High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea

Abstract

The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman correlation analysis. We found high correlations (>0.7) between Google search and Twitter results and the number of confirmed MERS cases for the previous three days using only four simple keywords: “MERS”, “” (“MERS (in Korean)”), “” (“MERS symptoms (in Korean)”), and “” (“MERS hospital (in Korean)”). Additionally, we found high correlations between the Google search and Twitter results and the number of quarantined cases using the above keywords. This study demonstrates the possibility of using a digital surveillance system to monitor the outbreak of MERS.

Applications of Google Search Trends for risk communication in infectious disease management: A case study of the COVID-19 outbreak in Taiwan

“…This moving average method used to facilitate the trends comparison among datasets. Time-lag correlation was utilized to assess whether J o u r n a l P r e -p r o o f the raised of GT data were correlated with the following increased of COVID-19 cases, as previously applied in other study (Shin et al, 2016).…”

Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020

“…Predicting the development of the outbreak as early and as reliably as possible is critical for action to prevent its spread. Internet searches and social media data have been reported to correlate with traditional surveillance data and can even predict the outbreak of disease epidemics several days or weeks earlier [4][5][6][7][8][9].…”

Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study

“…This observation further suggests that results of Twitter studies based on an English-only corpus may not always be generalizable to non-English-speaking Twitter users. Recent research identified significant correlation between the normalized volume of tweets with one of three Korean MERS-related keywords with number of laboratory-confirmed MERS cases in the outbreak [17]. Our study extended the existing literature on Twitter and MERS in Korea by going beyond the English and Korean languages by including tweets in Indonesian, Japanese and Thai.…”