A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction
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 Filterin...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85593-z |
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| author | SongTao Zhang LiHong Yang |
| author_facet | SongTao Zhang LiHong Yang |
| author_sort | SongTao Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e6fefa923a844ef0ab1bcf7fcacf717f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e6fefa923a844ef0ab1bcf7fcacf717f2025-01-26T12:30:09ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-025-85593-zA hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 predictionSongTao Zhang0LiHong Yang1College of Mathematical Sciences, Harbin Engineering UniversityCollege of Mathematical Sciences, Harbin Engineering UniversityAbstract 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.https://doi.org/10.1038/s41598-025-85593-zHybrid Data AssimilationCOVID-19 ForecastingEnKFKNN |
| spellingShingle | SongTao Zhang LiHong Yang A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction Scientific Reports Hybrid Data Assimilation COVID-19 Forecasting EnKF KNN |
| title | A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction |
| title_full | A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction |
| title_fullStr | A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction |
| title_full_unstemmed | A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction |
| title_short | A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction |
| title_sort | hybrid data assimilation method based on real time ensemble kalman filtering and knn for covid 19 prediction |
| topic | Hybrid Data Assimilation COVID-19 Forecasting EnKF KNN |
| url | https://doi.org/10.1038/s41598-025-85593-z |
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