ArticleJournal of the Royal Society, Interface2018
Evaluation of mechanistic and statistical methods in forecasting influenza-like illness.
Article in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Learning shared forecast-error structure to improve ensemble forecasts of seasonal respiratory outbreaks.medRxiv : the preprint server for health sciences · 2026Article
- Improving outbreak forecasts through model augmentation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Spatial and demographic heterogeneity in excess mortality in the United States, 2020-2023: a multimodel approach.American journal of epidemiology · 2025Article
- Machine Learning-Driven COVID-19 Hospitalization Forecasting: From Theory to Practice in a Major Northeastern Academic Medical Center.Open forum infectious diseases · 2025Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Predictive modeling of biomedical temporal data in healthcare applications: review and future directions.Frontiers in physiology · 2024Review
- Modeling Global Monkeypox Infection Spread Data: A Comparative Study of Time Series Regression and Machine Learning Models.Current microbiology · 2023Article
- Predicting Seasonal Influenza Hospitalizations Using an Ensemble Super Learner: A Simulation Study.American journal of epidemiology · 2023Article
- Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization.PloS one · 2023Article
- Modeling and Forecasting Monkeypox Cases Using Stochastic Models.Journal of clinical medicine · 2022Article
- Forecasting new diseases in low-data settings using transfer learning.Chaos, solitons, and fractals · 2022Article
- Evaluating the impact of stay-at-home and quarantine measures on COVID-19 spread.BMC infectious diseases · 2022Article
- Does knowing the influenza epidemic threshold has been reached influence the performance of influenza case definitions?PloS one · 2022Article
- Inter-provincial disparity of COVID-19 transmission and control in Nepal.Scientific reports · 2021Article
- Multiscale influenza forecasting.Nature communications · 2021Article
- Optimizing respiratory virus surveillance networks using uncertainty propagation.Nature communications · 2021Article
- A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments.PloS one · 2021Article
- Improving probabilistic infectious disease forecasting through coherence.PLoS computational biology · 2021Article
- Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness.PLoS computational biology · 2020Article
- Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas.Journal of the Royal Society, Interface · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
A variety of mechanistic and statistical methods to forecast seasonal influenza have been proposed and are in use; however, the effects of various data issues and design choices (statistical versus mechanistic methods, for example) on the accuracy of these approaches have not been thoroughly assessed. Here, we compare the accuracy of three forecasting approaches-a mechanistic method, a weighted average of two statistical methods and a super-ensemble of eight statistical and mechanistic models-in predicting seven outbreak characteristics of seasonal influenza during the 2016-2017 season at the national and 10 regional levels in the USA. For each of these approaches, we report the effects of real time under- and over-reporting in surveillance systems, use of non-surveillance proxies of influenza activity and manual override of model predictions on forecast quality. Our results suggest that a meta-ensemble of statistical and mechanistic methods has better overall accuracy than the individual methods. Supplementing surveillance data with proxy estimates generally improves the quality of forecasts and transient reporting errors degrade the performance of all three approaches considerably. The improvement in quality from ad hoc and post-forecast changes suggests that domain experts continue to possess information that is not being sufficiently captured by current forecasting approaches.
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