Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden.
Advancing Systems Analysis (ASA), International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.
Front Public Health. 2023 Feb 17;11:1064554. doi: 10.3389/fpubh.2023.1064554. eCollection 2023.
In this article, we summarize our findings from an EU-supported project for policy analyses applied to pandemics such as Covid-19 (with the potential to be applied as well to other, similar hazards) while considering various mitigation levels and consequence sets under several criteria.
It is based on our former development for handling imprecise information in risk trees and multi-criteria hierarchies using intervals and qualitative estimates. We shortly present the theoretical background and demonstrate how it can be used for systematic policy analyses. In our model, we use decision trees and multi-criteria hierarchies extended by belief distributions for weights, probabilities and values as well as combination rules to aggregate the background information in an extended expected value model, taking into criteria weights as well as probabilities and outcome values. We used the computer-supported tool DecideIT for the aggregate decision analysis under uncertainty.
The framework has been applied in three countries: Botswana, Romania and Jordan, and extended for scenario-building during the third wave of the pandemic in Sweden, proving its feasibility in real-time policy-making for pandemic mitigation measures.
This work resulted in a more fine-grained model for policy decision that is much more aligned to the societal needs in the future, either if the Covid-19 pandemic prevails or for the next pandemic or other society-wide hazardous emergencies.
本文总结了我们在欧盟支持的项目中的发现，该项目针对大流行（如 COVID-19）进行政策分析，同时考虑了在不同标准下的各种缓解水平和后果集，这些分析也可应用于其他类似的风险。
该方法基于我们之前在使用区间和定性估计处理风险树和多准则层次结构中的不精确信息的开发。我们简要介绍了理论背景，并演示了如何将其用于系统的政策分析。在我们的模型中，我们使用决策树和多准则层次结构，通过置信分布来扩展权重、概率和价值，以及组合规则，以便在扩展的期望值模型中汇总背景信息，同时考虑到标准权重、概率和结果价值。我们使用计算机支持的工具 DecideIT 进行不确定条件下的综合决策分析。
该框架已在三个国家（博茨瓦纳、罗马尼亚和约旦）中应用，并在瑞典大流行的第三波期间扩展到情景构建，证明了其在大流行缓解措施实时决策中的可行性。
这项工作导致了一个更精细的政策决策模型，该模型在未来与社会需求更加一致，无论是 COVID-19 大流行持续存在，还是下一次大流行或其他全社会危险紧急情况。
