背景:对公共卫生危机的反应在本质上越来越具有技术性,正如与COVID-19相关的统计数据和模拟所充分证明的那样。然而,技术的使用是有先决条件的,具有各种含义。这些影响不仅会影响接受,而且还会挑战这些技术在道德和规范方面的可接受性。
目的:本研究的重点是作为算法治理工具的大流行模拟模型,在COVID-19大流行期间在政治决策中发挥了核心作用。为了评估大流行模拟模型的社会影响,数据收集的前提,排序,评估必须披露和反思。因此,必须揭示数字健康技术的社会建构原则,并检查其对社会的影响,伦理,最终是政治问题。
方法:本案例研究从不同模拟方法的系统化开始,以创建大流行模拟模型的类型学。在此基础上,各种属性,功能,并从社会科学的角度揭示和讨论了这些模拟模型的挑战。
结果:大流行模拟方法的类型学揭示了模型驱动处理大流行威胁的多样性。然而,可以合理地假设,未来模拟模型的使用可能会越来越多地转向基于代理或人工智能的模型,从而促进了应对公共卫生危机的算法决策逻辑。由于算法决策更侧重于预测未来动态,而不是评估大流行事件的统计实践,这项研究详细讨论了这一发展,导致与大流行危机技术相关的关键社会和道德问题的可操作性概述。
结论:本研究确定了未来大流行危机技术的3个主要建议。
BACKGROUND: Responses to public health crises are increasingly technological in nature, as the prominence of COVID-19-related statistics and simulations amply demonstrates. However, the use of technologies is preconditional and has various implications. These implications can not only affect acceptance but also challenge the acceptability of these technologies with regard to the ethical and normative dimension.
OBJECTIVE: This study focuses on pandemic simulation models as algorithmic governance tools that played a central role in political decision-making during the COVID-19 pandemic. To assess the social implications of pandemic simulation models, the premises of data collection, sorting, and evaluation must be disclosed and reflected upon. Consequently, the social construction principles of digital health technologies must be revealed and examined for their effects with regard to social, ethical, and ultimately political issues.
METHODS: This case study starts with a systematization of different simulation approaches to create a typology of pandemic simulation models. On the basis of this, various properties, functions, and challenges of these simulation models are revealed and discussed in detail from a socioscientific point of view.
RESULTS: The typology of pandemic simulation methods reveals the diversity of model-driven handling of pandemic threats. However, it is reasonable to assume that the use of simulation models could increasingly shift toward agent-based or artificial intelligence models in the future, thus promoting the logic of algorithmic decision-making in response to public health crises. As algorithmic decision-making focuses more on predicting future dynamics than statistical practices of assessing pandemic events, this study discusses this development in detail, resulting in an operationalized overview of the key social and ethical issues related to pandemic crisis technologies.
CONCLUSIONS: This study identifies 3 major recommendations for the future of pandemic crisis technologies.