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新冠疫情下的政治决策:以风险管理视角看德国的情况。

Political Decision Making in the COVID-19 Pandemic: The Case of Germany from the Perspective of Risk Management.

机构信息

Faculty of Social and Behavioural Sciences, Friedrich Schiller University Jena, 07743 Jena, Germany.

Faculty of Management, Seeburg Castle University, 5201 Seekirchen, Austria.

出版信息

Int J Environ Res Public Health. 2021 Dec 30;19(1):397. doi: 10.3390/ijerph19010397.

Abstract

The COVID-19 pandemic is permanently changing modern social and economic coexistence. Most governments have declared infection control to be their top priority while citizens face great restrictions on their civil rights. A pandemic is an exemplary scenario in which political actors must decide about future, and thus uncertain, events. This paper tries to present a tool well established in the field of entrepreneurial and management decision making which could also be a first benchmark for political decisions. Our approach builds on the standard epidemiological SEIR model in combination with simulation techniques used in risk management. By our case study we want to demonstrate the opportunities that risk management techniques, especially risk analyses using Monte Carlo simulation, can provide to policy makers in general, and in a public health crisis in particular. Hence, our case study can be used as a framework for political decision making under incomplete information and uncertainty. Overall, we want to point out that a health policy that aims to provide comprehensive protection against infection should also be based on economic criteria. This is without prejudice to the integration of ethical considerations in the final political decision.

摘要

新冠疫情正在永久性地改变现代社会和经济的共存方式。大多数政府都将感染控制作为首要任务,而公民的公民权利则受到极大限制。大流行是政治行为者必须对未来的、不确定的事件做出决策的典型情况。本文试图提出一种在创业和管理决策领域已经确立的工具,该工具也可以作为政治决策的第一个基准。我们的方法基于标准的流行病学 SEIR 模型,结合风险管理中使用的模拟技术。通过我们的案例研究,我们希望展示风险管理技术,特别是使用蒙特卡罗模拟进行风险分析,能够为政策制定者提供的机会,特别是在公共卫生危机中。因此,我们的案例研究可以作为在不完全信息和不确定性下进行政治决策的框架。总的来说,我们想指出,旨在提供全面感染防护的卫生政策也应该基于经济标准。这并不影响最终政治决策中纳入伦理考虑。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a9b/8744640/ab39f568dd02/ijerph-19-00397-g001.jpg

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