A Markov chain consists of a finite set of states and transition probabilities between these states. In epidemiology, states might represent different health conditions (e.g., healthy, diseased, recovered). The model is characterized by the following components:
States: These represent the different health conditions an individual can be in. Transitions: Probabilities of moving from one state to another within a specific time frame. Cycle Length: The time interval between transitions, often set to one year in chronic disease models. Rewards/Costs: Associated with each state or transition, useful for cost-effectiveness analysis.
