This study is a statistical analysis examining the relationship between Google Trend and Daily Trend of the number of new COVID-19 cases in the United States using single term versus selecting 5 terms per day and taking time-lag into account. Google Trend was used to measure the interest of users in search terms over time during the pandemic of COVID-19. To make up for the lack of open datasets of search queries, tweets filtered by “COVID-19” related hashtags were used to generate top-5 terms per day according to term frequency on a 7-day moving average. Spearman correlation analysis was applied to examine the correlations between the two trends and examine the impact of time-lag. The results showed high correlation between the two trends by selecting 5 terms per day. Besides, taking time-lag into account improved the correlation between the difference of search interest and the number of new COVID-19 cases. The results indicate that it is important to account for the fact that covid-related language may change over time and time-lag should be considered as an important factor when conducting trend analysis. This study demonstrates the possibility of using Google Trend to predict future case trend of COVID-19 in areas with a large population of Web search users.Master of Science in Information Scienc
