LAUSR

BACKGROUND Coronavirus Disease 2019 (COVID-19) is a type of pneumonia caused by a novel coronavirus that was discovered in 2019. As of May 6, 2020, 84,407 COVID-19 cases and 4,643 COVID-19 related deaths have been confirmed in China. The Chinese population has expressed great concern since the COVID-19 outbreak. Meanwhile, an average of 1 billion people per day are using the Baidu search engine to find COVID-19 related health information. OBJECTIVE We conducted an infodemiological study to provide an analysis of the search features of COVID-19 based on the web search data. METHODS Using Baidu Index data, we assessed the search frequencies of specific search terms in Baidu to describe the impact of COVID-19 on public health, psychology, behaviors, lifestyles and social policies. RESULTS We summarized changes in web search data to provide our perspective and insights on the health literacy and social panic of the Chinese population during the early stages of the COVID-19 pandemic. CONCLUSIONS Our findings suggest that web search data may reflect changes in health literacy, social panic, and prevention/control policies in response to COVID-19. CLINICALTRIAL