Elucidating Transmission Patterns From Internet Reports: Ebola and Middle East Respiratory Syndrome as Case Studies
Identifieur interne : 000B86 ( Pmc/Checkpoint ); précédent : 000B85; suivant : 000B87Elucidating Transmission Patterns From Internet Reports: Ebola and Middle East Respiratory Syndrome as Case Studies
Auteurs : Gerardo Chowell [États-Unis] ; Julie M. Cleaton ; Cecile Viboud [États-Unis]Source :
- The Journal of Infectious Diseases [ 0022-1899 ] ; 2016.
Abstract
The paucity of traditional epidemiological data during epidemic emergencies calls for alternative data streams to characterize the key features of an outbreak, including the nature of risky exposures, the reproduction number, and transmission heterogeneities. We illustrate the potential of Internet data streams to improve preparedness and response in outbreak situations by drawing from recent work on the 2014–2015 Ebola epidemic in West Africa and the 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea. We show that Internet reports providing detailed accounts of epidemiological clusters are particularly useful to characterize time trends in the reproduction number. Moreover, exposure patterns based on Internet reports align with those derived from epidemiological surveillance data on MERS and Ebola, underscoring the importance of disease amplification in hospitals and during funeral rituals (associated with Ebola), prior to the implementation of control interventions. Finally, we discuss future developments needed to generalize Internet-based approaches to study transmission dynamics.
Url:
DOI: 10.1093/infdis/jiw356
PubMed: 28830110
PubMed Central: 5144900
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Elucidating Transmission Patterns From Internet Reports: Ebola and Middle East Respiratory Syndrome as Case Studies</title><author><name sortKey="Chowell, Gerardo" sort="Chowell, Gerardo" uniqKey="Chowell G" first="Gerardo" last="Chowell">Gerardo Chowell</name><affiliation><nlm:aff id="aff1"><addr-line>School of Public Health</addr-line>,<institution>Georgia State University</institution>,<addr-line>Atlanta</addr-line></nlm:aff></affiliation><affiliation wicri:level="2"><nlm:aff id="aff2"><addr-line>Division of International Epidemiology and Population Studies</addr-line>,<institution>Fogarty International Center, National Institutes of Health</institution>,<addr-line>Bethesda, Maryland</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><placeName><region type="state">Maryland</region></placeName><wicri:cityArea>Bethesda</wicri:cityArea></affiliation></author><author><name sortKey="Cleaton, Julie M" sort="Cleaton, Julie M" uniqKey="Cleaton J" first="Julie M." last="Cleaton">Julie M. Cleaton</name><affiliation><nlm:aff id="aff1"><addr-line>School of Public Health</addr-line>,<institution>Georgia State University</institution>,<addr-line>Atlanta</addr-line></nlm:aff></affiliation></author><author><name sortKey="Viboud, Cecile" sort="Viboud, Cecile" uniqKey="Viboud C" first="Cecile" last="Viboud">Cecile Viboud</name><affiliation wicri:level="2"><nlm:aff id="aff2"><addr-line>Division of International Epidemiology and Population Studies</addr-line>,<institution>Fogarty International Center, National Institutes of Health</institution>,<addr-line>Bethesda, Maryland</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><placeName><region type="state">Maryland</region></placeName><wicri:cityArea>Bethesda</wicri:cityArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">28830110</idno><idno type="pmc">5144900</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5144900</idno><idno type="RBID">PMC:5144900</idno><idno type="doi">10.1093/infdis/jiw356</idno><date when="2016">2016</date><idno type="wicri:Area/Pmc/Corpus">000E26</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000E26</idno><idno type="wicri:Area/Pmc/Curation">000E26</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000E26</idno><idno type="wicri:Area/Pmc/Checkpoint">000B86</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000B86</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Elucidating Transmission Patterns From Internet Reports: Ebola and Middle East Respiratory Syndrome as Case Studies</title><author><name sortKey="Chowell, Gerardo" sort="Chowell, Gerardo" uniqKey="Chowell G" first="Gerardo" last="Chowell">Gerardo Chowell</name><affiliation><nlm:aff id="aff1"><addr-line>School of Public Health</addr-line>,<institution>Georgia State University</institution>,<addr-line>Atlanta</addr-line></nlm:aff></affiliation><affiliation wicri:level="2"><nlm:aff id="aff2"><addr-line>Division of International Epidemiology and Population Studies</addr-line>,<institution>Fogarty International Center, National Institutes of Health</institution>,<addr-line>Bethesda, Maryland</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><placeName><region type="state">Maryland</region></placeName><wicri:cityArea>Bethesda</wicri:cityArea></affiliation></author><author><name sortKey="Cleaton, Julie M" sort="Cleaton, Julie M" uniqKey="Cleaton J" first="Julie M." last="Cleaton">Julie M. Cleaton</name><affiliation><nlm:aff id="aff1"><addr-line>School of Public Health</addr-line>,<institution>Georgia State University</institution>,<addr-line>Atlanta</addr-line></nlm:aff></affiliation></author><author><name sortKey="Viboud, Cecile" sort="Viboud, Cecile" uniqKey="Viboud C" first="Cecile" last="Viboud">Cecile Viboud</name><affiliation wicri:level="2"><nlm:aff id="aff2"><addr-line>Division of International Epidemiology and Population Studies</addr-line>,<institution>Fogarty International Center, National Institutes of Health</institution>,<addr-line>Bethesda, Maryland</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><placeName><region type="state">Maryland</region></placeName><wicri:cityArea>Bethesda</wicri:cityArea></affiliation></author></analytic><series><title level="j">The Journal of Infectious Diseases</title><idno type="ISSN">0022-1899</idno><idno type="eISSN">1537-6613</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Abstract</title><p>The paucity of traditional epidemiological data during epidemic emergencies calls for alternative data streams to characterize the key features of an outbreak, including the nature of risky exposures, the reproduction number, and transmission heterogeneities. We illustrate the potential of Internet data streams to improve preparedness and response in outbreak situations by drawing from recent work on the 2014–2015 Ebola epidemic in West Africa and the 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea. We show that Internet reports providing detailed accounts of epidemiological clusters are particularly useful to characterize time trends in the reproduction number. Moreover, exposure patterns based on Internet reports align with those derived from epidemiological surveillance data on MERS and Ebola, underscoring the importance of disease amplification in hospitals and during funeral rituals (associated with Ebola), prior to the implementation of control interventions. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Infect Dis</journal-id><journal-id journal-id-type="iso-abbrev">J. Infect. Dis</journal-id><journal-id journal-id-type="publisher-id">jid</journal-id><journal-id journal-id-type="hwp">jinfdis</journal-id><journal-title-group><journal-title>The Journal of Infectious Diseases</journal-title></journal-title-group><issn pub-type="ppub">0022-1899</issn><issn pub-type="epub">1537-6613</issn><publisher><publisher-name>Oxford University Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">28830110</article-id><article-id pub-id-type="pmc">5144900</article-id><article-id pub-id-type="doi">10.1093/infdis/jiw356</article-id><article-id pub-id-type="publisher-id">jiw356</article-id><article-categories><subj-group subj-group-type="heading"><subject>Big Data for Infectious Disease Surveillance and Modeling</subject></subj-group></article-categories><title-group><article-title>Elucidating Transmission Patterns From Internet Reports: Ebola and Middle East Respiratory Syndrome as Case Studies</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Chowell</surname><given-names>Gerardo</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff2">2</xref><pmc-comment>gchowell@gsu.edu </pmc-comment>
</contrib><contrib contrib-type="author"><name><surname>Cleaton</surname><given-names>Julie M.</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Viboud</surname><given-names>Cecile</given-names></name><xref ref-type="aff" rid="aff2">2</xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>School of Public Health</addr-line>,<institution>Georgia State University</institution>,<addr-line>Atlanta</addr-line></aff><aff id="aff2"><label>2</label><addr-line>Division of International Epidemiology and Population Studies</addr-line>,<institution>Fogarty International Center, National Institutes of Health</institution>,<addr-line>Bethesda, Maryland</addr-line></aff><author-notes><corresp id="cor1">Correspondence: G. Chowell, School of Public Health, Georgia State University, Atlanta, GA (<email>gchowell@gsu.edu</email>).</corresp></author-notes><pub-date pub-type="ppub"><day>01</day><month>12</month><year>2016</year></pub-date><pub-date pub-type="epub"><day>14</day><month>11</month><year>2016</year></pub-date><pub-date pub-type="pmc-release"><day>14</day><month>11</month><year>2016</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<pmc-comment> oupReleaseDelayRemoved from OA Article (10.1093/infdis/jiw356jiw356BIG DATA FOR INFECTIOUS DISEASE SURVEILLANCE AND MODELINGElucidating Transmission Patterns From Internet Reports: Ebola and Middle East Respiratory Syndrome as Case StudiesChowellGerardogchowell@gsu.eduCleatonJulie M.ViboudCecile
1
School of Public Health, Georgia State University, Atlanta
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Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MarylandCorrespondence: G. Chowell, School of Public Health, Georgia State University, Atlanta, GA (gchowell@gsu.edu).011220160112201614112016214suppl_4Big Data for Infectious Disease Surveillance and ModelingS421S426Supplementary DataPublished by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.2016AbstractThe paucity of traditional epidemiological data during epidemic emergencies calls for alternative data streams to characterize the key features of an outbreak, including the nature of risky exposures, the reproduction number, and transmission heterogeneities. We illustrate the potential of Internet data streams to improve preparedness and response in outbreak situations by drawing from recent work on the 2014–2015 Ebola epidemic in West Africa and the 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea. We show that Internet reports providing detailed accounts of epidemiological clusters are particularly useful to characterize time trends in the reproduction number. Moreover, exposure patterns based on Internet reports align with those derived from epidemiological surveillance data on MERS and Ebola, underscoring the importance of disease amplification in hospitals and during funeral rituals (associated with Ebola), prior to the implementation of control interventions. Finally, we discuss future developments needed to generalize Internet-based approaches to study transmission dynamics.transmission chainclusterInternet data streambig datatransmission patternsexposure settingcontact networkreproduction numberEbolaMERSDivision of International Epidemiology and Population StudiesFogarty International CenterNational Institutes of HealthNIHRAPIDD Program, Science and Technology Directorate, Department of Homeland SecurityNational Science FoundationNDF1414374NIHDepartment of Agriculture Ecology and Evolution of Infectious DiseasesUnited Kingdom Biotechnology and Biological Sciences Research CouncilBB/M008894/1) </pmc-comment>
<volume>214</volume><issue>Suppl 4</issue><issue-title>Big Data for Infectious Disease Surveillance and Modeling</issue-title><fpage>S421</fpage><lpage>S426</lpage><permissions><copyright-statement>Published by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.</copyright-statement><copyright-year>2016</copyright-year><license><license-p>This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.</license-p></license></permissions><self-uri xlink:href="jiw356.pdf"></self-uri><abstract><title>Abstract</title><p>The paucity of traditional epidemiological data during epidemic emergencies calls for alternative data streams to characterize the key features of an outbreak, including the nature of risky exposures, the reproduction number, and transmission heterogeneities. We illustrate the potential of Internet data streams to improve preparedness and response in outbreak situations by drawing from recent work on the 2014–2015 Ebola epidemic in West Africa and the 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea. We show that Internet reports providing detailed accounts of epidemiological clusters are particularly useful to characterize time trends in the reproduction number. Moreover, exposure patterns based on Internet reports align with those derived from epidemiological surveillance data on MERS and Ebola, underscoring the importance of disease amplification in hospitals and during funeral rituals (associated with Ebola), prior to the implementation of control interventions. Finally, we discuss future developments needed to generalize Internet-based approaches to study transmission dynamics.</p></abstract><kwd-group><kwd>transmission chain</kwd><kwd>cluster</kwd><kwd>Internet data stream</kwd><kwd>big data</kwd><kwd>transmission patterns</kwd><kwd>exposure setting</kwd><kwd>contact network</kwd><kwd>reproduction number</kwd><kwd>Ebola</kwd><kwd>MERS</kwd></kwd-group><funding-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">Division of International Epidemiology and Population Studies</named-content></funding-source></award-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">Fogarty International Center</named-content></funding-source></award-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">National Institutes of Health</named-content></funding-source></award-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">NIH</named-content></funding-source></award-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">RAPIDD Program, Science and Technology Directorate, Department of Homeland Security</named-content></funding-source></award-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">National Science Foundation</named-content></funding-source></award-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">NDF</named-content></funding-source><award-id>1414374</award-id></award-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">NIH</named-content></funding-source></award-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">Department of Agriculture Ecology and Evolution of Infectious Diseases</named-content></funding-source></award-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">United Kingdom Biotechnology and Biological Sciences Research Council</named-content></funding-source><award-id>BB/M008894/1</award-id></award-group></funding-group></article-meta></front></pmc><affiliations><list><country><li>États-Unis</li></country><region><li>Maryland</li></region></list><tree><noCountry><name sortKey="Cleaton, Julie M" sort="Cleaton, Julie M" uniqKey="Cleaton J" first="Julie M." last="Cleaton">Julie M. Cleaton</name></noCountry><country name="États-Unis"><region name="Maryland"><name sortKey="Chowell, Gerardo" sort="Chowell, Gerardo" uniqKey="Chowell G" first="Gerardo" last="Chowell">Gerardo Chowell</name></region><name sortKey="Viboud, Cecile" sort="Viboud, Cecile" uniqKey="Viboud C" first="Cecile" last="Viboud">Cecile Viboud</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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