ArticlePLoS computational biology2015
Forecasting the 2013-2014 influenza season using Wikipedia.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.
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Who cites it
71 citing papers in PubMed, 1 synthesis or guideline pooled it, 175 citations in OpenAlex.
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Mapping Incidence and Prevalence Peak Data for SIR Modeling Applications.Journal of mathematical biology · 2025Article
- A Deep Learning Framework for Using Search Engine Data to Predict Influenza-Like Illness and Distinguish Epidemic and Nonepidemic Seasons: Multifeature Time Series Analysis.Journal of medical Internet research · 2025Article
- Article
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- Long Short-term Memory-Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation.Journal of medical Internet research · 2023Article
- Using the Baidu index to predict trends in the incidence of tuberculosis in Jiangsu Province, China.Frontiers in public health · 2023Article
- Addressing delayed case reporting in infectious disease forecast modeling.PLoS computational biology · 2022Article
- Cloud-Based Influenza Surveillance System in Emergency Departments Using Molecular-Based Testing: Advances and Challenges.The western journal of emergency medicine · 2022Article
- Query-based-learning mortality-related decoders for the developed island economy.Scientific reports · 2022Article
- Rapid indicators of deprivation using grocery shopping data.Royal Society open science · 2021Article
- Cross-sectional telephone surveys as a tool to study epidemiological factors and monitor seasonal influenza activity in Malta.BMC public health · 2021Article
- Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019-2020.International journal of environmental research and public health · 2021Article
- Suggestion of a simpler and faster influenza-like illness surveillance system using 2014-2018 claims data in Korea.Scientific reports · 2021Article
- Multiscale influenza forecasting.Nature communications · 2021Article
- Assessing Public Interest Based on Wikipedia's Most Visited Medical Articles During the SARS-CoV-2 Outbreak: Search Trends Analysis.Journal of medical Internet research · 2021Article
- Correlation between flu and Wikipedia's pages visualization.Acta bio-medica : Atenei Parmensis · 2021Article
- Seasonality of Back Pain in Italy: An Infodemiology Study.International journal of environmental research and public health · 2021Article
- Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.PloS one · 2021Article
- Tracking and predicting U.S. influenza activity with a real-time surveillance network.PLoS computational biology · 2020Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.
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Registered trials
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.