ArticleNature communications2017
Counteracting structural errors in ensemble forecast of influenza outbreaks.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Climate-Aware Self-Retrospective Representation Learning for Spatio-Temporal Epidemic Forecasting.Tropical medicine and infectious disease · 2026Article
- Combining data assimilation of states and parameters for more precise infectious disease prediction.One health (Amsterdam, Netherlands) · 2025Article
- Influenza Activity and Preterm Birth in the Atlanta Metropolitan Area: A Time-Series Analysis from 2010 to 2017.Epidemiology (Cambridge, Mass.) · 2025Article
- Epidemiological characteristics and transmission dynamics of dengue fever in China.Nature communications · 2024Article
- Pharmacoinformatics and Breed-Based De Novo Hybridization Studies to Develop New Neuraminidase Inhibitors as Potential Anti-Influenza Agents.Molecules (Basel, Switzerland) · 2023Article
- Development of Accurate Long-lead COVID-19 Forecast.PLoS computational biology · 2023Article
- Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm.PLoS neglected tropical diseases · 2023Article
- Application progress of ensemble forecast technology in influenza forecast based on infectious disease model.Frontiers in public health · 2023Review
- Evaluating the impact of stay-at-home and quarantine measures on COVID-19 spread.BMC infectious diseases · 2022Article
- An ensemble forecast system for tracking dynamics of dengue outbreaks and its validation in China.PLoS computational biology · 2022Article
- Modeling analysis revealed the distinct global transmission patterns of influenza A viruses and their influencing factors.Integrative zoology · 2021Article
- Identifying asymptomatic spreaders of antimicrobial-resistant pathogens in hospital settings.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Trade-offs between individual and ensemble forecasts of an emerging infectious disease.Nature communications · 2021Article
- Multiscale influenza forecasting.Nature communications · 2021Article
- Spatial transmission network construction of influenza-like illness using dynamic Bayesian network and vector-autoregressive moving average model.BMC infectious diseases · 2021Article
- Optimizing respiratory virus surveillance networks using uncertainty propagation.Nature communications · 2021Article
- The Rise in Norovirus-Related Acute Gastroenteritis During the Fight Against the COVID-19 Pandemic in Southern China.Frontiers in public health · 2021Article
- Differential effects of intervention timing on COVID-19 spread in the United States.Science advances · 2020Article
- Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas.Journal of the Royal Society, Interface · 2020Article
- Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness.PLoS computational biology · 2020Article
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2 authors.
Funding
Abstract
For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. As a consequence, quantification of the nonlinear error structure in current forecast models is needed so that this growth can be corrected and forecast skill improved. Here, we inspect the error growth of a compartmental influenza model and find that a robust error structure arises naturally from the nonlinear model dynamics. By counteracting these structural errors, diagnosed using error breeding, we develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models.Inaccuracy of influenza forecasts based on dynamical models is partly due to nonlinear error growth. Here the authors address the error structure of a compartmental influenza model, and develop a new improved forecast approach combining dynamical error correction and statistical filtering techniques.
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