[Using the big data ofinternet to understand coronavirus disease 2019's symptom characteristics: a big data study].
Identifieur interne : 000355 ( PubMed/Curation ); précédent : 000354; suivant : 000356[Using the big data ofinternet to understand coronavirus disease 2019's symptom characteristics: a big data study].
Auteurs : H J Qiu [République populaire de Chine] ; L X Yuan [République populaire de Chine] ; X K Huang [République populaire de Chine] ; Y Q Zhou [République populaire de Chine] ; Q W Wu [République populaire de Chine] ; R. Zheng [République populaire de Chine] ; Q T Yang [République populaire de Chine]Source :
- Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery [ 1673-0860 ] ; 2020.
Abstract
Objective: Analyzing the symptom characteristics of Coronavirus Disease 2019(COVID-19) to improve its prevention. Methods: Using Baidu Index Platform (http://index.baidu.com) and the website of Chinese Center for Disease Control and Prevention as data resources to obtain the search volume (SV) of keywords for symptoms associated with COVID-19 from January 1 to February 20 in each year from 2017 to 2020, in Hubei province and other top 10 impacted provinces in China and the epidemic data. Data of 2020 were compared with the previous three years. Data of Hubei province were compared with confirmed cases. The differences and characteristics of the SV of COVID-19-related symptoms, and the correlation between the SV of COVID-19 and new confirmed or suspected cases were analyzed and the hysteresis effects were discussed. Results: Compared the data from January 1 to February 20, 2020, with the SV for the same period of previous three years, Hubei's SV for cough, fever, diarrhea, chest tightness, dyspnea and other symptoms were significantly increased. The total SV of lower respiratory symptoms was significantly higher than that of upper respiratory symptoms (P<0.001). The SV of COVID-19 in Hubei province was significantly correlated with new confirmed or suspected cases (R(confirmed) = 0.723, R(suspected) = 0.863, all P < 0.001). The results of the distributed lag model suggested that the patients who retrieved relevant symptoms on the Internet may begin to see a doctor in 2-3 days later and be diagnosed in 3-4 days later. Conclusions: The total SV of lower respiratory symptoms is higher than that of upper respiratory symptoms, and the SV of diarrhea also increased significantly. It warns us to pay attention to not only the symptoms of lower respiratory tract, but also the gastrointestinal symptoms, especially diarrhea in patients with COVID-19. There is a relationship between Internet retrieval behavior and the number of new confirmed or suspected cases. Big data has a certain role in the early warning of infectious diseases.
DOI: 10.3760/cma.j.cn115330-20200225-00128
PubMed: 32186171
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">[Using the big data ofinternet to understand coronavirus disease 2019's symptom characteristics: a big data study].</title><author><name sortKey="Qiu, H J" sort="Qiu, H J" uniqKey="Qiu H" first="H J" last="Qiu">H J Qiu</name><affiliation wicri:level="1"><nlm:affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Yuan, L X" sort="Yuan, L X" uniqKey="Yuan L" first="L X" last="Yuan">L X Yuan</name><affiliation wicri:level="1"><nlm:affiliation>Department of Science and Research, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Science and Research, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Huang, X K" sort="Huang, X K" uniqKey="Huang X" first="X K" last="Huang">X K Huang</name><affiliation wicri:level="1"><nlm:affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Zhou, Y Q" sort="Zhou, Y Q" uniqKey="Zhou Y" first="Y Q" last="Zhou">Y Q Zhou</name><affiliation wicri:level="1"><nlm:affiliation>Department of Pulmonary and Critical Care Medicine, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Pulmonary and Critical Care Medicine, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Wu, Q W" sort="Wu, Q W" uniqKey="Wu Q" first="Q W" last="Wu">Q W Wu</name><affiliation wicri:level="1"><nlm:affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Zheng, R" sort="Zheng, R" uniqKey="Zheng R" first="R" last="Zheng">R. Zheng</name><affiliation wicri:level="1"><nlm:affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Yang, Q T" sort="Yang, Q T" uniqKey="Yang Q" first="Q T" last="Yang">Q T Yang</name><affiliation wicri:level="1"><nlm:affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32186171</idno><idno type="pmid">32186171</idno><idno type="doi">10.3760/cma.j.cn115330-20200225-00128</idno><idno type="wicri:Area/PubMed/Corpus">000355</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000355</idno><idno type="wicri:Area/PubMed/Curation">000355</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000355</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">[Using the big data ofinternet to understand coronavirus disease 2019's symptom characteristics: a big data study].</title><author><name sortKey="Qiu, H J" sort="Qiu, H J" uniqKey="Qiu H" first="H J" last="Qiu">H J Qiu</name><affiliation wicri:level="1"><nlm:affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Yuan, L X" sort="Yuan, L X" uniqKey="Yuan L" first="L X" last="Yuan">L X Yuan</name><affiliation wicri:level="1"><nlm:affiliation>Department of Science and Research, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Science and Research, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Huang, X K" sort="Huang, X K" uniqKey="Huang X" first="X K" last="Huang">X K Huang</name><affiliation wicri:level="1"><nlm:affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Zhou, Y Q" sort="Zhou, Y Q" uniqKey="Zhou Y" first="Y Q" last="Zhou">Y Q Zhou</name><affiliation wicri:level="1"><nlm:affiliation>Department of Pulmonary and Critical Care Medicine, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Pulmonary and Critical Care Medicine, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Wu, Q W" sort="Wu, Q W" uniqKey="Wu Q" first="Q W" last="Wu">Q W Wu</name><affiliation wicri:level="1"><nlm:affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Zheng, R" sort="Zheng, R" uniqKey="Zheng R" first="R" last="Zheng">R. Zheng</name><affiliation wicri:level="1"><nlm:affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author><author><name sortKey="Yang, Q T" sort="Yang, Q T" uniqKey="Yang Q" first="Q T" last="Yang">Q T Yang</name><affiliation wicri:level="1"><nlm:affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630</wicri:regionArea></affiliation></author></analytic><series><title level="j">Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery</title><idno type="ISSN">1673-0860</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><b>Objective:</b> Analyzing the symptom characteristics of Coronavirus Disease 2019(COVID-19) to improve its prevention. <b>Methods:</b> Using Baidu Index Platform (http://index.baidu.com) and the website of Chinese Center for Disease Control and Prevention as data resources to obtain the search volume (SV) of keywords for symptoms associated with COVID-19 from January 1 to February 20 in each year from 2017 to 2020, in Hubei province and other top 10 impacted provinces in China and the epidemic data. Data of 2020 were compared with the previous three years. Data of Hubei province were compared with confirmed cases. The differences and characteristics of the SV of COVID-19-related symptoms, and the correlation between the SV of COVID-19 and new confirmed or suspected cases were analyzed and the hysteresis effects were discussed. <b>Results:</b> Compared the data from January 1 to February 20, 2020, with the SV for the same period of previous three years, Hubei's SV for cough, fever, diarrhea, chest tightness, dyspnea and other symptoms were significantly increased. The total SV of lower respiratory symptoms was significantly higher than that of upper respiratory symptoms (P<0.001). The SV of COVID-19 in Hubei province was significantly correlated with new confirmed or suspected cases (R(confirmed) = 0.723, R(suspected) = 0.863, all P < 0.001). The results of the distributed lag model suggested that the patients who retrieved relevant symptoms on the Internet may begin to see a doctor in 2-3 days later and be diagnosed in 3-4 days later. <b>Conclusions:</b> The total SV of lower respiratory symptoms is higher than that of upper respiratory symptoms, and the SV of diarrhea also increased significantly. It warns us to pay attention to not only the symptoms of lower respiratory tract, but also the gastrointestinal symptoms, especially diarrhea in patients with COVID-19. There is a relationship between Internet retrieval behavior and the number of new confirmed or suspected cases. Big data has a certain role in the early warning of infectious diseases.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32186171</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>18</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">1673-0860</ISSN><JournalIssue CitedMedium="Print"><Volume>55</Volume><Issue>0</Issue><PubDate><Year>2020</Year><Month>Mar</Month><Day>18</Day></PubDate></JournalIssue><Title>Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery</Title><ISOAbbreviation>Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi</ISOAbbreviation></Journal><ArticleTitle>[Using the big data ofinternet to understand coronavirus disease 2019's symptom characteristics: a big data study].</ArticleTitle><Pagination><MedlinePgn>E004</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3760/cma.j.cn115330-20200225-00128</ELocationID><Abstract><AbstractText><b>Objective:</b> Analyzing the symptom characteristics of Coronavirus Disease 2019(COVID-19) to improve its prevention. <b>Methods:</b> Using Baidu Index Platform (http://index.baidu.com) and the website of Chinese Center for Disease Control and Prevention as data resources to obtain the search volume (SV) of keywords for symptoms associated with COVID-19 from January 1 to February 20 in each year from 2017 to 2020, in Hubei province and other top 10 impacted provinces in China and the epidemic data. Data of 2020 were compared with the previous three years. Data of Hubei province were compared with confirmed cases. The differences and characteristics of the SV of COVID-19-related symptoms, and the correlation between the SV of COVID-19 and new confirmed or suspected cases were analyzed and the hysteresis effects were discussed. <b>Results:</b> Compared the data from January 1 to February 20, 2020, with the SV for the same period of previous three years, Hubei's SV for cough, fever, diarrhea, chest tightness, dyspnea and other symptoms were significantly increased. The total SV of lower respiratory symptoms was significantly higher than that of upper respiratory symptoms (P<0.001). The SV of COVID-19 in Hubei province was significantly correlated with new confirmed or suspected cases (R(confirmed) = 0.723, R(suspected) = 0.863, all P < 0.001). The results of the distributed lag model suggested that the patients who retrieved relevant symptoms on the Internet may begin to see a doctor in 2-3 days later and be diagnosed in 3-4 days later. <b>Conclusions:</b> The total SV of lower respiratory symptoms is higher than that of upper respiratory symptoms, and the SV of diarrhea also increased significantly. It warns us to pay attention to not only the symptoms of lower respiratory tract, but also the gastrointestinal symptoms, especially diarrhea in patients with COVID-19. There is a relationship between Internet retrieval behavior and the number of new confirmed or suspected cases. Big data has a certain role in the early warning of infectious diseases.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Qiu</LastName><ForeName>H J</ForeName><Initials>HJ</Initials><AffiliationInfo><Affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yuan</LastName><ForeName>L X</ForeName><Initials>LX</Initials><AffiliationInfo><Affiliation>Department of Science and Research, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>X K</ForeName><Initials>XK</Initials><AffiliationInfo><Affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhou</LastName><ForeName>Y Q</ForeName><Initials>YQ</Initials><AffiliationInfo><Affiliation>Department of Pulmonary and Critical Care Medicine, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Q W</ForeName><Initials>QW</Initials><AffiliationInfo><Affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zheng</LastName><ForeName>R</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Q T</ForeName><Initials>QT</Initials><AffiliationInfo><Affiliation>Department of OtorhinolaryngologyHead and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>chi</Language><PublicationTypeList><PublicationType UI="D004740">English Abstract</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>03</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>China</Country><MedlineTA>Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi</MedlineTA><NlmUniqueID>101247574</NlmUniqueID><ISSNLinking>1673-0860</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><OtherAbstract Type="Publisher" Language="chi"><AbstractText><b>目的：</b> 探索新型冠状病毒肺炎（新冠肺炎）的症状特征，提高新冠肺炎防控意识。 <b>方法：</b> 通过互联网搜索引擎收录的关键词检索数据（百度指数）及中国疾病预防控制中心公布的疫情相关数据，选取2017—2020年每年1月1日至2月20日期间新冠肺炎相关症状关键词的搜索量及2020年1月20日至3月5日的新增确诊及新增疑似病例数，分析新冠肺炎相关症状的搜索量差异及其特征，分析新冠肺炎百度指数与新增确诊、新增疑似的相关性，并探讨网络检索热度的滞后效应。2020年的数据与2017—2019年的数据进行比较，湖北省的数据与确诊人数第2至第10位的9个省份的数据进行比较。 <b>结果：</b> 将2020年1月20日至2月20日期间的搜索量与往年（2017—2019年）同期的搜索量均值相比，发现湖北省民众对咳嗽、发热、腹泻、胸闷、呼吸困难等症状的搜索量大幅升高。下呼吸道症状的总检索量明显高于上呼吸道症状的总检索量（<i>P</i><0.001）。湖北省新冠肺炎百度指数与新增确诊及新增疑似具有明显相关性（<i>r</i>(s确诊)=0.723，<i>r</i>(s疑似)=0.863，<i>P</i>值均<0.001）。分布滞后模型结果提示在互联网上检索相关症状的感染者可能在后来的2～3 d左右开始就诊并成为疑似病例，3～4 d左右确诊。 <b>结论：</b> 疫情期间湖北省民众对下呼吸道症状的总检索量较上呼吸道症状的总检索量增多，而且腹泻症状的检索量亦显著升高，警示我们除了关注下呼吸道症状，也应该重视新冠肺炎患者腹泻等消化道症状。互联网检索大数据与新增确诊数、新增疑似数存在相关性，提示其对于传染性疾病具有一定的预警作用。.</AbstractText></OtherAbstract><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">2019 novel coronavirus</Keyword><Keyword MajorTopicYN="N">Baidu index</Keyword><Keyword MajorTopicYN="N">Big data</Keyword><Keyword MajorTopicYN="N">Coronavirus Disease 2019</Keyword><Keyword MajorTopicYN="N">Internet</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32186171</ArticleId><ArticleId IdType="doi">10.3760/cma.j.cn115330-20200225-00128</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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