Background: New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.
Methods: We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed.
Results: Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10- 6; cough: rs=0.592, p=4.485× 10- 4; fatigue: rs=0.629, p=1.494× 10- 4; sputum production: rs=0.648, p=8.206× 10- 5; shortness of breath: rs=0.656, p=6.182× 10-5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value's optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever.
Conclusion: The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public's attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.
Keywords: Baidu index; COVID-19; Internet searching; Web-based data.