  Decision making in the face of a disaster requires the consideration of
several complex factors. In such cases, Bayesian multi-criteria decision
analysis provides a framework for decision making. In this paper, we present
how to construct a multi-attribute decision support system for choosing between
countermeasure strategies, such as lockdowns, designed to mitigate the effects
of COVID-19. Such an analysis can evaluate both the short term and long term
efficacy of various candidate countermeasures. The expected utility scores of a
countermeasure strategy capture the expected impact of the policies on health
outcomes and other measures of population well-being. The broad methodologies
we use here have been established for some time. However, this application has
many novel elements to it: the pervasive uncertainty of the science; the
necessary dynamic shifts between regimes within each candidate suite of
countermeasures; and the fast moving stochastic development of the underlying
threat all present new challenges to this domain. Our methodology is
illustrated by demonstrating in a simplified example how the efficacy of
various strategies can be formally compared through balancing impacts of
countermeasures, not only on the short term (e.g. COVID-19 deaths) but the
medium to long term effects on the population (e.g increased poverty).
