The methodological purpose of this article is to demonstrate how data mining contributes to rapid complex case study descriptions. Our complexity-informed design draws on freely accessible datasets reporting the public health response surrounding the onset of the COVID-19 pandemic in Alberta (Canada) and involves the cross analysis of integrated findings across six periods of fluctuation identified in the initial quantitative phase of a convergent sequential approach. We discuss how our case meta-inferences, informing how public health briefings can build credibility and trust, were derived by attending to three key concepts of complex adaptive systems: emergence, interdependence, and adaptation. This article serves as an essential reference for using data mining within a case study–mixed methods design for studying complex phenomena.

本文的方法论目的是演示数据挖掘如何促进快速复杂的案例研究描述。我们针对复杂性的设计利用了可自由访问的数据集，该数据集报告了加拿大艾伯塔省COVID-19大流行爆发前后的公共卫生反应，并涉及了对在最初的定量阶段确定的六个波动时期内综合发现的交叉分析。收敛顺序法。我们讨论了如何通过关注复杂的适应性系统的三个关键概念（即出现，相互依赖和适应性）来得出案例元推论，以告知公共卫生简报如何建立信誉和信任。本文是在案例研究-研究复杂现象的混合方法设计中使用数据挖掘的重要参考。