ArticlePloS one2015
Age-related differences in the accuracy of web query-based predictions of influenza-like illness.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 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
- Evaluating Google Trends as a proxy for symptom incidence: insights from the winter COVID-19 infection study in England 2023/24.Epidemiology and infection · 2025Article
- Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.Journal of medical Internet research · 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
- Population's health information-seeking behaviors and geographic variations of stroke in Malaysia: an ecological correlation and time series study.Scientific reports · 2020Article
- Accurate spatiotemporal mapping of drug overdose deaths by machine learning of drug-related web-searches.PloS one · 2020Article
- Influence of Climate on Google Internet Searches for Pruritus Across 16 German Cities: Retrospective Analysis.Journal of medical Internet research · 2019Article
- Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts.Tropical medicine and infectious disease · 2019Article
- Article
- Is digital epidemiology reliable?-insight from updated cancer statistics.Annals of translational medicine · 2019Article
- A New Influenza-Tracking Smartphone App (Flu-Report) Based on a Self-Administered Questionnaire: Cross-Sectional Study.JMIR mHealth and uHealth · 2018Article
- Whiplash Syndrome Reloaded: Digital Echoes of Whiplash Syndrome in the European Internet Search Engine Context.JMIR public health and surveillance · 2017Article
- Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data.Influenza and other respiratory viruses · 2016Article
- Twitter Influenza Surveillance: Quantifying Seasonal Misdiagnosis Patterns and their Impact on Surveillance Estimates.Online journal of public health informatics · 2016Article
- Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWeb queries are now widely used for modeling, nowcasting and forecasting influenza-like illness (ILI). However, given that ILI attack rates vary significantly across ages, in terms of both magnitude and timing, little is known about whether the association between ILI morbidity and ILI-related queries is comparable across different age-groups. The present study aimed to investigate features of the association between ILI morbidity and ILI-related query volume from the perspective of age.
methodsSince Google Flu Trends is unavailable in Italy, Google Trends was used to identify entry terms that correlated highly with official ILI surveillance data. All-age and age-class-specific modeling was performed by means of linear models with generalized least-square estimation. Hold-out validation was used to quantify prediction accuracy. For purposes of comparison, predictions generated by exponential smoothing were computed.
resultsFive search terms showed high correlation coefficients of > .6. In comparison with exponential smoothing, the all-age query-based model correctly predicted the peak time and yielded a higher correlation coefficient with observed ILI morbidity (.978 vs. .929). However, query-based prediction of ILI morbidity was associated with a greater error. Age-class-specific query-based models varied significantly in terms of prediction accuracy. In the 0-4 and 25-44-year age-groups, these did well and outperformed exponential smoothing predictions; in the 15-24 and ≥ 65-year age-classes, however, the query-based models were inaccurate and highly overestimated peak height. In all but one age-class, peak timing predicted by the query-based models coincided with observed timing.
conclusionsThe accuracy of web query-based models in predicting ILI morbidity rates could differ among ages. Greater age-specific detail may be useful in flu query-based studies in order to account for age-specific features of the epidemiology of ILI.
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