Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks
Identifieur interne : 000762 ( Pmc/Curation ); précédent : 000761; suivant : 000763Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks
Auteurs : Dong-Woo Seo [Corée du Sud] ; Soo-Yong Shin [Corée du Sud]Source :
- Healthcare Informatics Research [ 2093-3681 ] ; 2017.
Abstract
For earlier detection of infectious disease outbreaks, a digital syndromic surveillance system based on search queries or social media should be utilized. By using real-time data sources, a digital syndromic surveillance system can overcome the limitation of time-delay in traditional surveillance systems. Here, we introduce an approach to develop such a digital surveillance system.
We first explain how the statistics data of infectious diseases, such as influenza and Middle East Respiratory Syndrome (MERS) in Korea, can be collected for reference data. Then we also explain how search engine queries can be retrieved from Google Trends. Finally, we describe the implementation of the prediction model using lagged correlation, which can be calculated by the statistical packages, i.e., SPSS (Statistical Package for the Social Sciences).
Lag correlation analyses demonstrated that search engine data/Twitter have a significant temporal relationship with influenza and MERS data. Therefore, the proposed digital surveillance system can be used to predict infectious disease outbreaks earlier.
This prediction method could be the core engine for implementing a (near-) real-time digital surveillance system. A digital surveillance system that uses Internet resources has enormous potential to monitor disease outbreaks in the early phase.
Url:
DOI: 10.4258/hir.2017.23.4.343
PubMed: 29181246
PubMed Central: 5688036
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By using real-time data sources, a digital syndromic surveillance system can overcome the limitation of time-delay in traditional surveillance systems. Here, we introduce an approach to develop such a digital surveillance system.</p></sec><sec><title>Methods</title><p>We first explain how the statistics data of infectious diseases, such as influenza and Middle East Respiratory Syndrome (MERS) in Korea, can be collected for reference data. Then we also explain how search engine queries can be retrieved from Google Trends. Finally, we describe the implementation of the prediction model using lagged correlation, which can be calculated by the statistical packages, i.e., SPSS (Statistical Package for the Social Sciences).</p></sec><sec><title>Results</title><p>Lag correlation analyses demonstrated that search engine data/Twitter have a significant temporal relationship with influenza and MERS data. Therefore, the proposed digital surveillance system can be used to predict infectious disease outbreaks earlier.</p></sec><sec><title>Conclusions</title><p>This prediction method could be the core engine for implementing a (near-) real-time digital surveillance system. A digital surveillance system that uses Internet resources has enormous potential to monitor disease outbreaks in the early phase.</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><analytic><author><name sortKey="Dawood, Fs" uniqKey="Dawood F">FS Dawood</name></author><author><name sortKey="Jain, S" uniqKey="Jain S">S Jain</name></author><author><name sortKey="Finelli, L" uniqKey="Finelli L">L Finelli</name></author><author><name sortKey="Shaw, Mw" uniqKey="Shaw M">MW Shaw</name></author><author><name sortKey="Lindstrom, S" uniqKey="Lindstrom S">S Lindstrom</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Shin, Sy" uniqKey="Shin S">SY Shin</name></author><author><name sortKey="Seo, Dw" uniqKey="Seo D">DW Seo</name></author><author><name sortKey="An, J" uniqKey="An J">J An</name></author><author><name sortKey="Kwak, H" uniqKey="Kwak H">H Kwak</name></author><author><name sortKey="Kim, Sh" uniqKey="Kim S">SH Kim</name></author><author><name sortKey="Gwack, J" uniqKey="Gwack J">J Gwack</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><author><name sortKey="Mohebbi, Mh" uniqKey="Mohebbi M">MH Mohebbi</name></author><author><name sortKey="Patel, Rs" uniqKey="Patel R">RS Patel</name></author><author><name sortKey="Brammer, L" uniqKey="Brammer L">L Brammer</name></author><author><name sortKey="Smolinski, Ms" uniqKey="Smolinski M">MS Smolinski</name></author><author><name sortKey="Brilliant, L" uniqKey="Brilliant L">L Brilliant</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Eysenbach, G" uniqKey="Eysenbach G">G Eysenbach</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cho, S" uniqKey="Cho S">S Cho</name></author><author><name sortKey="Sohn, Ch" uniqKey="Sohn C">CH Sohn</name></author><author><name sortKey="Jo, Mw" uniqKey="Jo M">MW Jo</name></author><author><name sortKey="Shin, Sy" uniqKey="Shin S">SY Shin</name></author><author><name sortKey="Lee, Jh" uniqKey="Lee J">JH Lee</name></author><author><name sortKey="Ryoo, Sm" uniqKey="Ryoo S">SM Ryoo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Seo, Dw" uniqKey="Seo D">DW Seo</name></author><author><name sortKey="Jo, Mw" uniqKey="Jo M">MW Jo</name></author><author><name sortKey="Sohn, Ch" uniqKey="Sohn C">CH Sohn</name></author><author><name sortKey="Shin, Sy" uniqKey="Shin S">SY Shin</name></author><author><name sortKey="Lee, J" uniqKey="Lee J">J Lee</name></author><author><name sortKey="Yu, M" uniqKey="Yu M">M Yu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hulth, A" uniqKey="Hulth A">A Hulth</name></author><author><name sortKey="Rydevik, G" uniqKey="Rydevik G">G Rydevik</name></author><author><name sortKey="Linde, A" uniqKey="Linde A">A Linde</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Shin, Sy" uniqKey="Shin S">SY Shin</name></author><author><name sortKey="Kim, T" uniqKey="Kim T">T Kim</name></author><author><name sortKey="Seo, Dw" uniqKey="Seo D">DW Seo</name></author><author><name sortKey="Sohn, Ch" uniqKey="Sohn C">CH Sohn</name></author><author><name sortKey="Kim, Sh" uniqKey="Kim S">SH Kim</name></author><author><name sortKey="Ryoo, Sm" uniqKey="Ryoo S">SM Ryoo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Signorini, A" uniqKey="Signorini A">A Signorini</name></author><author><name sortKey="Segre, Am" uniqKey="Segre A">AM Segre</name></author><author><name sortKey="Polgreen, Pm" uniqKey="Polgreen P">PM Polgreen</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Lazer, D" uniqKey="Lazer D">D Lazer</name></author><author><name sortKey="Kennedy, R" uniqKey="Kennedy R">R Kennedy</name></author><author><name sortKey="King, G" uniqKey="King G">G King</name></author><author><name sortKey="Vespignani, A" uniqKey="Vespignani A">A Vespignani</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="case-report"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Healthc Inform Res</journal-id><journal-id journal-id-type="iso-abbrev">Healthc Inform Res</journal-id><journal-id journal-id-type="publisher-id">HIR</journal-id><journal-title-group><journal-title>Healthcare Informatics Research</journal-title></journal-title-group><issn pub-type="ppub">2093-3681</issn><issn pub-type="epub">2093-369X</issn><publisher><publisher-name>Korean Society of Medical Informatics</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">29181246</article-id><article-id pub-id-type="pmc">5688036</article-id><article-id pub-id-type="doi">10.4258/hir.2017.23.4.343</article-id><article-categories><subj-group subj-group-type="heading"><subject>Tutorial</subject></subj-group></article-categories><title-group><article-title>Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Seo</surname><given-names>Dong-Woo</given-names></name><degrees>MD</degrees><degrees>PhD</degrees><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author" corresp="yes"><name><surname>Shin</surname><given-names>Soo-Yong</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="A2">2</xref></contrib></contrib-group><aff id="A1"><label>1</label>Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.</aff><aff id="A2"><label>2</label>Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea.</aff><author-notes><corresp>Corresponding Author: Soo-Yong Shin, PhD. Department of Computer Science and Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin 17104, Korea. Tel: +82-31-201-2543, <email>sooyong.shin@khu.ac.kr</email></corresp></author-notes><pub-date pub-type="ppub"><month>10</month><year>2017</year></pub-date><pub-date pub-type="epub"><day>31</day><month>10</month><year>2017</year></pub-date><volume>23</volume><issue>4</issue><fpage>343</fpage><lpage>348</lpage><history><date date-type="received"><day>11</day><month>7</month><year>2017</year></date><date date-type="rev-recd"><day>24</day><month>8</month><year>2017</year></date><date date-type="accepted"><day>10</day><month>9</month><year>2017</year></date></history><permissions><copyright-statement>© 2017 The Korean Society of Medical Informatics</copyright-statement><copyright-year>2017</copyright-year><copyright-holder>The Korean Society of Medical Informatics</copyright-holder><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/">http://creativecommons.org/licenses/by-nc/4.0/</ext-link>) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract><sec><title>Objectives</title><p>For earlier detection of infectious disease outbreaks, a digital syndromic surveillance system based on search queries or social media should be utilized. By using real-time data sources, a digital syndromic surveillance system can overcome the limitation of time-delay in traditional surveillance systems. Here, we introduce an approach to develop such a digital surveillance system.</p></sec><sec><title>Methods</title><p>We first explain how the statistics data of infectious diseases, such as influenza and Middle East Respiratory Syndrome (MERS) in Korea, can be collected for reference data. Then we also explain how search engine queries can be retrieved from Google Trends. Finally, we describe the implementation of the prediction model using lagged correlation, which can be calculated by the statistical packages, i.e., SPSS (Statistical Package for the Social Sciences).</p></sec><sec><title>Results</title><p>Lag correlation analyses demonstrated that search engine data/Twitter have a significant temporal relationship with influenza and MERS data. Therefore, the proposed digital surveillance system can be used to predict infectious disease outbreaks earlier.</p></sec><sec><title>Conclusions</title><p>This prediction method could be the core engine for implementing a (near-) real-time digital surveillance system. A digital surveillance system that uses Internet resources has enormous potential to monitor disease outbreaks in the early phase.</p></sec></abstract><kwd-group><kwd>Digital Syndromic Surveillance System</kwd><kwd>Disease Outbreak</kwd><kwd>Social Media</kwd><kwd>Search Engine</kwd></kwd-group></article-meta></front><floats-group><fig id="F1" orientation="portrait" position="float"><label>Figure 1</label><caption><title>ILI report example from week 28, 2017 (July 9, 2017–July 15, 2017). The ratio is the number of outpatients divided by 1,000.</title></caption><graphic xlink:href="hir-23-343-g001"></graphic></fig><fig id="F2" orientation="portrait" position="float"><label>Figure 2</label><caption><title>Trends of influenza search queries, according to Google Trends, between September 9, 2007 and September 8, 2012.</title></caption><graphic xlink:href="hir-23-343-g002"></graphic></fig><table-wrap id="T1" orientation="portrait" position="float"><label>Table 1</label><caption><title>The chosen keywords for influenza and MERS</title></caption><graphic xlink:href="hir-23-343-i001"></graphic><table-wrap-foot><fn><p>MERS: Middle East Respiratory Syndrome, PCR: polymerase chain reaction.</p><p><sup>a</sup>Only Korean keyword was used, bonly English keywords were used.</p></fn></table-wrap-foot></table-wrap><table-wrap id="T2" orientation="portrait" position="float"><label>Table 2</label><caption><title>Example of influenza data and Google Trends data</title></caption><graphic xlink:href="hir-23-343-i002"></graphic><table-wrap-foot><fn><p>Virological and ILI data were extracted manually from ILI reports. The trend data of representative keywords were extracted from Google Trends. Data column represents the week data with starting date.</p><p>KCDC: Korea Centers for Disease Control & Prevention, ILI: influenza-like illness.</p></fn></table-wrap-foot></table-wrap></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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