The stochastic SEIR model offers an innovative approach to understanding the spread of infectious diseases, particularly tuberculosis, in limited populations. This study adopts a stochastic model to capture random variability in individual interactions, often overlooked in deterministic models. The population is divided into four main categories: Susceptible (S), Exposed (E), Infected (I), and Recovered (R), with transitions between categories determined by probabilities based on epidemiological parameters. Through simulations, the model demonstrates its capability to depict more realistic patterns of disease spread, including fluctuations in case numbers and epidemic duration. The findings indicate that stochastic variability plays a crucial role in understanding the dynamics of tuberculosis transmission, especially in small populations or when the number of individual contacts is limited. The stochastic SEIR model can serve as an effective tool for policymakers to evaluate various intervention strategies, such as vaccination, transmission control, and treatment, as well as to design public health policies that are data-driven and adaptive to epidemiological uncertainties.
