A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction

• Published in: Scientific reports Vol. 15; no. 1; pp. 2454 - 14
• Main Authors: Zhang, SongTao, Yang, LiHong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/1305; 631/114/1314; 631/114/2397; 631/114/2401; Accuracy; Algorithms; Asymptomatic; China - epidemiology; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - transmission; COVID-19 Forecasting; Data assimilation; Data collection; Disease control; Disease transmission; EnKF; Epidemics; Epidemiology; Forecasting; Forecasting - methods; Health surveillance; Humanities and Social Sciences; Humans; Hybrid Data Assimilation; Infectious diseases; Kalman filters; KNN; Methods; multidisciplinary; Pattern recognition; Prediction models; Quarantine; SARS-CoV-2 - isolation & purification; Science; Science (multidisciplinary); Trends; China; KNN; Hybrid Data Assimilation; EnKF; COVID-19 Forecasting
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-85593-z

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.