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Correlation between National Influenza Surveillance Data and Google Trends in South Korea
Authors:Sungjin Cho  Chang Hwan Sohn  Min Woo Jo  Soo-Yong Shin  Jae Ho Lee  Seoung Mok Ryoo  Won Young Kim  Dong-Woo Seo
Institution:1. Department of Emergency Medicine, University of Ulsan, College of Medicine, Asan Medical Center, Seoul, South Korea.; 2. Department of Preventive Medicine, University of Ulsan, College of Medicine, Asan Medical Center, Seoul, South Korea.; 3. Department of Biomedical Informatics, University of Ulsan, College of Medicine, Asan Medical Center, Seoul, South Korea.; National Institutes of Health, United States of America,
Abstract:

Background

In South Korea, there is currently no syndromic surveillance system using internet search data, including Google Flu Trends. The purpose of this study was to investigate the correlation between national influenza surveillance data and Google Trends in South Korea.

Methods

Our study was based on a publicly available search engine database, Google Trends, using 12 influenza-related queries, from September 9, 2007 to September 8, 2012. National surveillance data were obtained from the Korea Centers for Disease Control and Prevention (KCDC) influenza-like illness (ILI) and virologic surveillance system. Pearson''s correlation coefficients were calculated to compare the national surveillance and the Google Trends data for the overall period and for 5 influenza seasons.

Results

The correlation coefficient between the KCDC ILI and virologic surveillance data was 0.72 (p<0.05). The highest correlation was between the Google Trends query of H1N1 and the ILI data, with a correlation coefficient of 0.53 (p<0.05), for the overall study period. When compared with the KCDC virologic data, the Google Trends query of bird flu had the highest correlation with a correlation coefficient of 0.93 (p<0.05) in the 2010-11 season. The following queries showed a statistically significant correlation coefficient compared with ILI data for three consecutive seasons: Tamiflu (r = 0.59, 0.86, 0.90, p<0.05), new flu (r = 0.64, 0.43, 0.70, p<0.05) and flu (r = 0.68, 0.43, 0.77, p<0.05).

Conclusions

In our study, we found that the Google Trends for certain queries using the survey on influenza correlated with national surveillance data in South Korea. The results of this study showed that Google Trends in the Korean language can be used as complementary data for influenza surveillance but was insufficient for the use of predictive models, such as Google Flu Trends.
Keywords:
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