2025, Vol. 6, Issue 1, Part D

This research applies a Markov chain model to a time series representing the number of tuberculosis (TB) cases, as part of an applied statistical approach to understanding disease dynamics. A (5×5) transition matrix was constructed based on the distribution of TB cases, and hypotheses were formulated to fit the problem within the Markov framework. Upon analyzing the chain’s properties, it was found to be non-ergodic, prompting the calculation of the absorbing state probabilities and the expected absorption time for transient states.

The findings have significant implications for regional planning, particularly in public health management. By forecasting the spatial and temporal evolution of TB cases, the model aids decision-makers in allocating healthcare resources more efficiently and prioritizing intervention efforts in high-risk areas. This statistical modeling approach provides valuable insights for evidence-based planning, allowing regional authorities to anticipate and respond proactively to patterns of disease spread.