9.9.4: Case Study Analysis

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Page ID: 58332

Felix Amoruwa
De Anza College

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Case Study Analysis

COVID-19 Dashboards: Visualization Under Pressure

During 2020-2021, COVID-19 dashboards became the most widely viewed data visualizations in history. Johns Hopkins University's dashboard, created by a small team, tracked cases and deaths globally and received billions of page views. Meanwhile, local health departments, media outlets (NYT, Financial Times), and governments each built their own dashboards with different design choices.

Design challenges included: rapidly changing data (hourly updates), massive scale differences (countries with thousands of cases vs. millions), multiple audiences (epidemiologists, policymakers, general public), controversial metrics (how to count deaths, what denominator to use for rates), and the need for both geographic maps and time-series trends. Some dashboards were praised for clarity; others were criticized for using misleading scales, inappropriate chart types (e.g., bubble maps where bubble overlap obscured patterns), or failing to account for population differences (showing raw case counts instead of per-capita rates).

Discussion Questions:

The Johns Hopkins dashboard used a dark background with red dots sized by case count on a world map. Evaluate this design: what preattentive attributes does it leverage? What are its limitations (e.g., overlapping bubbles in dense regions, no per-capita normalization)?
Some dashboards showed cumulative case counts (always rising) while others showed daily new cases (showing waves). Each tells a different story from the same data. Discuss how chart choice shapes narrative and how a dashboard designer should handle this — show both? Default to one?
COVID dashboards needed to serve epidemiologists (wanting detailed, filterable data) and the general public (wanting a simple answer: "Is it getting better or worse?"). Apply the exploratory vs. explanatory distinction and Shneiderman's mantra to design a dashboard serving both.
Real-time COVID data had significant quality issues: reporting delays, inconsistent definitions across countries, retroactive corrections. How should dashboard designers communicate data uncertainty and quality limitations without undermining trust in the data?

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