A statistical model investigating the prevalence of tuberculosis in New York City using counting processes with two change-points.
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.
Achcar
J A
JA
Departamento de Medicina Social, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Brazil.
Martinez
E Z
EZ
Ruffino-Netto
A
A
Paulino
C D
CD
Soares
P
P
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*