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

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

Research output: Contribution to journalArticlepeer-review


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).
Original languageEnglish
JournalJournal of the Operational Research Society
Publication statusPublished - 18 Jan 2022


  • COVID-19; decision support system; emergency management; multi-criteria decision analysis

Research Beacons, Institutes and Platforms

  • Institute for Data Science and AI


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