ArticlePloS one2013
Influenza forecasting with Google Flu Trends.
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 130 papers, 4 of them syntheses that pooled it.
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Who cites it
130 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Pooled it
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Web-based infectious disease surveillance systems and public health perspectives: a systematic review.BMC public health · 2016Pooled it
- A systematic review of studies on forecasting the dynamics of influenza outbreaks.Influenza and other respiratory viruses · 2014Pooled it
- A real-time early warning system to anticipate respiratory disease outbreaks using transfer learning.Nature communications · 2026Article
- Viral emergence and pandemic preparedness in a One Health framework.Nature reviews. Microbiology · 2026Review
- 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
- On the importance of communication, advocacy, and information accuracy in destigmatizing mpox disease.Scientific reports · 2025Article
- An ensemble approach improves the prediction of the COVID-19 pandemic in South Korea.Journal of global health · 2025Article
- Impact of Demographic and Clinical Subgroups in Google Trends Data: Infodemiology Case Study on Asthma Hospitalizations.Journal of medical Internet research · 2025Article
- [Everything flows, nothing stands still-Anesthesiology and intensive care medicine in the digital age].Die Anaesthesiologie · 2025Review
- Cross-Correlation Analysis of Monthly Google Search Volume and Suicide in Taiwan, 2012-2022.Depression and anxiety · 2025Article
- Forecasting severe respiratory disease hospitalizations using machine learning algorithms.BMC medical informatics and decision making · 2024Article
- Integrating information from historical data into mechanistic models for influenza forecasting.PLoS computational biology · 2024Article
- Predicting seasonal influenza outbreaks with regime shift-informed dynamics for improved public health preparedness.Scientific reports · 2024Article
- REFORMS: Consensus-based Recommendations for Machine-learning-based Science.Science advances · 2024Review
- Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.Journal of clinical medicine · 2024Article
- Use of Telehealth Information for Early Detection: Insights From the COVID-19 Pandemic.American journal of public health · 2024Observational
- Quantitatively assessing early detection strategies for mitigating COVID-19 and future pandemics.Nature communications · 2023Article
- Real-time estimation of the effective reproduction number of COVID-19 from behavioral data.Scientific reports · 2023Article
70 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy.
methodsForecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information.
resultsA GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets.
conclusionsInteger-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.