ArticleEuro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin2020
Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season.
Article in Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Viral infections and invasive group A streptococcal disease incidence during 2022-2023 - authors' reply.The Lancet regional health. Europe · 2026Article
- Paediatric invasive group A streptococcal infections and associations with viral infections in 15 European countries after lifting non-pharmaceutical interventions against SARS-CoV-2: an interrupted time-series analysis.The Lancet regional health. Europe · 2025Article
- Developing predictive models for COVID-19 positive tests based on the XGBoost and random forest algorithms with internet search data.BMC public health · 2025Article
- Differences in Fear and Negativity Levels Between Formal and Informal Health-Related Websites: Analysis of Sentiments and Emotions.Journal of medical Internet research · 2024Article
- The prediction of influenza-like illness using national influenza surveillance data and Baidu query data.BMC public health · 2024Article
- Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.Journal of medical Internet research · 2023Article
- Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review.International journal of environmental research and public health · 2022Review
- Correlation between national surveillance and search engine query data on respiratory syncytial virus infections in Japan.BMC public health · 2022Article
- Early Warning of Infectious Diseases in Hospitals Based on Multi-Self-Regression Deep Neural Network.Journal of healthcare engineering · 2022Article
- Analyzing Citizens' and Health Care Professionals' Searches for Smell/Taste Disorders and Coronavirus in Finland During the COVID-19 Pandemic: Infodemiological Approach Using Database Logs.JMIR public health and surveillance · 2021Article
- Novel Methods in the Surveillance of Influenza-Like Illness in Germany Using Data From a Symptom Assessment App (Ada): Observational Case Study.JMIR public health and surveillance · 2021Article
- Digital Surveillance Through an Online Decision Support Tool for COVID-19 Over One Year of the Pandemic in Italy: Observational Study.Journal of medical Internet research · 2021Observational
- High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021Article
- Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study.JMIR medical informatics · 2021Article
- Trends of Online Search of COVID-19 Related Terms in Cyprus.Epidemiologia (Basel, Switzerland) · 2021Article
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Authors and funding
7 authors.
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Abstract
BackgroundDespite the early development of Google Flu Trends in 2009, standards for digital epidemiology methods have not been established and research from European countries is scarce.AimIn this article, we study the use of web search queries to monitor influenza-like illness (ILI) rates in the Netherlands in real time.MethodsIn this retrospective analysis, we simulated the weekly use of a prediction model for estimating the then-current ILI incidence across the 2017/18 influenza season solely based on Google search query data. We used weekly ILI data as reported to The European Surveillance System (TESSY) each week, and we removed the then-last 4 weeks from our dataset. We then fitted a prediction model based on the then-most-recent search query data from Google Trends to fill the 4-week gap ('Nowcasting'). Lasso regression, in combination with cross-validation, was applied to select predictors and to fit the 52 models, one for each week of the season.ResultsThe models provided accurate predictions with a mean and maximum absolute error of 1.40 (95% confidence interval: 1.09-1.75) and 6.36 per 10,000 population. The onset, peak and end of the epidemic were predicted with an error of 1, 3 and 2 weeks, respectively. The number of search terms retained as predictors ranged from three to five, with one keyword, 'griep' ('flu'), having the most weight in all models.DiscussionThis study demonstrates the feasibility of accurate, real-time ILI incidence predictions in the Netherlands using Google search query data.
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