This paper proposes a seven-compartment Markov decision process model
for control of epidemic infections. Decision variables include
vaccination, treatment, and quarantine. Cost function includes cost of
treatment, cost of quarantine, and cost of vaccination. Transition
probabilities have been represented by Bayesian network. Scalability of
the proposed model has been discussed. Extensions of the proposed
approach has also been included as well as comparison with the existing
models. Superiority of the proposed approach has been elaborated through
a case study.