A statistical model investigating the prevalence of tuberculosis in New York City using counting processes with two change-points
Jorge Alberto Achcar, Edson Zangiacomí Martínez, Antônio Ruffino-Netto, Carlos Daniel Paulino, P. SOARES · Epidemiology and Infection · 2008
We considered a Bayesian analysis for the prevalence of tuberculosis cases in New York City from 1970 to 2000. This counting dataset presented two change-points during this period. We modelled this counting dataset considering non-homogeneous Poisson processes in the presence of the two-change points. A Bayesian analysis for the data is considered using Markov chain Monte Carlo methods. Simulated Gibbs samples for the parameters of interest were obtained using WinBugs software.