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Decision Support for COVID-19 Policy Including Poverty Drivers

by   Peter Strong, et al.

In this paper we present a multi-attribute decision support framework for choosing between countermeasure strategies designed to mitigate the effects of COVID-19 in the UK. 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 captures 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 unusual elements to it: the pervasive uncertainty of the science; the necessary dynamic shifts between states within each candidate suite of counter measures; the fast moving stochastic development of the underlying threat all present new challenges to this domain. We incorporate these within our framework. Further, we demonstrate our framework through an example where we evaluate several strategies by considering short- and long-term attributes that impact on the health of our population.


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