High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea
Identifieur interne : 001223 ( Main/Exploration ); précédent : 001222; suivant : 001224High 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.
Descripteurs français
- KwdFr :
- Coronavirus du syndrome respiratoire du Moyen-Orient (isolement et purification), Flambées de maladies, Humains, Infections à coronavirus (diagnostic), Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Laboratoires, Médias sociaux (), Médias sociaux (tendances), Navigateur (), Navigateur (tendances), Prévalence, Quarantaine, République de Corée (épidémiologie).
- MESH :
- diagnostic : Infections à coronavirus.
- isolement et purification : Coronavirus du syndrome respiratoire du Moyen-Orient.
- tendances : Médias sociaux, Navigateur.
- épidémiologie : Infections à coronavirus, République de Corée.
- Flambées de maladies, Humains, Laboratoires, Médias sociaux, Navigateur, Prévalence, Quarantaine.
English descriptors
- KwdEn :
- Coronavirus Infections (diagnosis), Coronavirus Infections (epidemiology), Coronavirus Infections (transmission), Disease Outbreaks, Humans, Laboratories, Middle East Respiratory Syndrome Coronavirus (isolation & purification), Prevalence, Quarantine, Republic of Korea (epidemiology), Social Media (statistics & numerical data), Social Media (trends), Web Browser (statistics & numerical data), Web Browser (trends).
- MESH :
- diagnosis : Coronavirus Infections.
- epidemiology : Coronavirus Infections, Republic of Korea.
- isolation & purification : Middle East Respiratory Syndrome Coronavirus.
- statistics & numerical data : Social Media, Web Browser.
- transmission : Coronavirus Infections.
- trends : Social Media, Web Browser.
- Disease Outbreaks, Humans, Laboratories, Prevalence, Quarantine.
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
Affiliations:
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Le document en format XML
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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 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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><keywords scheme="KwdEn" xml:lang="en"><term>Coronavirus Infections (diagnosis)</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (transmission)</term><term>Disease Outbreaks</term><term>Humans</term><term>Laboratories</term><term>Middle East Respiratory Syndrome Coronavirus (isolation & purification)</term><term>Prevalence</term><term>Quarantine</term><term>Republic of Korea (epidemiology)</term><term>Social Media (statistics & numerical data)</term><term>Social Media (trends)</term><term>Web Browser (statistics & numerical data)</term><term>Web Browser (trends)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Coronavirus du syndrome respiratoire du Moyen-Orient (isolement et purification)</term><term>Flambées de maladies</term><term>Humains</term><term>Infections à coronavirus (diagnostic)</term><term>Infections à coronavirus (transmission)</term><term>Infections à coronavirus (épidémiologie)</term><term>Laboratoires</term><term>Médias sociaux ()</term><term>Médias sociaux (tendances)</term><term>Navigateur ()</term><term>Navigateur (tendances)</term><term>Prévalence</term><term>Quarantaine</term><term>République de Corée (épidémiologie)</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Infections à coronavirus</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Republic of Korea</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Middle East Respiratory Syndrome Coronavirus</term></keywords><keywords scheme="MESH" qualifier="isolement et purification" xml:lang="fr"><term>Coronavirus du syndrome respiratoire du Moyen-Orient</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Social Media</term><term>Web Browser</term></keywords><keywords scheme="MESH" qualifier="tendances" xml:lang="fr"><term>Médias sociaux</term><term>Navigateur</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term></keywords><keywords scheme="MESH" qualifier="trends" xml:lang="en"><term>Social Media</term><term>Web Browser</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term><term>République de Corée</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Disease Outbreaks</term><term>Humans</term><term>Laboratories</term><term>Prevalence</term><term>Quarantine</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Flambées de maladies</term><term>Humains</term><term>Laboratoires</term><term>Médias sociaux</term><term>Navigateur</term><term>Prévalence</term><term>Quarantaine</term></keywords></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" uniqKey="Osterhaus A">ADME Osterhaus</name></author><author><name sortKey="Fouchier, Ram" uniqKey="Fouchier R">RAM Fouchier</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="De Groot, Rj" uniqKey="De Groot R">RJ de Groot</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Assiri, A" uniqKey="Assiri A">A Assiri</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Henning, Kj" uniqKey="Henning K">KJ Henning</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ginsberg, J" uniqKey="Ginsberg J">J Ginsberg</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ortiz, Jr" uniqKey="Ortiz J">JR Ortiz</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Valdivia, A" uniqKey="Valdivia A">A Valdivia</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Seo, Dw" uniqKey="Seo D">DW Seo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cho, S" uniqKey="Cho S">S Cho</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Milinovich, Gj" uniqKey="Milinovich G">GJ Milinovich</name></author><author><name sortKey="Williams, Gm" uniqKey="Williams G">GM Williams</name></author><author><name sortKey="Clements, Aca" uniqKey="Clements A">ACA Clements</name></author><author><name sortKey="Hu, W" uniqKey="Hu W">W Hu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Brownstein, Js" uniqKey="Brownstein J">JS Brownstein</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Desai, R" uniqKey="Desai R">R Desai</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Desai, R" uniqKey="Desai R">R 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last="Jo">Min-Woo Jo</name><name sortKey="Kim, Sung Han" sort="Kim, Sung Han" uniqKey="Kim S" first="Sung-Han" last="Kim">Sung-Han Kim</name><name sortKey="Kwak, Haewoon" sort="Kwak, Haewoon" uniqKey="Kwak H" first="Haewoon" last="Kwak">Haewoon Kwak</name><name sortKey="Seo, Dong Woo" sort="Seo, Dong Woo" uniqKey="Seo D" first="Dong-Woo" last="Seo">Dong-Woo Seo</name><name sortKey="Shin, Soo Yong" sort="Shin, Soo Yong" uniqKey="Shin S" first="Soo-Yong" last="Shin">Soo-Yong Shin</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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