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引用次数: 0
摘要
我们探讨如何利用统计数据来了解疾病传播情况,并为政府决策提供支持。我们希望 "过去的表现并不能保证未来的结果"。我们讨论并展示了美国国家科学基金会(NSF)资助的 COVID-Inspired Data Science Education through Epidemiology (CIDSEE) 项目中的实例。我们始终强调证据、建模和理论化与适当行动之间的关系。统计应该是所有这些方面的基本要素。我们指出了一些 "大统计思想",它们是整个建模过程的基础,可以在大流行病的背景下生动地加以说明。我们认为,统计教育应强调统计在实际情况中的应用,而许多课程并没有使学生具备在课堂之外运用他们对统计的理解的能力。我们提供了一个课程分析框架,并指出了一些丰富的教学资源。
New viruses are inevitable; pandemics are optional—Lessons for and from statistics
We explore ways in which statistics can be used to understand disease spread and support decision‐making by governments. “Past performance does not guarantee future results”—we hope. We discuss and show examples from the National Science Foundation (NSF)‐funded COVID‐Inspired Data Science Education through Epidemiology (CIDSEE) project. Throughout, the emphasis is on the relationships between evidence, modeling and theorizing, and appropriate action. Statistics should be an essential element in all these aspects. We point to some “big statistical ideas” that underpin the whole process of modeling, which can be illustrated vividly in the context of pandemics. We argue that statistics education should emphasize the application of statistics in practical situations, and that many curricula do not equip students to use their understandings of statistics outside the classroom. We offer a framework for curriculum analysis and point to some rich teaching resources.