ArticlePLoS computational biology2019
Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- Real-time forecasting of data revisions in epidemic surveillance streams.PLoS computational biology · 2025Article
- Source Robust Non-Parametric Reconstruction of Epidemic-like Event-Based Network Diffusion Processes Under Online Data.Big data and cognitive computing · 2025Article
- Ensemble approaches for short-term dengue fever forecasts: A global evaluation study.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Predicting seasonal influenza outbreaks with regime shift-informed dynamics for improved public health preparedness.Scientific reports · 2024Article
- Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.Frontiers in public health · 2024Article
- Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States.Epidemics · 2023Article
- The Effect of Nonpharmaceutical Interventions Implemented in Response to the COVID-19 Pandemic on Seasonal Respiratory Syncytial Virus: Analysis of Google Trends Data.Journal of medical Internet research · 2022Article
- Addressing delayed case reporting in infectious disease forecast modeling.PLoS computational biology · 2022Article
- Fast and accurate influenza forecasting in the United States with Inferno.PLoS computational biology · 2022Article
- Adaptively stacking ensembles for influenza forecasting.Statistics in medicine · 2021Article
- Multiscale influenza forecasting.Nature communications · 2021Article
- Improving probabilistic infectious disease forecasting through coherence.PLoS computational biology · 2021Article
- Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study.JMIR public health and surveillance · 2020Observational
- Google Health Trends performance reflecting dengue incidence for the Brazilian states.BMC infectious diseases · 2020Article
- FluSense: A Contactless Syndromic Surveillance Platform for Influenza-Like Illness in Hospital Waiting Areas.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2020Article
- Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model.International journal of environmental research and public health · 2020Article
- Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data.PLoS computational biology · 2019Article
- On the multibin logarithmic score used in the FluSight competitions.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Estimating influenza incidence using search query deceptiveness and generalized ridge regression.PLoS computational biology · 2019Article
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3 authors.
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Abstract
The ability to produce timely and accurate flu forecasts in the United States can significantly impact public health. Augmenting forecasts with internet data has shown promise for improving forecast accuracy and timeliness in controlled settings, but results in practice are less convincing, as models augmented with internet data have not consistently outperformed models without internet data. In this paper, we perform a controlled experiment, taking into account data backfill, to improve clarity on the benefits and limitations of augmenting an already good flu forecasting model with internet-based nowcasts. Our results show that a good flu forecasting model can benefit from the augmentation of internet-based nowcasts in practice for all considered public health-relevant forecasting targets. The degree of forecast improvement due to nowcasting, however, is uneven across forecasting targets, with short-term forecasting targets seeing the largest improvements and seasonal targets such as the peak timing and intensity seeing relatively marginal improvements. The uneven forecasting improvements across targets hold even when "perfect" nowcasts are used. These findings suggest that further improvements to flu forecasting, particularly seasonal targets, will need to derive from other, non-nowcasting approaches.
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