Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks
Identifieur interne : 000C96 ( Main/Exploration ); précédent : 000C95; suivant : 000C97Methods 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
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Pmc, to step Corpus: 000762
- to stream Pmc, to step Curation: 000762
- to stream Pmc, to step Checkpoint: 000790
- to stream PubMed, to step Corpus: 000A68
- to stream PubMed, to step Curation: 000A68
- to stream PubMed, to step Checkpoint: 000C25
- to stream Ncbi, to step Merge: 001C66
- to stream Ncbi, to step Curation: 001C66
- to stream Ncbi, to step Checkpoint: 001C66
- to stream Main, to step Merge: 000C99
- to stream Main, to step Curation: 000C96
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks</title>
<author><name sortKey="Seo, Dong Woo" sort="Seo, Dong Woo" uniqKey="Seo D" first="Dong-Woo" last="Seo">Dong-Woo Seo</name>
<affiliation wicri:level="3"><nlm:aff id="A1">Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.</nlm:aff>
<country xml:lang="fr" wicri:curation="lc">Corée du Sud</country>
<wicri:regionArea>Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul</wicri:regionArea>
<placeName><settlement type="city">Séoul</settlement>
<region type="capital">Région capitale de Séoul</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Shin, Soo Yong" sort="Shin, Soo Yong" uniqKey="Shin S" first="Soo-Yong" last="Shin">Soo-Yong Shin</name>
<affiliation wicri:level="1"><nlm:aff id="A2">Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea.</nlm:aff>
<country xml:lang="fr" wicri:curation="lc">Corée du Sud</country>
<wicri:regionArea>Department of Computer Science and Engineering, Kyung Hee University, Yongin</wicri:regionArea>
<wicri:noRegion>Yongin</wicri:noRegion>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PMC</idno>
<idno type="pmid">29181246</idno>
<idno type="pmc">5688036</idno>
<idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688036</idno>
<idno type="RBID">PMC:5688036</idno>
<idno type="doi">10.4258/hir.2017.23.4.343</idno>
<date when="2017">2017</date>
<idno type="wicri:Area/Pmc/Corpus">000762</idno>
<idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000762</idno>
<idno type="wicri:Area/Pmc/Curation">000762</idno>
<idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000762</idno>
<idno type="wicri:Area/Pmc/Checkpoint">000790</idno>
<idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000790</idno>
<idno type="wicri:source">PubMed</idno>
<idno type="RBID">pubmed:29181246</idno>
<idno type="wicri:Area/PubMed/Corpus">000A68</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000A68</idno>
<idno type="wicri:Area/PubMed/Curation">000A68</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000A68</idno>
<idno type="wicri:Area/PubMed/Checkpoint">000C25</idno>
<idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000C25</idno>
<idno type="wicri:Area/Ncbi/Merge">001C66</idno>
<idno type="wicri:Area/Ncbi/Curation">001C66</idno>
<idno type="wicri:Area/Ncbi/Checkpoint">001C66</idno>
<idno type="wicri:doubleKey">2093-3681:2017:Seo D:methods:using:social</idno>
<idno type="wicri:Area/Main/Merge">000C99</idno>
<idno type="wicri:Area/Main/Curation">000C96</idno>
<idno type="wicri:Area/Main/Exploration">000C96</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks</title>
<author><name sortKey="Seo, Dong Woo" sort="Seo, Dong Woo" uniqKey="Seo D" first="Dong-Woo" last="Seo">Dong-Woo Seo</name>
<affiliation wicri:level="3"><nlm:aff id="A1">Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.</nlm:aff>
<country xml:lang="fr" wicri:curation="lc">Corée du Sud</country>
<wicri:regionArea>Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul</wicri:regionArea>
<placeName><settlement type="city">Séoul</settlement>
<region type="capital">Région capitale de Séoul</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Shin, Soo Yong" sort="Shin, Soo Yong" uniqKey="Shin S" first="Soo-Yong" last="Shin">Soo-Yong Shin</name>
<affiliation wicri:level="1"><nlm:aff id="A2">Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea.</nlm:aff>
<country xml:lang="fr" wicri:curation="lc">Corée du Sud</country>
<wicri:regionArea>Department of Computer Science and Engineering, Kyung Hee University, Yongin</wicri:regionArea>
<wicri:noRegion>Yongin</wicri:noRegion>
</affiliation>
</author>
</analytic>
<series><title level="j">Healthcare Informatics Research</title>
<idno type="ISSN">2093-3681</idno>
<idno type="eISSN">2093-369X</idno>
<imprint><date when="2017">2017</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><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>
</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>
<affiliations><list><country><li>Corée du Sud</li>
</country>
<region><li>Région capitale de Séoul</li>
</region>
<settlement><li>Séoul</li>
</settlement>
</list>
<tree><country name="Corée du Sud"><region name="Région capitale de Séoul"><name sortKey="Seo, Dong Woo" sort="Seo, Dong Woo" uniqKey="Seo D" first="Dong-Woo" last="Seo">Dong-Woo Seo</name>
</region>
<name sortKey="Shin, Soo Yong" sort="Shin, Soo Yong" uniqKey="Shin S" first="Soo-Yong" last="Shin">Soo-Yong Shin</name>
</country>
</tree>
</affiliations>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/MersV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000C96 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000C96 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Sante |area= MersV1 |flux= Main |étape= Exploration |type= RBID |clé= PMC:5688036 |texte= Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks }}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:29181246" \ | HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \ | NlmPubMed2Wicri -a MersV1
This area was generated with Dilib version V0.6.33. |