Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
Creating a Health Management Policy for Potential Pandemics
Using Partially Observable Markov Decision Processes
Tal Ben-Zvi and Donald N. Lombardi
In this study we propose a novel approach to time-sensitive health decisions that promote readiness
to pandemic threats. A disease is potentially infectious, and someone needs to decide quickly what to
do. If the potential threat is mild, then harsh courses of action, such as the enhancement of border
controls, the closing of schools and public agencies or the immunization of an entire population, can be
very costly and will unquestionably deplete resources. Yet, if the potential threat is real, then any delays
in action are extremely harmful. It is thus difficult to determine appropriate control strategies: given the
costs associated with the disease, policy makers need to determine how to improve planning for
pandemics; that is, how to achieve the optimal cost-beneficial level of health with respect to their cost
and degree of threat.
We formulate the problem as a finite-time Partially Observable Markov Decision Process. The model
we suggest specifies a course of action in situations of relative uncertainty and time pressure. An
emerging (potential) pandemic is modeled as a stochastic process. Although the decision maker cannot
directly identify the underlying state of the process (the severity of the disease), the process maintains a
probability distribution over a set of possible states, and information attained can lead to an optimal
policy. This policy has the following structure: determine a certain disease a pandemic if and only if the
probability that it is a pandemic exceeds a certain control limit threshold value. The exact value of the
control limit depends on several parameters, as we show in this study. Overall, our study presents a
step toward addressing a larger set of Health Economics problems where there is an impending event
that may require intervention (e.g., assessing pathologies in the body) with broad societal effects.
______________________________________________________________________
Dr. Tal Ben-Zvi, Howe School of Technology Management, Stevens Institute of Technology, Hoboken, NJ,
USA. Email: tal.benzvi@stevens.edu
Dr. Donald N. Lombardi, Howe School of Technology Management, Stevens Institute of Technology, Hoboken,
NJ, USA. Email: donald,lombardi@stevens.edu