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

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)...