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
Pages (from-to)476-488
Number of pages13
JournalJournal of the Operational Research Society
Issue number2
Early online date18 Jan 2022
Publication statusPublished - 1 Feb 2023


  • COVID-19
  • decision support system
  • emergency management
  • evaluation methodology
  • expected utility
  • multi-criteria


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