Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
Identifieur interne : 000852 ( Pmc/Curation ); précédent : 000851; suivant : 000853Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
Auteurs : Cuilian Li [République populaire de Chine] ; Li Jia Chen [République populaire de Chine] ; Xueyu Chen [République populaire de Chine] ; Mingzhi Zhang [République populaire de Chine] ; Chi Pui Pang [République populaire de Chine] ; Haoyu Chen [République populaire de Chine]Source :
- Eurosurveillance [ 1025-496X ] ; 2020.
Abstract
The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10–14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. The lag correlations also showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases.
Url:
DOI: 10.2807/1560-7917.ES.2020.25.10.2000199
PubMed: 32183935
PubMed Central: 7078825
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<front><div type="abstract" xml:lang="en"><p>The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10–14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. The lag correlations also showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases.</p>
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<publisher><publisher-name>European Centre for Disease Prevention and Control (ECDC)</publisher-name>
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<article-id pub-id-type="publisher-id">2000199</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Rapid Communication</subject>
</subj-group>
</article-categories>
<title-group><article-title>Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Li</surname>
<given-names>Cuilian</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chen</surname>
<given-names>Li Jia</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chen</surname>
<given-names>Xueyu</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zhang</surname>
<given-names>Mingzhi</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Pang</surname>
<given-names>Chi Pui</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
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<contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">http://orcid.org/0000-0003-0676-4610</contrib-id>
<name><surname>Chen</surname>
<given-names>Haoyu</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<aff id="aff1"><label>1</label>
Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, China</aff>
<aff id="aff2"><label>2</label>
Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong, China</aff>
</contrib-group>
<author-notes><fn id="afn1"><p>Correspondence: Haoyu Chen (<email xlink:href="drchenhaoyu@gmail.com">drchenhaoyu@gmail.com</email>
)</p>
</fn>
</author-notes>
<pub-date pub-type="ppub"><day>12</day>
<month>3</month>
<year>2020</year>
</pub-date>
<volume>25</volume>
<issue>10</issue>
<elocation-id>2000199</elocation-id>
<history><date date-type="received"><day>26</day>
<month>2</month>
<year>2020</year>
</date>
<date date-type="accepted"><day>09</day>
<month>3</month>
<year>2020</year>
</date>
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<permissions><copyright-statement>This article is copyright of the authors or their affiliated institutions, 2020.</copyright-statement>
<copyright-year>2020</copyright-year>
<copyright-holder>The authors or their affiliated institutions</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.</license-p>
</license>
</permissions>
<abstract><p>The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10–14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. The lag correlations also showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases.</p>
</abstract>
<kwd-group><title>Keywords: </title>
<kwd>COVID-19</kwd>
<kwd>coronavirus</kwd>
<kwd>Internet surveillance</kwd>
<kwd>Google Trends</kwd>
<kwd>Baidu Index</kwd>
<kwd>Weibo Index</kwd>
</kwd-group>
<custom-meta-group><custom-meta><meta-name>sequence</meta-name>
<meta-value>2</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
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