In this thesis, health care policy issues for prevention and cure of cervical cancer have been considered. The cancer is typically caused by Human Papilloma Virus (HPV) for which individuals can be tested and also given vaccinations. Policymakers are faced with the decision of how many cancer treatments to subsidize, how many vaccinations to give and how many tests to be performed in each period of a given time horizon. To aid this decision-making exercise, a stochastic dynamic optimal control problem with feedback was formulated, which can be modeled as a Markov decision process (MDP). Solving the MDP is, however, computationally intractable because of the large state space as the embedded stochastic network cannot be decomposed. Hence, an algorithm was proposed that initially ignores the feedback and later incorporates it heuristically. As part of the algorithm, alternate methodologies, based on deterministic analysis, were developed, Markov chains and simulations to approximately evaluate the objective function. Upon implementing the algorithm using a meta-heuristic for a case study of the population in the United States, several measures were calculated to observe the behavior of the system through the course of time, based on the different proposed policies. The policies compared were static, dynamic without feedback and dynamic with feedback. It was found that the dynamic policy without feedback performs almost as well as the dynamic policy with feedback, both of them outperforming the static policy. All these policies are applicable and fast for easy what-if analysis for the policymakers.
