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Abstract 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. 

After participating in an afterschool program where they used the Common Online Data Analysis Platform (CODAP) to study time-series data about infectious diseases, four middle school students were interviewed to determine how they understood features of and trends within these graphs. Our focus was on how students compared graphs. Students were readily able to compare cumulative/total infection rates among two countries with differently sized populations. It was more challenging for them to link a graph of yearly cases to the corresponding graph of cumulative cases. Students offered reasonable interpretations for spikes or steady periods in the graphs. Time-series graphs are accessible for 11- to 14-year-old students, who were able to make comparisons within and between graphs. Students used proportional reasoning for one comparison task, and on the other task, while it was challenging, they were beginning to understand how yearly and cumulative graphs were related. Time-series graphs are ubiquitous and socially relevant: Students should study time-series data more regularly in school, and more research is needed on the progression of sense-making with these graphs. 

Quickly disseminating an innovative, timely afterschool program raises challenges, from recruitment and professional development to assessment, program fidelity, and quality. In this paper, we describe our experience as project developers, trainers, and researchers working with an afterschool network, Imagine Science, to disseminate a middle school club program about epidemic diseases and data. What we learned from working with this network may be useful to others who have created an afterschool science, technology, engineering, and mathematics (STEM) program they hope to spread widely. 

We examine how developers of data science curricula determine what makes a pedagogically effective dataset enabling 10–14 year-old students (“middle school” in the United States) to engage in the data investigation cycle by posing their own questions about relationships among variables. We describe strategies for curating existing datasets to address goals for learning about data, and for optimizing the use of these datasets once they are curated. We investigate how data science educators can transform existing datasets into ones appropriate for students with little data experience, drawing on our experience working with several publicly available datasets, which students explored in CODAP (the Common Online Data Analysis Platform).