Research Article
Jorge Alberto Achcar, Edilbert
Abstract
The daily number of hospital admission due to respiratory diseases can have a great variability. This variability could be explained by different factors as year seasons, temperature, pollution levels among many others [1,2]. In this paper, we have been using non-homogeneous Poisson processes with different intensity functions under the Bayesian paradigm and using standard existing MCMC (Markov Chain Monte Carlo) methods to simulate samples for the joint posterior distribution of interest. An application is given considering the daily number of hospital admission in Ribeirao Preto, Brazil in the period ranging from January 01, 1998 to December 31, 2007. The proposed model showed a good fit for the seasonality of the disease with simple interpretation in the framework of epidemiology.