Modelling of tuberculosis prevalence through Bayesian technique

Tuberculosis, or TB, is a serious global health problem that the leading cause of death from single infectious disease. There are various approaches used by research workers for modelling TB prevalence and Bayesian technique one them. For this study, number notified cases tuberculosis in India 1999 to 2019 were analyzed, using data annual reports published Ministry Health Family Welfare. To model India, current study counting process, represented random variable {N(t), t ∈ [0, ∞)}, which counts each year. The utilized Non-Homogeneous Poisson Process (NHPP), allows average rate vary with time, their analysis. parameters posterior distribution assumed follow uniform distribution, certain hyperparameter values assigned specific intervals time. Markov Chain Monte Carlo (MCMC) method was analyze data. proposed found fit well, as demonstrated results chi-square test goodness fit.