Measuring the potential of individual airports for pandemic spread over the world airline network
Identifieur interne : 000188 ( Pmc/Curation ); précédent : 000187; suivant : 000189Measuring the potential of individual airports for pandemic spread over the world airline network
Auteurs : Glenn Lawyer [Allemagne]Source :
- BMC Infectious Diseases [ 1471-2334 ] ; 2016.
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.
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.
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 %.
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.
The online version of this article (doi:10.1186/s12879-016-1350-4) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12879-016-1350-4
PubMed: 26861206
PubMed Central: 4746766
Links toward previous steps (curation, corpus...)
- to stream Pmc, to step Corpus: Pour aller vers cette notice dans l'étape Curation :000188
Links to Exploration step
PMC:4746766Le 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:aff id="Aff1">Department of Computational Biology, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, Germany</nlm:aff><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">PMC</idno><idno type="pmid">26861206</idno><idno type="pmc">4746766</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4746766</idno><idno type="RBID">PMC:4746766</idno><idno type="doi">10.1186/s12879-016-1350-4</idno><date when="2016">2016</date><idno type="wicri:Area/Pmc/Corpus">000188</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000188</idno><idno type="wicri:Area/Pmc/Curation">000188</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000188</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">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:aff id="Aff1">Department of Computational Biology, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, Germany</nlm:aff><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">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>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.</p></sec><sec><title>Methods</title><p>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.</p></sec><sec><title>Results</title><p>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 %.</p></sec><sec><title>Conclusions</title><p>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.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12879-016-1350-4) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Tatem, Aj" uniqKey="Tatem A">AJ Tatem</name></author><author><name sortKey="Rogers, Dj" uniqKey="Rogers D">DJ Rogers</name></author><author><name sortKey="Hay, Si" uniqKey="Hay S">SI Hay</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tatem, Aj" uniqKey="Tatem A">AJ Tatem</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jones, Ke" uniqKey="Jones K">KE Jones</name></author><author><name sortKey="Patel, Ng" uniqKey="Patel N">NG Patel</name></author><author><name sortKey="Levy, Ma" uniqKey="Levy M">MA Levy</name></author><author><name sortKey="Storeygard, A" uniqKey="Storeygard A">A Storeygard</name></author><author><name sortKey="Balk, D" uniqKey="Balk D">D Balk</name></author><author><name sortKey="Gittleman, Jl" uniqKey="Gittleman J">JL Gittleman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Brockmann, D" uniqKey="Brockmann D">D Brockmann</name></author><author><name sortKey="Helbing, D" uniqKey="Helbing D">D Helbing</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Johansson, Ma" uniqKey="Johansson M">MA Johansson</name></author><author><name sortKey="Powers, Am" uniqKey="Powers A">AM Powers</name></author><author><name sortKey="Pesik, N" uniqKey="Pesik N">N Pesik</name></author><author><name sortKey="Cohen, Nj" uniqKey="Cohen N">NJ Cohen</name></author><author><name sortKey="Staples, Je" uniqKey="Staples J">JE Staples</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Colizza, V" uniqKey="Colizza V">V Colizza</name></author><author><name sortKey="Barrat, A" uniqKey="Barrat A">A Barrat</name></author><author><name sortKey="Barthelemy, M" uniqKey="Barthelemy M">M Barthélemy</name></author><author><name sortKey="Vespignani, A" uniqKey="Vespignani A">A Vespignani</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Colizza, V" uniqKey="Colizza V">V Colizza</name></author><author><name sortKey="Pastor Satorras, R" uniqKey="Pastor Satorras R">R Pastor-Satorras</name></author><author><name sortKey="Vespignani, A" uniqKey="Vespignani A">A Vespignani</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Balcan, D" uniqKey="Balcan D">D Balcan</name></author><author><name sortKey="Hu, H" uniqKey="Hu H">H Hu</name></author><author><name sortKey="Goncalves, B" uniqKey="Goncalves B">B Goncalves</name></author><author><name sortKey="Bajardi, P" uniqKey="Bajardi P">P Bajardi</name></author><author><name sortKey="Poletto, C" uniqKey="Poletto C">C Poletto</name></author><author><name sortKey="Ramasco, Jj" uniqKey="Ramasco J">JJ Ramasco</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Huang, Z" uniqKey="Huang Z">Z Huang</name></author><author><name sortKey="Tatem, Aj" uniqKey="Tatem A">AJ Tatem</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Semenza, Jc" uniqKey="Semenza J">JC Semenza</name></author><author><name sortKey="Sudre, B" uniqKey="Sudre B">B Sudre</name></author><author><name sortKey="Miniota, J" uniqKey="Miniota J">J Miniota</name></author><author><name sortKey="Rossi, M" uniqKey="Rossi M">M Rossi</name></author><author><name sortKey="Hu, W" uniqKey="Hu W">W Hu</name></author><author><name sortKey="Kossowsky, D" uniqKey="Kossowsky D">D Kossowsky</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Heffernan, Jm" uniqKey="Heffernan J">JM Heffernan</name></author><author><name sortKey="Smith, Rj" uniqKey="Smith R">RJ Smith</name></author><author><name sortKey="Wahl, Lm" uniqKey="Wahl L">LM Wahl</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Newman, Mej" uniqKey="Newman M">MEJ Newman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Volz, E" uniqKey="Volz E">E Volz</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Barrat, A" uniqKey="Barrat A">A Barrat</name></author><author><name sortKey="Barthelemy, M" uniqKey="Barthelemy M">M Barthélemy</name></author><author><name sortKey="Vespignani, A" uniqKey="Vespignani A">A Vespignani</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Verma, T" uniqKey="Verma T">T Verma</name></author><author><name sortKey="Araujo, Nam" uniqKey="Araujo N">NAM Araújo</name></author><author><name sortKey="Herrmann, Hj" uniqKey="Herrmann H">HJ Herrmann</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Guimera, R" uniqKey="Guimera R">R Guimerà</name></author><author><name sortKey="Mossa, S" uniqKey="Mossa S">S Mossa</name></author><author><name sortKey="Turtschi, A" uniqKey="Turtschi A">A Turtschi</name></author><author><name sortKey="Amaral, Lan" uniqKey="Amaral L">LAN Amaral</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pan, Rk" uniqKey="Pan R">RK Pan</name></author><author><name sortKey="Saram Ki, J" uniqKey="Saram Ki J">J Saramäki</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bauch, Ct" uniqKey="Bauch C">CT Bauch</name></author><author><name sortKey="Lloyd Smith, Jo" uniqKey="Lloyd Smith J">JO Lloyd-Smith</name></author><author><name sortKey="Coffee, Mp" uniqKey="Coffee M">MP Coffee</name></author><author><name sortKey="Galvani, Ap" uniqKey="Galvani A">AP Galvani</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tizzoni, M" uniqKey="Tizzoni M">M Tizzoni</name></author><author><name sortKey="Bajardi, P" uniqKey="Bajardi P">P Bajardi</name></author><author><name sortKey="Poletto, C" uniqKey="Poletto C">C Poletto</name></author><author><name sortKey="Ramasco, Jj" uniqKey="Ramasco J">JJ Ramasco</name></author><author><name sortKey="Balcan, D" uniqKey="Balcan D">D Balcan</name></author><author><name sortKey="Goncalves, B" uniqKey="Goncalves B">B Gonçalves</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Den Broeck, Wv" uniqKey="Den Broeck W">WV den Broeck</name></author><author><name sortKey="Gioannini, C" uniqKey="Gioannini C">C Gioannini</name></author><author><name sortKey="Goncalves, B" uniqKey="Goncalves B">B Gonçalves</name></author><author><name sortKey="Quaggiotto, M" uniqKey="Quaggiotto M">M Quaggiotto</name></author><author><name sortKey="Colizza, V" uniqKey="Colizza V">V Colizza</name></author><author><name sortKey="Vespignani, A" uniqKey="Vespignani A">A Vespignani</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ajelli, M" uniqKey="Ajelli M">M Ajelli</name></author><author><name sortKey="Merler, S" uniqKey="Merler S">S Merler</name></author><author><name sortKey="Pugliese, A" uniqKey="Pugliese A">A Pugliese</name></author><author><name sortKey="Rizzo, C" uniqKey="Rizzo C">C Rizzo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ajelli, M" uniqKey="Ajelli M">M Ajelli</name></author><author><name sortKey="Goncalves, B" uniqKey="Goncalves B">B Gonçalves</name></author><author><name sortKey="Balcan, D" uniqKey="Balcan D">D Balcan</name></author><author><name sortKey="Colizza, V" uniqKey="Colizza V">V Colizza</name></author><author><name sortKey="Hu, H" uniqKey="Hu H">H Hu</name></author><author><name sortKey="Ramasco, Jj" uniqKey="Ramasco J">JJ Ramasco</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Johansson, Ma" uniqKey="Johansson M">MA Johansson</name></author><author><name sortKey="Arana Vizcarrondo, N" uniqKey="Arana Vizcarrondo N">N Arana-Vizcarrondo</name></author><author><name sortKey="Biggerstaff, Bj" uniqKey="Biggerstaff B">BJ Biggerstaff</name></author><author><name sortKey="Gallagher, N" uniqKey="Gallagher N">N Gallagher</name></author><author><name sortKey="Marano, N" uniqKey="Marano N">N Marano</name></author><author><name sortKey="Staples, Je" uniqKey="Staples J">JE Staples</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lawyer, G" uniqKey="Lawyer G">G Lawyer</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Borgatti, Sp" uniqKey="Borgatti S">SP Borgatti</name></author><author><name sortKey="Everett, Mg" uniqKey="Everett M">MG Everett</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Martin, T" uniqKey="Martin T">T Martin</name></author><author><name sortKey="Zhang, X" uniqKey="Zhang X">X Zhang</name></author><author><name sortKey="Newman, Mej" uniqKey="Newman M">MEJ Newman</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Nicolaides, C" uniqKey="Nicolaides C">C Nicolaides</name></author><author><name sortKey="Cueto Felgueroso, L" uniqKey="Cueto Felgueroso L">L Cueto-Felgueroso</name></author><author><name sortKey="Gonzalez, Mc" uniqKey="Gonzalez M">MC González</name></author><author><name sortKey="Juanes, R" uniqKey="Juanes R">R Juanes</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Seidman, Sb" uniqKey="Seidman S">SB Seidman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Machens, A" uniqKey="Machens A">A Machens</name></author><author><name sortKey="Gesualdo, F" uniqKey="Gesualdo F">F Gesualdo</name></author><author><name sortKey="Rizzo, C" uniqKey="Rizzo C">C Rizzo</name></author><author><name sortKey="Tozzi, Ae" uniqKey="Tozzi A">AE Tozzi</name></author><author><name sortKey="Barrat, A" uniqKey="Barrat A">A Barrat</name></author><author><name sortKey="Cattuto, C" uniqKey="Cattuto C">C Cattuto</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tizzoni, M" uniqKey="Tizzoni M">M Tizzoni</name></author><author><name sortKey="Bajardi, P" uniqKey="Bajardi P">P Bajardi</name></author><author><name sortKey="Decuyper, A" uniqKey="Decuyper A">A Decuyper</name></author><author><name sortKey="King, Gkk" uniqKey="King G">GKK King</name></author><author><name sortKey="Schneider, Cm" uniqKey="Schneider C">CM Schneider</name></author><author><name sortKey="Blondel, V" uniqKey="Blondel V">V Blondel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Deville, P" uniqKey="Deville P">P Deville</name></author><author><name sortKey="Linard, C" uniqKey="Linard C">C Linard</name></author><author><name sortKey="Martin, S" uniqKey="Martin S">S Martin</name></author><author><name sortKey="Gilbert, M" uniqKey="Gilbert M">M Gilbert</name></author><author><name sortKey="Stevens, Fr" uniqKey="Stevens F">FR Stevens</name></author><author><name sortKey="Gaughan, Ae" uniqKey="Gaughan A">AE Gaughan</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Infect Dis</journal-id><journal-id journal-id-type="iso-abbrev">BMC Infect. Dis</journal-id><journal-title-group><journal-title>BMC Infectious Diseases</journal-title></journal-title-group><issn pub-type="epub">1471-2334</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">26861206</article-id><article-id pub-id-type="pmc">4746766</article-id><article-id pub-id-type="publisher-id">1350</article-id><article-id pub-id-type="doi">10.1186/s12879-016-1350-4</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>Measuring the potential of individual airports for pandemic spread over the world airline network</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Lawyer</surname><given-names>Glenn</given-names></name><address><email>lawyer@mpi-inf.mpg.de</email></address><xref ref-type="aff" rid="Aff1"></xref></contrib><aff id="Aff1">Department of Computational Biology, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, Germany</aff></contrib-group><pub-date pub-type="epub"><day>9</day><month>2</month><year>2016</year></pub-date><pub-date pub-type="pmc-release"><day>9</day><month>2</month><year>2016</year></pub-date><pub-date pub-type="collection"><year>2016</year></pub-date><volume>16</volume><elocation-id>70</elocation-id><history><date date-type="received"><day>4</day><month>5</month><year>2015</year></date><date date-type="accepted"><day>13</day><month>1</month><year>2016</year></date></history><permissions><copyright-statement>© Lawyer. 2016</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold> This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p>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.</p></sec><sec><title>Methods</title><p>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.</p></sec><sec><title>Results</title><p>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 %.</p></sec><sec><title>Conclusions</title><p>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.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12879-016-1350-4) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Epidemic</kwd><kwd>Pandemic</kwd><kwd>Airline</kwd><kwd>Network</kwd><kwd>Stochastic</kwd><kwd>Centrality</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2016</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/PandemieGrippaleV1/Data/Pmc/Curation
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000188 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Pmc/Curation/biblio.hfd -nk 000188 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= PandemieGrippaleV1
|flux= Pmc
|étape= Curation
|type= RBID
|clé= PMC:4746766
|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/Pmc/Curation/RBID.i -Sk "pubmed:26861206" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Pmc/Curation/biblio.hfd \
| NlmPubMed2Wicri -a PandemieGrippaleV1
| This area was generated with Dilib version V0.6.34. |  |