Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu Index 

Abstract

Background: New coronavirus disease 2019 (COVID-19) poses a severe threat to human life and causes a global pandemic. The purpose of current research is 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 c, 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 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 symptoms presented a robust positive correlation 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 is 19.8 days in China. The daily Baidu Index value's optimal time lags were the fourth day for cough, third day for fatigue, firth day for sputum production, firth day for shortness of breath, and 0 days for fever. Conclusion: Search terms of COVID-19-related symptoms on the Baidu search engine have significant correlations with confirmed cases. Since the Baidu search engine can reflect the Public's attention to the pandemic and 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.