High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea
Identifieur interne : 000436 ( Pmc/Curation ); précédent : 000435; suivant : 000437High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea
Auteurs : Soo-Yong Shin ; Dong-Woo Seo ; Jisun An ; Haewoon Kwak ; Sung-Han Kim ; Jin Gwack ; Min-Woo JoSource :
- Scientific Reports [ 2045-2322 ] ; 2016.
Abstract
The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman correlation analysis. We found high correlations (>0.7) between Google search and Twitter results and the number of confirmed MERS cases for the previous three days using only four simple keywords: “MERS”, “
The online version of this article (doi:10.1038/srep32920) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1038/srep32920
PubMed: 27595921
PubMed Central: 5011762
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Jisun An<affiliation><nlm:aff id="Aff3">Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha Qatar</nlm:aff><wicri:noCountry code="subfield">Doha Qatar</wicri:noCountry></affiliation><affiliation><nlm:aff id="Aff3">Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha Qatar</nlm:aff><wicri:noCountry code="subfield">Doha Qatar</wicri:noCountry></affiliation><affiliation><nlm:aff id="Aff5">Center for Disease Control and Prevention, Osong, Chungbuk Korea</nlm:aff><wicri:noCountry code="subfield">Chungbuk Korea</wicri:noCountry></affiliation>Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea</title><author><name sortKey="Shin, Soo Yong" sort="Shin, Soo Yong" uniqKey="Shin S" first="Soo-Yong" last="Shin">Soo-Yong Shin</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.413967.e</institution-id><institution-id institution-id-type="ISNI">0000 0001 0842 2126</institution-id><institution>Department of Biomedical Informatics,</institution><institution>Asan Medical Center,</institution></institution-wrap>Seoul Korea</nlm:aff></affiliation></author><author><name sortKey="Seo, Dong Woo" sort="Seo, Dong Woo" uniqKey="Seo D" first="Dong-Woo" last="Seo">Dong-Woo Seo</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.413967.e</institution-id><institution-id institution-id-type="ISNI">0000 0001 0842 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with Google search and Twitter trends in Korea</title><author><name sortKey="Shin, Soo Yong" sort="Shin, Soo Yong" uniqKey="Shin S" first="Soo-Yong" last="Shin">Soo-Yong Shin</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.413967.e</institution-id><institution-id institution-id-type="ISNI">0000 0001 0842 2126</institution-id><institution>Department of Biomedical Informatics,</institution><institution>Asan Medical Center,</institution></institution-wrap>Seoul Korea</nlm:aff></affiliation></author><author><name sortKey="Seo, Dong Woo" sort="Seo, Dong Woo" uniqKey="Seo D" first="Dong-Woo" last="Seo">Dong-Woo Seo</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.413967.e</institution-id><institution-id institution-id-type="ISNI">0000 0001 0842 2126</institution-id><institution>Department of Emergency Medicine,</institution><institution>Asan Medical Center, University of Ulsan College of Medicine,</institution></institution-wrap>Seoul Korea</nlm:aff></affiliation></author><author><name sortKey="An, Jisun" sort="An, Jisun" uniqKey="An J" first="Jisun" last="An">Jisun An</name><affiliation><nlm:aff id="Aff3">Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha Qatar</nlm:aff><wicri:noCountry code="subfield">Doha Qatar</wicri:noCountry></affiliation></author><author><name sortKey="Kwak, Haewoon" sort="Kwak, Haewoon" uniqKey="Kwak H" first="Haewoon" last="Kwak">Haewoon Kwak</name><affiliation><nlm:aff id="Aff3">Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha Qatar</nlm:aff><wicri:noCountry code="subfield">Doha Qatar</wicri:noCountry></affiliation></author><author><name sortKey="Kim, Sung Han" sort="Kim, Sung Han" uniqKey="Kim S" first="Sung-Han" last="Kim">Sung-Han Kim</name><affiliation><nlm:aff id="Aff4"><institution-wrap><institution-id institution-id-type="GRID">grid.413967.e</institution-id><institution-id 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Medicine,</institution></institution-wrap>Seoul Korea</nlm:aff></affiliation></author></analytic><series><title level="j">Scientific Reports</title><idno type="eISSN">2045-2322</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman correlation analysis. We found high correlations (>0.7) between Google search and Twitter results and the number of confirmed MERS cases for the previous three days using only four simple keywords: “MERS”, “<inline-formula id="IEq1"><inline-graphic xlink:href="41598_2016_Article_BFsrep32920_IEq1_HTML.gif"></inline-graphic></inline-formula>” (“MERS (in Korean)”), “<inline-formula id="IEq2"><inline-graphic xlink:href="41598_2016_Article_BFsrep32920_IEq1_HTML.gif"></inline-graphic></inline-formula>” (“MERS symptoms (in Korean)”), and “<inline-formula id="IEq3"><inline-graphic xlink:href="41598_2016_Article_BFsrep32920_IEq2_HTML.gif"></inline-graphic></inline-formula>” (“MERS hospital (in Korean)”). Additionally, we found high correlations between the Google search and Twitter results and the number of quarantined cases using the above keywords. This study demonstrates the possibility of using a digital surveillance system to monitor the outbreak of MERS.</p><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1038/srep32920) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Peiris, Js" uniqKey="Peiris J">JS Peiris</name></author><author><name sortKey="Guan, Y" uniqKey="Guan Y">Y Guan</name></author><author><name sortKey="Yuen, Ky" uniqKey="Yuen K">KY Yuen</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Zaki, Am" uniqKey="Zaki A">AM Zaki</name></author><author><name sortKey="Van Boheemen, S" uniqKey="Van Boheemen S">S van Boheemen</name></author><author><name sortKey="Bestebroer, Tm" uniqKey="Bestebroer T">TM Bestebroer</name></author><author><name sortKey="Osterhaus, Adme" 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Sci Rep</journal-id><journal-id journal-id-type="iso-abbrev">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type="epub">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">27595921</article-id><article-id pub-id-type="pmc">5011762</article-id><article-id pub-id-type="publisher-id">BFsrep32920</article-id><article-id pub-id-type="doi">10.1038/srep32920</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea</article-title></title-group><contrib-group><contrib contrib-type="author" equal-contrib="yes"><name><surname>Shin</surname><given-names>Soo-Yong</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author" equal-contrib="yes"><name><surname>Seo</surname><given-names>Dong-Woo</given-names></name><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>An</surname><given-names>Jisun</given-names></name><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Kwak</surname><given-names>Haewoon</given-names></name><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Kim</surname><given-names>Sung-Han</given-names></name><xref ref-type="aff" rid="Aff4">4</xref></contrib><contrib contrib-type="author"><name><surname>Gwack</surname><given-names>Jin</given-names></name><xref ref-type="aff" rid="Aff5">5</xref></contrib><contrib contrib-type="author"><name><surname>Jo</surname><given-names>Min-Woo</given-names></name><address><email>mdjominwoo@gmail.com</email></address><xref ref-type="aff" rid="Aff6">6</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="GRID">grid.413967.e</institution-id><institution-id institution-id-type="ISNI">0000 0001 0842 2126</institution-id><institution>Department of Biomedical Informatics,</institution><institution>Asan Medical Center,</institution></institution-wrap>Seoul Korea</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="GRID">grid.413967.e</institution-id><institution-id institution-id-type="ISNI">0000 0001 0842 2126</institution-id><institution>Department of Emergency Medicine,</institution><institution>Asan Medical Center, University of Ulsan College of Medicine,</institution></institution-wrap>Seoul Korea</aff><aff id="Aff3"><label>3</label>Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha Qatar</aff><aff id="Aff4"><label>4</label><institution-wrap><institution-id institution-id-type="GRID">grid.413967.e</institution-id><institution-id institution-id-type="ISNI">0000 0001 0842 2126</institution-id><institution>Department of Infectious Diseases,</institution><institution>Asan Medical Center, University of Ulsan College of Medicine,</institution></institution-wrap>Seoul Korea</aff><aff id="Aff5"><label>5</label>Center for Disease Control and Prevention, Osong, Chungbuk Korea</aff><aff id="Aff6"><label>6</label><institution-wrap><institution-id institution-id-type="GRID">grid.267370.7</institution-id><institution-id institution-id-type="ISNI">0000 0004 0533 4667</institution-id><institution>Department of Preventive Medicine,</institution><institution>University of Ulsan College of Medicine,</institution></institution-wrap>Seoul Korea</aff></contrib-group><pub-date pub-type="epub"><day>6</day><month>9</month><year>2016</year></pub-date><pub-date pub-type="pmc-release"><day>6</day><month>9</month><year>2016</year></pub-date><pub-date pub-type="collection"><year>2016</year></pub-date><volume>6</volume><elocation-id>32920</elocation-id><history><date date-type="received"><day>23</day><month>9</month><year>2015</year></date><date date-type="accepted"><day>16</day><month>8</month><year>2016</year></date></history><permissions><copyright-statement>© The Author(s) 2016</copyright-statement><license license-type="OpenAccess"><license-p>This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><abstract id="Abs1"><p>The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman correlation analysis. We found high correlations (>0.7) between Google search and Twitter results and the number of confirmed MERS cases for the previous three days using only four simple keywords: “MERS”, “<inline-formula id="IEq1"><inline-graphic xlink:href="41598_2016_Article_BFsrep32920_IEq1_HTML.gif"></inline-graphic></inline-formula>” (“MERS (in Korean)”), “<inline-formula id="IEq2"><inline-graphic xlink:href="41598_2016_Article_BFsrep32920_IEq1_HTML.gif"></inline-graphic></inline-formula>” (“MERS symptoms (in Korean)”), and “<inline-formula id="IEq3"><inline-graphic xlink:href="41598_2016_Article_BFsrep32920_IEq2_HTML.gif"></inline-graphic></inline-formula>” (“MERS hospital (in Korean)”). Additionally, we found high correlations between the Google search and Twitter results and the number of quarantined cases using the above keywords. This study demonstrates the possibility of using a digital surveillance system to monitor the outbreak of MERS.</p><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1038/srep32920) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group kwd-group-type="npg-subject"><title>Subject terms</title><kwd>Health care</kwd><kwd>Medical research</kwd><kwd>Signs and symptoms</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)
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