A location-centric network approach to analyzing epidemic dynamics
Identifieur interne : 002C92 ( Ncbi/Merge ); précédent : 002C91; suivant : 002C93A location-centric network approach to analyzing epidemic dynamics
Auteurs : Shiran Zhong ; Ling BianSource :
- Annals of the American Association of Geographers [ 2469-4452 ] ; 2016.
Abstract
Recent health threats, such as the SARS, H1N1, and Ebola pandemics, have stimulated great interest in network models to study the transmission of communicable diseases through human interaction and mobility. Most current network models have focused on an individual-centric perspective where individuals are represented as nodes, and the interactions among them as edges. Few of these models are concerned with the discovery of the spatial patterns and dynamics of epidemics.
We propose a location-centric, transmission network approach, in which nodes denote locations and edges denote possible disease transmissions between locations. We then identify the dynamics of transmission flows, the dynamics of critical locations, and the spatial-temporal extent of transmission pathways to assess the impact of these spatial dynamics on the evolution of an epidemic.
Results show that transmission flows shift from elementary schools to middle schools and finally universities and professional schools at different phases of an epidemic. Critical locations, identified using network analysis, are responsible for the upsurge in transmission flows during the peaks of the epidemic. The length of transmission pathways shows a power law distribution and their spatial extent is rather small. Insights gained from this study will help devise spatially sensitive strategies to control communicable diseases.
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DOI: 10.1080/00045608.2015.1113113
PubMed: 27493998
PubMed Central: 4968948
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<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">101688222</journal-id><journal-id journal-id-type="pubmed-jr-id">45472</journal-id><journal-id journal-id-type="nlm-ta">Ann Am Assoc Geogr</journal-id><journal-id journal-id-type="iso-abbrev">Ann Am Assoc Geogr</journal-id><journal-title-group><journal-title>Annals of the American Association of Geographers</journal-title></journal-title-group><issn pub-type="ppub">2469-4452</issn><issn pub-type="epub">2469-4460</issn></journal-meta><article-meta><article-id pub-id-type="pmid">27493998</article-id><article-id pub-id-type="pmc">4968948</article-id><article-id pub-id-type="doi">10.1080/00045608.2015.1113113</article-id><article-id pub-id-type="manuscript">NIHMS805004</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>A location-centric network approach to analyzing epidemic dynamics</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Zhong</surname><given-names>Shiran</given-names></name></contrib><contrib contrib-type="author"><name><surname>Bian</surname><given-names>Ling</given-names></name></contrib><aff id="A1">Department of Geography, University at Buffalo, the State University of New York</aff></contrib-group><pub-date pub-type="nihms-submitted"><day>26</day><month>7</month><year>2016</year></pub-date><pub-date pub-type="epub"><day>12</day><month>1</month><year>2016</year></pub-date><pub-date pub-type="ppub"><year>2016</year></pub-date><pub-date pub-type="pmc-release"><day>12</day><month>1</month><year>2017</year></pub-date><volume>106</volume><issue>2</issue><fpage>480</fpage><lpage>488</lpage><pmc-comment>elocation-id from pubmed: 10.1080/00045608.2015.1113113</pmc-comment>
<self-uri xlink:href="http://www.tandfonline.com/doi/full/10.1080/00045608.2015.1113113"></self-uri><abstract><p id="P1">Recent health threats, such as the SARS, H1N1, and Ebola pandemics, have stimulated great interest in network models to study the transmission of communicable diseases through human interaction and mobility. Most current network models have focused on an individual-centric perspective where individuals are represented as nodes, and the interactions among them as edges. Few of these models are concerned with the discovery of the spatial patterns and dynamics of epidemics.</p><p id="P2">We propose a location-centric, transmission network approach, in which nodes denote locations and edges denote possible disease transmissions between locations. We then identify the dynamics of transmission flows, the dynamics of critical locations, and the spatial-temporal extent of transmission pathways to assess the impact of these spatial dynamics on the evolution of an epidemic.</p><p id="P3">Results show that transmission flows shift from elementary schools to middle schools and finally universities and professional schools at different phases of an epidemic. Critical locations, identified using network analysis, are responsible for the upsurge in transmission flows during the peaks of the epidemic. The length of transmission pathways shows a power law distribution and their spatial extent is rather small. Insights gained from this study will help devise spatially sensitive strategies to control communicable diseases.</p></abstract><kwd-group><kwd>location-centric networks</kwd><kwd>spatial dynamics</kwd><kwd>network centrality</kwd><kwd>influenza</kwd></kwd-group></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Bian, Ling" sort="Bian, Ling" uniqKey="Bian L" first="Ling" last="Bian">Ling Bian</name><name sortKey="Zhong, Shiran" sort="Zhong, Shiran" uniqKey="Zhong S" first="Shiran" last="Zhong">Shiran Zhong</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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