Seasonal Influenza Dynamics in Korea Before and After COVID-19: Parameter Estimation using the Ensemble Kalman Filter Method

Post-COVID-19, influenza epidemiological patterns shifted in the Republic of Korea. This study comparatively analyzes influenza trends before and after the pandemic. The previous studies, including experiments utilizing the guinea pig model, have identified a significant correlation between influenza transmission and absolute humidity, highlighting absolute humidity as a crucial factor influencing seasonal influenza variability. Considering the observed post-pandemic shifts in influenza trends, we tested a hypothesis that absolute humidity and seasonality are vital factors. A Susceptible-Exposed-Infectious-Recovered (SEIR) model, which incorporates seasonal effects, was developed to address these factors. Parameter estimation, conducted via the Ensemble Kalman Filter (EnKF), emphasized a more prolonged recovery rate during the 2022/23 season relative to pre-pandemic seasons. Compared to pre-COVID-19 seasons, the 2022/23 season exhibited a higher initial exposed population (\(\:\text{E}\left(0\right)\)), reflecting increased transmission potential post-restrictions. In addition, our findings indicate a 2.7-week delay between the effective reproduction number and case peaks on average, reflecting distinct transmission dynamics influenced by seasonal and regional factors. The post-pandemic baseline population exhibiting the condition was significantly greater, likely reflecting increased healthcare-seeking behaviors among those experiencing symptoms. The significance of effective post-pandemic influenza management strategies, incorporating both medical response and infection control, are underscored by these findings.