title: A statistical model investigating the prevalence of tuberculosis in New York City using counting processes with two change-points.
abstract: 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.
authors.affiliation: Departamento de Medicina Social, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Brazil.
authors.forename: J A
authors.initials: JA
authors.lastname: Achcar
authors.forename: E Z
authors.initials: EZ
authors.lastname: Martinez
authors.forename: A
authors.initials: A
authors.lastname: Ruffino-Netto
authors.forename: C D
authors.initials: CD
authors.lastname: Paulino
authors.forename: P
authors.initials: P
authors.lastname: Soares
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D009519:New York City / Q000453:epidemiology; D015995:Prevalence; D013997:Time Factors; D014376:Tuberculosis / Q000453:epidemiology*