ArticlePloS one2013
Correlation between national influenza surveillance data and google trends in South Korea.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 3 of them syntheses that pooled it.
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Who cites it
58 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.Journal of medical Internet research · 2018Pooled it
- Pooled it
- Effects of direct-to-consumer alcohol home delivery policies on alcohol-related online searches in the United States from 2019 to 2023: A Google Trends study.Preventive medicine reports · 2025Article
- Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.Journal of clinical medicine · 2024Article
- Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning.Scientific reports · 2023Article
- Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study.Tropical medicine and infectious disease · 2023Article
- Seasonal Variation for Plantar Fasciitis: Evidence from Google Trends Search Query Data.Healthcare (Basel, Switzerland) · 2022Article
- United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study.JMIR public health and surveillance · 2022Article
- Article
- Subjective well-being during the 2020-21 global coronavirus pandemic: Evidence from high frequency time series data.PloS one · 2022Article
- Suggestion of a simpler and faster influenza-like illness surveillance system using 2014-2018 claims data in Korea.Scientific reports · 2021Article
- Global Dieting Trends and Seasonality: Social Big-Data Analysis May Be a Useful Tool.Nutrients · 2021Article
- Examining the Correlation of Google Influenza Trend with Hospital Data: Retrospective Study.Journal of multidisciplinary healthcare · 2021Article
- Exploring online search behavior for COVID-19 preventive measures: The Philippine case.PloS one · 2021Article
- Dental Challenges and the Needs of the Population during the Covid-19 Pandemic Period. Real-Time Surveillance Using Google Trends.International journal of environmental research and public health · 2020Article
- [Medical applications of social networks. Specific aspects of the COVID-19 pandemic].Medicine · 2020Article
- Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations.Journal of medical Internet research · 2020Article
- Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.International journal of environmental research and public health · 2020Article
- Population's health information-seeking behaviors and geographic variations of stroke in Malaysia: an ecological correlation and time series study.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn 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.
methodsOur 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.
resultsThe 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).
conclusionsIn 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.
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