ArticleJournal of medical Internet research2014
Cumulative query method for influenza surveillance using search engine data.
Article in Journal of medical Internet research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.Journal of medical Internet research · 2023Article
- Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.Frontiers in public health · 2023Article
- Searching for HIV and AIDS Health Information in South Africa, 2004-2019: Analysis of Google and Wikipedia Search Trends.JMIR formative research · 2022Article
- Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives.Frontiers in public health · 2022Article
- Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study.Journal of medical Internet research · 2020Article
- Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis.Healthcare informatics research · 2020Article
- Infodemiology and Infoveillance: Scoping Review.Journal of medical Internet research · 2020Article
- Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index.International journal of environmental research and public health · 2020Article
- Using big data to predict pertussis infections in Jinan city, China: a time series analysis.International journal of biometeorology · 2020Article
- Heartburn-Related Internet Searches and Trends of Interest across Six Western Countries: A Four-Year Retrospective Analysis Using Google Ads Keyword Planner.International journal of environmental research and public health · 2019Article
- Association of sociodemographic factors and internet query data with pertussis infections in Shandong, China.Epidemiology and infection · 2019Article
- Article
- Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches.JMIR public health and surveillance · 2017Article
- Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks.Healthcare informatics research · 2017Article
- Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data.Journal of medical Internet research · 2017Article
- Using Web-Based Search Data to Study the Public's Reactions to Societal Events: The Case of the Sandy Hook Shooting.JMIR public health and surveillance · 2017Article
- Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis.Journal of medical Internet research · 2016Article
- High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea.Scientific reports · 2016Article
- Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea.Journal of medical Internet research · 2016Article
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundInternet search queries have become an important data source in syndromic surveillance system. However, there is currently no syndromic surveillance system using Internet search query data in South Korea.
objectivesThe objective of this study was to examine correlations between our cumulative query method and national influenza surveillance data.
methodsOur study was based on the local search engine, Daum (approximately 25% market share), and influenza-like illness (ILI) data from the Korea Centers for Disease Control and Prevention. A quota sampling survey was conducted with 200 participants to obtain popular queries. We divided the study period into two sets: Set 1 (the 2009/10 epidemiological year for development set 1 and 2010/11 for validation set 1) and Set 2 (2010/11 for development Set 2 and 2011/12 for validation Set 2). Pearson's correlation coefficients were calculated between the Daum data and the ILI data for the development set. We selected the combined queries for which the correlation coefficients were .7 or higher and listed them in descending order. Then, we created a cumulative query method n representing the number of cumulative combined queries in descending order of the correlation coefficient.
resultsIn validation set 1, 13 cumulative query methods were applied, and 8 had higher correlation coefficients (min=.916, max=.943) than that of the highest single combined query. Further, 11 of 13 cumulative query methods had an r value of ≥.7, but 4 of 13 combined queries had an r value of ≥.7. In validation set 2, 8 of 15 cumulative query methods showed higher correlation coefficients (min=.975, max=.987) than that of the highest single combined query. All 15 cumulative query methods had an r value of ≥.7, but 6 of 15 combined queries had an r value of ≥.7.
conclusionsCumulative query method showed relatively higher correlation with national influenza surveillance data than combined queries in the development and validation set.
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