MersV1, PubMed, Corpus, bibRecord, 000A68

Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks.

Identifieur interne : 000A68 ( PubMed/Corpus ); précédent : 000A67; suivant : 000A69

Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks.

Auteurs : Dong-Woo Seo ; Soo-Yong Shin

Source :

Healthcare informatics research [ 2093-3681 ] ; 2017.

RBID : pubmed:29181246

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.

DOI: 10.4258/hir.2017.23.4.343
PubMed: 29181246

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pubmed:29181246

Le document en format XML

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<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="Shin, Soo Yong" sort="Shin, Soo Yong" uniqKey="Shin S" first="Soo-Yong" last="Shin">Soo-Yong Shin</name>
<affiliation><nlm:affiliation>Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea.</nlm:affiliation>
</affiliation>
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<sourceDesc><biblStruct><analytic><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><nlm:affiliation>Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.</nlm:affiliation>
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<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 Computer Science and Engineering, Kyung Hee University, Yongin, Korea.</nlm:affiliation>
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<series><title level="j">Healthcare informatics research</title>
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<front><div type="abstract" xml:lang="en">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.</div>
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<pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">29181246</PMID>
<DateRevised><Year>2019</Year>
<Month>11</Month>
<Day>20</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">2093-3681</ISSN>
<JournalIssue CitedMedium="Print"><Volume>23</Volume>
<Issue>4</Issue>
<PubDate><Year>2017</Year>
<Month>Oct</Month>
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<Title>Healthcare informatics research</Title>
<ISOAbbreviation>Healthc Inform Res</ISOAbbreviation>
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<ArticleTitle>Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks.</ArticleTitle>
<Pagination><MedlinePgn>343-348</MedlinePgn>
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<Abstract><AbstractText Label="Objectives" NlmCategory="UNASSIGNED">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.</AbstractText>
<AbstractText Label="Methods" NlmCategory="UNASSIGNED">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).</AbstractText>
<AbstractText Label="Results" NlmCategory="UNASSIGNED">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.</AbstractText>
<AbstractText Label="Conclusions" NlmCategory="UNASSIGNED">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.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><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>Shin</LastName>
<ForeName>Soo-Yong</ForeName>
<Initials>SY</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea.</Affiliation>
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<Language>eng</Language>
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<ArticleDate DateType="Electronic"><Year>2017</Year>
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<MedlineJournalInfo><Country>Korea (South)</Country>
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<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Digital Syndromic Surveillance System</Keyword>
<Keyword MajorTopicYN="N">Disease Outbreak</Keyword>
<Keyword MajorTopicYN="N">Search Engine</Keyword>
<Keyword MajorTopicYN="N">Social Media</Keyword>
</KeywordList>
<CoiStatement>Conflict of Interest: No potential conflict of interest relevant to this article was reported.</CoiStatement>
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Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks.

Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks.

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Abstract

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