Application of Dynamic Linear Model on Data Corresponding to Chronic Asthma Disease

The linear Gaussian state space model, also known as dynamic linear model in the Bayesian literature, has become one of the standard parametric modeling forms with parameters changing over time in the time series analysis. It provides a unified and flexible framework for describing, modeling and forecasting a wide array of time series and other types of longitudinal data. There are several studies which have been concerned with describing the seasonal pattern of admission to hospital for children with asthma, and have also explained the relationship between unexpected medical contacts and the end of the summer holidays. In this paper we are interested to use asthma chronic disease data for constructing a dynamic linear model and investigate the behavior of this model also, we are interested to make one step ahead forecasting.