Abstract

Seasonal influenza is an acute respiratory infection caused by several types of influenza viruses worldwide. Its outbreak exhibits a seasonal cycle in temperate climates. For public health decision-making and medical resource management during the time course of seasonal epidemics, a reliable real-time forecasting system is necessary. In this study, we introduce a novel approach combining two different data assimilation techniques to produce a real-time prediction of seasonal influenza governed by the standard SIR model. When applying our developed approach to Influenza-Like-Illness(ILI) data collected in Korea for 2016–2021, it successfully near-casted the upcoming week’s flu incidence.