ArticleScientific reports2025
A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.Clinical and experimental medicine · 2025Pooled it
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Association between persistent organic pollutants and malaria incidence in mainland China: a population-based surveillance and modeling analysis.BMC public health · 2026Article
- Efficient reporting delay calibration in spatial metapopulation models for reconstructing cross-regional epidemic dynamics.Biosafety and health · 2026Article
- Artificial intelligence driven platform for rapid catalytic performance assessment of nanozymes.Scientific reports · 2025Article
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Authors and funding
2 authors.
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Abstract
This study introduces a hybrid data assimilation method that significantly improves the predictive accuracy of the time-dependent Susceptible-Exposed-Asymptomatic-Infected-Quarantined-Removed (SEAIQR) model for epidemic forecasting. The approach integrates real-time Ensemble Kalman Filtering (EnKF) with the K-Nearest Neighbors (KNN) algorithm, combining dynamic real-time adjustments with pattern recognition techniques tailored to the specific dynamics of epidemics. This hybrid methodology overcomes the limitations of single-model predictions in the face of increasingly complex transmission pathways in modern society. Numerical experiments conducted using COVID-19 case data from Xi'an, Shaanxi Province, China (December 9, 2021, to January 8, 2022) demonstrate a marked improvement in forecasting accuracy relative to traditional models and other data assimilation approaches. These findings underscore the potential of the proposed method to enhance the accuracy and reliability of predictive models, providing valuable insights for future epidemic forecasting and disease control strategies.
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