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Vol. 12, Issue 2 (2023)

Modelling of tuberculosis prevalence through Bayesian technique

Rohit Kundu, OP Sheoran and Sanjeev
Tuberculosis, or TB, is a serious global health problem that is the leading cause of death from a single infectious disease. There are various approaches used by the research workers for modelling TB prevalence and Bayesian technique is one of them. For this study, the number of notified cases of tuberculosis in India from 1999 to 2019 were analyzed, using data from annual reports published by the Ministry of Health and Family Welfare. To model the number of TB cases in India, the current study used a counting process, represented by the random variable {N(t), t ∈ [0, ∞)}, which counts the number of TB cases each year. The study utilized a Non-Homogeneous Poisson Process (NHPP), which allows for the average rate of TB cases to vary with time, in their analysis. The parameters of the posterior distribution were assumed to follow a uniform distribution, with certain hyperparameter values assigned to specific intervals of time. The Markov Chain Monte Carlo (MCMC) method was used to analyze the data. The proposed model was found to fit the data well, as demonstrated by the results of the chi-square test for goodness of fit.
Pages: 180-184  |  271 Views  80 Downloads

The Pharma Innovation Journal
How to cite this article:
Rohit Kundu, OP Sheoran, Sanjeev. Modelling of tuberculosis prevalence through Bayesian technique. Pharma Innovation 2023;12(2):180-184. DOI: 10.22271/tpi.2023.v12.i2c.18433

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