Building A Bayesian Decision Support System for Evaluating COVID-19 Countermeasure Strategies

TL;DR

This paper develops a Bayesian decision support system to evaluate COVID-19 countermeasure strategies, incorporating uncertainty, dynamic regime shifts, and long-term impacts to aid complex decision making during the pandemic.

Contribution

It introduces a novel application of Bayesian multi-criteria decision analysis to COVID-19 strategies, addressing uncertainty and dynamic changes in a comprehensive framework.

Findings

Demonstrates how to compare strategies using expected utility scores.

Shows the importance of balancing short-term health outcomes with long-term societal impacts.

Provides a simplified example illustrating the decision-making process.

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).