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.

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HfdSelect -h $EXPLOR_AREA/Data/an2020/Analysis/biblio.hfd -nk 000010 | SxmlIndent | more

{{Explor lien
   |wiki=    Wicri/Sante
   |area=    CovidV2
   |flux=    an2020
   |étape=   Analysis
   |type=    RBID
   |clé=     pubmed:32186171
   |texte=   [Using the big data ofinternet to understand coronavirus disease 2019's symptom characteristics: a big data study].
}}

HfdIndexSelect -h $EXPLOR_AREA/Data/an2020/Analysis/RBID.i   -Sk "pubmed:32186171" \
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       | NlmPubMed2Wicri -a CovidV2