>16</Volume>
<PubDate><Year>2016</Year>
<Month>Feb</Month>
<Day>09</Day>
</PubDate>
</JournalIssue>
<Title>BMC infectious diseases</Title>
<ISOAbbreviation>BMC Infect. Dis.</ISOAbbreviation>
</Journal>
<ArticleTitle>Measuring the potential of individual airports for pandemic spread over the world airline network.</ArticleTitle>
<Pagination><MedlinePgn>70</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1186/s12879-016-1350-4</ELocationID>
<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Massive growth in human mobility has dramatically increased the risk and rate of pandemic spread. Macro-level descriptors of the topology of the World Airline Network (WAN) explains middle and late stage dynamics of pandemic spread mediated by this network, but necessarily regard early stage variation as stochastic. We propose that much of this early stage variation can be explained by appropriately characterizing the local network topology surrounding an outbreak's debut location.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">Based on a model of the WAN derived from public data, we measure for each airport the expected force of infection (AEF) which a pandemic originating at that airport would generate, assuming an epidemic process which transmits from airport to airport via scheduled commercial flights. We observe, for a subset of world airports, the minimum transmission rate at which a disease becomes pandemically competent at each airport. We also observe, for a larger subset, the time until a pandemically competent outbreak achieves pandemic status given its debut location. Observations are generated using a highly sophisticated metapopulation reaction-diffusion simulator under a disease model known to well replicate the 2009 influenza pandemic. The robustness of the AEF measure to model misspecification is examined by degrading the underlying model WAN.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">AEF powerfully explains pandemic risk, showing correlation of 0.90 to the transmission level needed to give a disease pandemic competence, and correlation of 0.85 to the delay until an outbreak becomes a pandemic. The AEF is robust to model misspecification. For 97 % of airports, removing 15 % of airports from the model changes their AEF metric by less than 1 %.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Appropriately summarizing the size, shape, and diversity of an airport's local neighborhood in the WAN accurately explains much of the macro-level stochasticity in pandemic outcomes.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Lawyer</LastName>
<ForeName>Glenn</ForeName>
<Initials>G</Initials>
<AffiliationInfo><Affiliation>Department of Computational Biology, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, Germany. lawyer@mpi-inf.mpg.de.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2016</Year>
<Month>02</Month>
<Day>09</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>England</Country>
<MedlineTA>BMC Infect Dis</MedlineTA>
<NlmUniqueID>100968551</NlmUniqueID>
<ISSNLinking>1471-2334</ISSNLinking>
</MedlineJournalInfo>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D064870" MajorTopicYN="Y">Air Travel</DescriptorName>
<QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D059028" MajorTopicYN="Y">Airports</DescriptorName>
<QualifierName UI="Q000592" MajorTopicYN="N">standards</QualifierName>
<QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D004196" MajorTopicYN="N">Disease Outbreaks</DescriptorName>
<QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D018562" MajorTopicYN="Y">Disease Transmission, Infectious</DescriptorName>
<QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName>
<QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008962" MajorTopicYN="Y">Models, Theoretical</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D017063" MajorTopicYN="N">Outcome Assessment, Health Care</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D058873" MajorTopicYN="Y">Pandemics</DescriptorName>
<QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012307" MajorTopicYN="N">Risk Factors</DescriptorName>
</MeshHeading>
</MeshHeadingList>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year>
<Month>05</Month>
<Day>04</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2016</Year>
<Month>01</Month>
<Day>13</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2016</Year>
<Month>2</Month>
<Day>11</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2016</Year>
<Month>2</Month>
<Day>11</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2016</Year>
<Month>10</Month>
<Day>1</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>epublish</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">26861206</ArticleId>
<ArticleId IdType="doi">10.1186/s12879-016-1350-4</ArticleId>
<ArticleId IdType="pii">10.1186/s12879-016-1350-4</ArticleId>
<ArticleId IdType="pmc">PMC4746766</ArticleId>
</ArticleIdList>
<ReferenceList><Reference><Citation>PLoS One. 2012;7(7):e40961</Citation>
<ArticleIdList><ArticleId IdType="pubmed">22829902</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Am J Trop Med Hyg. 2012 Feb;86(2):349-58</Citation>
<ArticleIdList><ArticleId IdType="pubmed">22302873</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Sci Rep. 2016;6:18847</Citation>
<ArticleIdList><ArticleId IdType="pubmed">26754565</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Proc Natl Acad Sci U S A. 2014 Nov 11;111(45):15888-93</Citation>
<ArticleIdList><ArticleId IdType="pubmed">25349388</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Malar J. 2013;12:269</Citation>
<ArticleIdList><ArticleId IdType="pubmed">23914776</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Med. 2009;7:45</Citation>
<ArticleIdList><ArticleId IdType="pubmed">19744314</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Sci Rep. 2014 Jul 09;4:5638</Citation>
<ArticleIdList><ArticleId IdType="pubmed">25005934</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Adv Parasitol. 2006;62:293-343</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16647974</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Jul;84(1 Pt 2):016105</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21867255</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Proc Natl Acad Sci U S A. 2006 Feb 14;103(7):2015-20</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16461461</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Math Biol. 2008 Mar;56(3):293-310</Citation>
<ArticleIdList><ArticleId IdType="pubmed">17668212</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>PLoS Negl Trop Dis. 2014 Dec;8(12):e3278</Citation>
<ArticleIdList><ArticleId IdType="pubmed">25474491</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Med. 2012;10:165</Citation>
<ArticleIdList><ArticleId IdType="pubmed">23237460</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Epidemiology. 2005 Nov;16(6):791-801</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16222170</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Infect Dis. 2011;11:37</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21288355</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Science. 2013 Dec 13;342(6164):1337-42</Citation>
<ArticleIdList><ArticleId IdType="pubmed">24337289</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>PLoS One. 2014;9(8):e104915</Citation>
<ArticleIdList><ArticleId IdType="pubmed">25111394</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Nov;90(5-1):052808</Citation>
<ArticleIdList><ArticleId IdType="pubmed">25493835</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Proc Natl Acad Sci U S A. 2005 May 31;102(22):7794-9</Citation>
<ArticleIdList><ArticleId IdType="pubmed">15911778</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>PLoS Comput Biol. 2014 Jul;10(7):e1003716</Citation>
<ArticleIdList><ArticleId IdType="pubmed">25010676</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Sci Rep. 2015;5:8665</Citation>
<ArticleIdList><ArticleId IdType="pubmed">25727453</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J R Soc Interface. 2005 Sep 22;2(4):281-93</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16849186</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Epidemiol Infect. 2011 Jan;139(1):68-79</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20546633</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Phys Rev E Stat Nonlin Soft Matter Phys. 2002 Jul;66(1 Pt 2):016128</Citation>
<ArticleIdList><ArticleId IdType="pubmed">12241447</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Infect Dis. 2010;10:190</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20587041</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nature. 2008 Feb 21;451(7181):990-3</Citation>
<ArticleIdList><ArticleId IdType="pubmed">18288193</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Infect Dis. 2013;13:185</Citation>
<ArticleIdList><ArticleId IdType="pubmed">23618005</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Int Health. 2014 Mar;6(1):5-11</Citation>
<ArticleIdList><ArticleId IdType="pubmed">24480992</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
</PubmedData>
</pubmed>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/PandemieGrippaleV1/Data/PubMed/Curation
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000561 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PubMed/Curation/biblio.hfd -nk 000561 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Sante |area= PandemieGrippaleV1 |flux= PubMed |étape= Curation |type= RBID |clé= pubmed:26861206 |texte= Measuring the potential of individual airports for pandemic spread over the world airline network. }}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Curation/RBID.i -Sk "pubmed:26861206" \ | HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Curation/biblio.hfd \ | NlmPubMed2Wicri -a PandemieGrippaleV1
![]() | This area was generated with Dilib version V0.6.34. | ![]() |