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Building a Bayesian decision support system for evaluating COVID-19 countermeasure strategies
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Strong, Peter, Shenvi, Aditi, Yu, Xuewen, Papamichail, K. Nadia, Wynn, Henry P. and Smith, Jim Q. (2023) Building a Bayesian decision support system for evaluating COVID-19 countermeasure strategies. Journal of the Operational Research Society, 74 (2). pp. 476-488. doi:10.1080/01605682.2021.2023673 ISSN 0160-5682.
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Official URL: https://doi.org/10.1080/01605682.2021.2023673
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).
Item Type: | Journal Article | ||||||||
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Subjects: | R Medicine > RA Public aspects of medicine T Technology > T Technology (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | ||||||||
Library of Congress Subject Headings (LCSH): | COVID-19 (Disease), Bayesian statistical decision theory, Decision making, Multiple criteria decision making | ||||||||
Journal or Publication Title: | Journal of the Operational Research Society | ||||||||
Publisher: | Palgrave Macmillan Ltd. | ||||||||
ISSN: | 0160-5682 | ||||||||
Official Date: | 2023 | ||||||||
Dates: |
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Volume: | 74 | ||||||||
Number: | 2 | ||||||||
Page Range: | pp. 476-488 | ||||||||
DOI: | 10.1080/01605682.2021.2023673 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | βThis is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on [date of publication], available online: http://www.tandfonline.com/[Article DOI].β | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 12 January 2022 | ||||||||
Date of first compliant Open Access: | 2 February 2022 | ||||||||
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