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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. 

Through the “COVID-Inspired Data Science through Epidemiology Education” project, 400 underserved middle-school youth across the United States are engaging in a 20-hour out-of-school data club centered on a novel. The narrative is integrated with hands-on data activities and modeling (e.g., creating graphs of infections over time in CODAP; modeling disease transmission rates in NetLogo). Youth learn to: 1) Use data tools to track the spread of a variety of infectious diseases; 2) Ask and address their own questions of data; and 3) Use data to communicate to local audiences about epidemiological patterns and challenges. The project breaks new ground in integrating data science with epidemiology education for 11–14-year-old youth.