Epidemic Contact Tracing via Communication Traces
Identifieur interne : 002A11 ( Pmc/Curation ); précédent : 002A10; suivant : 002A12Epidemic Contact Tracing via Communication Traces
Auteurs : Katayoun Farrahi [Royaume-Uni] ; Rémi Emonet [France] ; Manuel Cebrian [États-Unis, Australie]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Abstract
Traditional contact tracing relies on knowledge of the interpersonal network of physical interactions, where contagious outbreaks propagate. However, due to privacy constraints and noisy data assimilation, this network is generally difficult to reconstruct accurately. Communication traces obtained by mobile phones are known to be good proxies for the physical interaction network, and they may provide a valuable tool for contact tracing. Motivated by this assumption, we propose a model for contact tracing, where an infection is spreading in the physical interpersonal network, which can never be fully recovered; and contact tracing is occurring in a communication network which acts as a proxy for the first. We apply this dual model to a dataset covering 72 students over a 9 month period, for which both the physical interactions as well as the mobile communication traces are known. Our results suggest that a wide range of contact tracing strategies may significantly reduce the final size of the epidemic, by mainly affecting its peak of incidence. However, we find that for low overlap between the face-to-face and communication interaction network, contact tracing is only efficient at the beginning of the outbreak, due to rapidly increasing costs as the epidemic evolves. Overall, contact tracing via mobile phone communication traces may be a viable option to arrest contagious outbreaks.
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DOI: 10.1371/journal.pone.0095133
PubMed: 24787614
PubMed Central: 4006791
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xml:lang="fr">France</country><wicri:regionArea>Department of Machine Learning, Laboratoire Hubert Curien, Saint-Etienne</wicri:regionArea></affiliation></author><author><name sortKey="Cebrian, Manuel" sort="Cebrian, Manuel" uniqKey="Cebrian M" first="Manuel" last="Cebrian">Manuel Cebrian</name><affiliation wicri:level="1"><nlm:aff id="aff3"><addr-line>Media Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Media Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff4"><addr-line>Department of Computer Science and Engineering, University of California at San Diego, La Jolla, California, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science and Engineering, University of California at San Diego, La Jolla, California</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff5"><addr-line>National Information and Communications Technology Australia, Melbourne, Victoria, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>National Information and Communications Technology Australia, Melbourne, Victoria</wicri:regionArea></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Traditional contact tracing relies on knowledge of the interpersonal network of physical interactions, where contagious outbreaks propagate. However, due to privacy constraints and noisy data assimilation, this network is generally difficult to reconstruct accurately. Communication traces obtained by mobile phones are known to be good proxies for the physical interaction network, and they may provide a valuable tool for contact tracing. Motivated by this assumption, we propose a model for contact tracing, where an infection is spreading in the physical interpersonal network, which can never be fully recovered; and contact tracing is occurring in a communication network which acts as a proxy for the first. We apply this dual model to a dataset covering 72 students over a 9 month period, for which both the physical interactions as well as the mobile communication traces are known. Our results suggest that a wide range of contact tracing strategies may significantly reduce the final size of the epidemic, by mainly affecting its peak of incidence. However, we find that for low overlap between the face-to-face and communication interaction network, contact tracing is only efficient at the beginning of the outbreak, due to rapidly increasing costs as the epidemic evolves. Overall, contact tracing via mobile phone communication traces may be a viable option to arrest contagious outbreaks.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Salathe, M" uniqKey="Salathe M">M Salathé</name></author><author><name sortKey="Freifeld, Cc" uniqKey="Freifeld C">CC Freifeld</name></author><author><name sortKey="Mekaru, Sr" uniqKey="Mekaru S">SR Mekaru</name></author><author><name sortKey="Tomasulo, Af" uniqKey="Tomasulo A">AF Tomasulo</name></author><author><name sortKey="Brownstein, Js" uniqKey="Brownstein J">JS Brownstein</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Balcan, D" uniqKey="Balcan D">D Balcan</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 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">24787614</article-id><article-id pub-id-type="pmc">4006791</article-id><article-id pub-id-type="publisher-id">PONE-D-13-30076</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0095133</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biology and Life Sciences</subject><subj-group><subject>Population Biology</subject></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Computer and Information Sciences</subject><subj-group><subject>Computer Modeling</subject></subj-group><subj-group><subject>Information Technology</subject><subj-group><subject>Data Mining</subject></subj-group></subj-group><subj-group><subject>Network Analysis</subject><subj-group><subject>Social Networks</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Engineering and Technology</subject><subj-group><subject>Signal Processing</subject></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Medicine and Health Sciences</subject><subj-group><subject>Epidemiology</subject><subj-group><subject>Infectious Disease Epidemiology</subject><subject>Social Epidemiology</subject><subject>Spatial Epidemiology</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Social Sciences</subject><subj-group><subject>Sociology</subject><subj-group><subject>Communications</subject><subject>Social Mobility</subject></subj-group></subj-group></subj-group></article-categories><title-group><article-title>Epidemic Contact Tracing via Communication Traces</article-title><alt-title alt-title-type="running-head">Epidemic Contact Tracing via Communication Traces</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Farrahi</surname><given-names>Katayoun</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Emonet</surname><given-names>Rémi</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Cebrian</surname><given-names>Manuel</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref><xref ref-type="aff" rid="aff4"><sup>4</sup></xref><xref ref-type="aff" rid="aff5"><sup>5</sup></xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>Department of Computing, Goldsmiths, University of London, London, United Kingdom</addr-line></aff><aff id="aff2"><label>2</label><addr-line>Department of Machine Learning, Laboratoire Hubert Curien, Saint-Etienne, France</addr-line></aff><aff id="aff3"><label>3</label><addr-line>Media Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America</addr-line></aff><aff id="aff4"><label>4</label><addr-line>Department of Computer Science and Engineering, University of California at San Diego, La Jolla, California, United States of America</addr-line></aff><aff id="aff5"><label>5</label><addr-line>National Information and Communications Technology Australia, Melbourne, Victoria, Australia</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Lambiotte</surname><given-names>Renaud</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>University of Namur, Belgium</addr-line></aff><author-notes><corresp id="cor1">* E-mail: <email>k.farrahi@gold.ac.uk</email></corresp><fn fn-type="conflict"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type="con"><p>Conceived and designed the experiments: KF RE MC. Performed the experiments: KF RE. Analyzed the data: KF RE MC. Wrote the paper: KF RE MC.</p></fn></author-notes><pub-date pub-type="collection"><year>2014</year></pub-date><pub-date pub-type="epub"><day>1</day><month>5</month><year>2014</year></pub-date><volume>9</volume><issue>5</issue><elocation-id>e95133</elocation-id><history><date date-type="received"><day>10</day><month>7</month><year>2013</year></date><date date-type="accepted"><day>24</day><month>3</month><year>2014</year></date></history><permissions><copyright-year>2014</copyright-year><copyright-holder>Farrahi et al</copyright-holder><license><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><abstract><p>Traditional contact tracing relies on knowledge of the interpersonal network of physical interactions, where contagious outbreaks propagate. However, due to privacy constraints and noisy data assimilation, this network is generally difficult to reconstruct accurately. Communication traces obtained by mobile phones are known to be good proxies for the physical interaction network, and they may provide a valuable tool for contact tracing. Motivated by this assumption, we propose a model for contact tracing, where an infection is spreading in the physical interpersonal network, which can never be fully recovered; and contact tracing is occurring in a communication network which acts as a proxy for the first. We apply this dual model to a dataset covering 72 students over a 9 month period, for which both the physical interactions as well as the mobile communication traces are known. Our results suggest that a wide range of contact tracing strategies may significantly reduce the final size of the epidemic, by mainly affecting its peak of incidence. However, we find that for low overlap between the face-to-face and communication interaction network, contact tracing is only efficient at the beginning of the outbreak, due to rapidly increasing costs as the epidemic evolves. Overall, contact tracing via mobile phone communication traces may be a viable option to arrest contagious outbreaks.</p></abstract><funding-group><funding-statement>Manuel Cebrian is funded by the Australian Government as represented by The Department of Broadband, Communications and the Digital Economy, and The Australian Research Council through the ICT Centre of Excellence program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement></funding-group><counts><page-count count="11"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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