Building a Bayesian decision support system for evaluating COVID-19 countermeasure strategies

Peter Strong, Aditi Shenvi, Xuewen Yu, K. Nadia Papamichail, Henry P. Wynn and Jim Q. Smith

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: 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).

Keywords: Covid-19; decision support system; expected utility; emergency management; multi-criteria; evaluation methodology; coronavirus (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2023-02-01
New Economics Papers: this item is included in nep-hea and nep-upt
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Published in Journal of the Operational Research Society, 1, February, 2023, 74(2), pp. 476 - 488. ISSN: 0160-5682

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