Presentation Information
[POS-18]Influenza Trends in South Korea: Parameter Estimation Before and After the Pandemic with the Ensemble Kalman Filter
*Yeonsu Lee1, Wasim Abbas2, Hyojung Lee1 (1. Department of Statistics, Kyungpook National University (Korea), 2. Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University (Korea))
Keywords:
Influenza,Absolute humidity,Seasonality,Epidemic model,Ensemble Kalman Filter
Influenza is an acute respiratory disease caused by the influenza virus, primarily occurring in winter with a well-defined seasonal pattern. However, influenza surveillance data in Korea suggest that the resurgence of influenza after the COVID-19 pandemic differs significantly from pre-pandemic trends. Understanding these changes is crucial for improving epidemic models and public health interventions.
This study aims to analyze post-COVID-19 changes in influenza trends by improving epidemic models and estimating parameter distributions using Ensemble Kalman Filter. We extracted daily influenza case data in Korea from the National Health Insurance Sharing Service (NHISS), and absolute humidity data from Automated Synoptic Observing System.
To investigate changes in influenza trends, we compare two epidemic models: (1) the SEIR model incorporating absolute humidity and (2) the SEIR model incorporating both absolute humidity and seasonality. For parameter estimation, we apply two methods. First, we use Least Squares Estimation (LSE) to compare model performance and select the most suitable model for influenza dynamics. Second, we employ the Ensemble Kalman Filter (EnKF) to estimate parameter distributions for the selected model. By applying EnKF to data from different time periods, we identify shifts in influenza transmission patterns.
This analysis offers valuable insights into the evolving dynamics of influenza in the post-COVID-19 era and emphasizes the importance of environmental factors, such as humidity, in future epidemic modeling and public health planning.
This study aims to analyze post-COVID-19 changes in influenza trends by improving epidemic models and estimating parameter distributions using Ensemble Kalman Filter. We extracted daily influenza case data in Korea from the National Health Insurance Sharing Service (NHISS), and absolute humidity data from Automated Synoptic Observing System.
To investigate changes in influenza trends, we compare two epidemic models: (1) the SEIR model incorporating absolute humidity and (2) the SEIR model incorporating both absolute humidity and seasonality. For parameter estimation, we apply two methods. First, we use Least Squares Estimation (LSE) to compare model performance and select the most suitable model for influenza dynamics. Second, we employ the Ensemble Kalman Filter (EnKF) to estimate parameter distributions for the selected model. By applying EnKF to data from different time periods, we identify shifts in influenza transmission patterns.
This analysis offers valuable insights into the evolving dynamics of influenza in the post-COVID-19 era and emphasizes the importance of environmental factors, such as humidity, in future epidemic modeling and public health planning.