An Infodemiological Analysis of Google Trends in COVID-19 Outbreak: Predict Case Numbers and Attitudes of Different Societies

摘要

A new type of coronavirus (COVID-19), detected at the end of December 2019 in Wuhan, China, can pass from person to person, spreading very quickly. The COVID-19 outbreak has created stress among societies. This study aims to evaluate the usability of Google Trends data in predicting and modeling the COVID-19 outbreak and the attitudes of different societies to it by using an infodemiological method. The authors collected the search words related to coronavirus and their relative search volume (RSV) from 11 different countries affected by the COVID-19 outbreak from Google Trends. A positive correlation was found between the trend rate of the words searched on the internet and the number of COVID-19 cases in countries related to the COVID-19 outbreak (p＜0.05). There was a significant difference between 11 country societies in the daily RSV for the COVID-19 outbreak (p＜0.05). The Turkish, South Korean, Iranian, and Swiss society have searched more intensely on the internet for COVID-19 than others. The research shows that Google Trends data can be used to build the forecast model for case numbers in the COVID-19 outbreak. Besides, Google Trends data provides information about different societies' attitudes in the COVID-19 outbreak.