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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| 主要な著者: | , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Nature Portfolio
2025-01-01
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| シリーズ: | Scientific Reports |
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1038/s41598-025-85593-z |
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