Abstract for: Calibrating SEIR Epidemic Model: Exploring estimates of the reproduction number for seasonal influenza in Ireland

In the 21st century, the world is facing a wide range of increasingly dynamic problems related to infectious diseases. Immunization plays a critical role in controlling the outbreak of infectious diseases. This study implements a system dynamic SEIR disease model to fit seasonal influenza data in Ireland. Fitting disease models to incidence data is complex when all infected and immunized cases are not reported. The special focus is the model calibration process for the model parameters. Model calibration estimates the best parameter values to reduce the mismatch between simulated model results and data. Markov Chain Monte Carlo (MCMC) sampling algorithm is applied to calibrate the model with prior and likelihood for the epidemic model parameters: the disease transmission rate, incubation rate, recovery rate and the fraction of notified cases. The model calibration and parameters analysis work in progress to explore the seasonal influenza transmission rate and notified influenza infected cases in Ireland. A robust mathematical model and statistical analysis can provide reliable insight into an epidemic and policy development.