Branching processes are stochastic processes with extensive applications across various scientific disciplines, including biology, medicine, epidemiology, and finance. These processes are particularly effective in modeling the reproduction mechanisms of entities such as human populations, cellular structures, and even subatomic particles. In the realm of epidemiology, branching processes are instrumental in estimating reproduction numbers, which are crucial for understanding the spread of infectious diseases. The reproduction number, often denoted as R, represents the average number of secondary infections produced by a single infected individual in a susceptible population. Tuberculosis (TB), caused by the bacterium \\\"Mycobacterium tuberculosis\\\", remains one of the most significant global health threats, especially in developing countries. The estimation of the reproduction number for TB is vital for public health authorities to design effective control and prevention strategies. In this talk, we focus on the application of a Bayesian approach to estimate the reproduction number for pulmonary tuberculosis in Iran, leveraging the framework of branching processes.

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