Optimal Immunization Policy Using Dynamic Programming
  Decisions in public health are almost always made in the context of
uncertainty. Policy makers are responsible for making important decisions,
faced with the daunting task of choosing from amongst many possible options.
This task is called planning under uncertainty, and is particularly acute when
addressing complex systems, such as issues of global health and development.
Uncertainty leads to cautious or incorrect decisions that cost time, money, and
human life. It is with this understanding that we pursue greater clarity on,
and methods to address optimal policy making in health. Decision making under
uncertainty is a challenging task, and all too often this uncertainty is
averaged away to simplify results for policy makers. Our goal in this work is
to implement dynamic programming which provides basis for compiling planning
results into reactive strategies. We present here a description of an AI-based
method and illustrate how this method can improve our ability to find an
optimal vaccination strategy. We model the problem as a partially observable
Markov decision process, POMDP and show how a re-active policy can be computed
using dynamic programming. In this paper, we developed a framework for optimal
health policy design in an uncertain dynamic setting. We apply a stochastic
dynamic programming approach to identify the optimal time to change the health
intervention policy and the value of decision relevant information for
improving the impact of the policy.
