High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea.
Identifieur interne : 000F76 ( PubMed/Corpus ); précédent : 000F75; suivant : 000F77High 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.
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", " ("MERS (in Korean)"), " ("MERS symptoms (in Korean)"), and " ("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.
DOI: 10.1038/srep32920
PubMed: 27595921
Links to Exploration step
pubmed:27595921Le 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:affiliation>Department of Biomedical Informatics, Asan Medical Center, Seoul, Korea.</nlm:affiliation></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:affiliation>Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.</nlm:affiliation></affiliation></author><author><name sortKey="An, Jisun" sort="An, Jisun" uniqKey="An J" first="Jisun" last="An">Jisun An</name><affiliation><nlm:affiliation>Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.</nlm:affiliation></affiliation></author><author><name sortKey="Kwak, Haewoon" sort="Kwak, Haewoon" uniqKey="Kwak H" first="Haewoon" last="Kwak">Haewoon Kwak</name><affiliation><nlm:affiliation>Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.</nlm:affiliation></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:affiliation>Department of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Gwack, Jin" sort="Gwack, Jin" uniqKey="Gwack J" first="Jin" last="Gwack">Jin Gwack</name><affiliation><nlm:affiliation>Center for Disease Control and Prevention, Osong, Chungbuk, Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Jo, Min Woo" sort="Jo, Min Woo" uniqKey="Jo M" first="Min-Woo" last="Jo">Min-Woo Jo</name><affiliation><nlm:affiliation>Department of Preventive Medicine, University of Ulsan College of Medicine, Seoul, Korea.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2016">2016</date><idno type="RBID">pubmed:27595921</idno><idno type="pmid">27595921</idno><idno type="doi">10.1038/srep32920</idno><idno type="wicri:Area/PubMed/Corpus">000F76</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000F76</idno></publicationStmt><sourceDesc><biblStruct><analytic><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:affiliation>Department of Biomedical Informatics, Asan Medical Center, Seoul, Korea.</nlm:affiliation></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:affiliation>Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.</nlm:affiliation></affiliation></author><author><name sortKey="An, Jisun" sort="An, Jisun" uniqKey="An J" first="Jisun" last="An">Jisun An</name><affiliation><nlm:affiliation>Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.</nlm:affiliation></affiliation></author><author><name sortKey="Kwak, Haewoon" sort="Kwak, Haewoon" uniqKey="Kwak H" first="Haewoon" last="Kwak">Haewoon Kwak</name><affiliation><nlm:affiliation>Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.</nlm:affiliation></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:affiliation>Department of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Gwack, Jin" sort="Gwack, Jin" uniqKey="Gwack J" first="Jin" last="Gwack">Jin Gwack</name><affiliation><nlm:affiliation>Center for Disease Control and Prevention, Osong, Chungbuk, Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Jo, Min Woo" sort="Jo, Min Woo" uniqKey="Jo M" first="Min-Woo" last="Jo">Min-Woo Jo</name><affiliation><nlm:affiliation>Department of Preventive Medicine, University of Ulsan College of Medicine, Seoul, Korea.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Scientific reports</title><idno type="eISSN">2045-2322</idno><imprint><date when="2016" type="published">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="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</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="statistics & numerical data" xml:lang="en"><term>Social Media</term><term>Web Browser</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" xml:lang="en"><term>Disease Outbreaks</term><term>Humans</term><term>Laboratories</term><term>Prevalence</term><term>Quarantine</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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", " ("MERS (in Korean)"), " ("MERS symptoms (in Korean)"), and " ("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.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">27595921</PMID><DateCompleted><Year>2018</Year><Month>06</Month><Day>27</Day></DateCompleted><DateRevised><Year>2020</Year><Month>03</Month><Day>25</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2045-2322</ISSN><JournalIssue CitedMedium="Internet"><Volume>6</Volume><PubDate><Year>2016</Year><Month>09</Month><Day>06</Day></PubDate></JournalIssue><Title>Scientific reports</Title><ISOAbbreviation>Sci Rep</ISOAbbreviation></Journal><ArticleTitle>High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea.</ArticleTitle><Pagination><MedlinePgn>32920</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1038/srep32920</ELocationID><Abstract><AbstractText>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", " ("MERS (in Korean)"), " ("MERS symptoms (in Korean)"), and " ("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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Shin</LastName><ForeName>Soo-Yong</ForeName><Initials>SY</Initials><AffiliationInfo><Affiliation>Department of Biomedical Informatics, Asan Medical Center, Seoul, Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Seo</LastName><ForeName>Dong-Woo</ForeName><Initials>DW</Initials><AffiliationInfo><Affiliation>Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>An</LastName><ForeName>Jisun</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kwak</LastName><ForeName>Haewoon</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kim</LastName><ForeName>Sung-Han</ForeName><Initials>SH</Initials><AffiliationInfo><Affiliation>Department of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gwack</LastName><ForeName>Jin</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Center for Disease Control and Prevention, Osong, Chungbuk, Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jo</LastName><ForeName>Min-Woo</ForeName><Initials>MW</Initials><AffiliationInfo><Affiliation>Department of Preventive Medicine, University of Ulsan College of Medicine, Seoul, Korea.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>09</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Sci Rep</MedlineTA><NlmUniqueID>101563288</NlmUniqueID><ISSNLinking>2045-2322</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="Y">Disease Outbreaks</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" 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