Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Mar 22, 2020
Date Accepted: Jun 13, 2020
Date Submitted to PubMed: Jun 16, 2020
Public Health Emergency, Health Literacy and Social Panic: Mapping of COVID-19 Based on the Web Search Data
ABSTRACT
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.
Citation
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Copyright
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