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Back to Work with Masks

We develop Quantitative Data Representations that aid citizens in understanding and assessing pandemic information and risks.


COVID-19 Relative

Risk Tool

Compare risks from COVID-19 infection, vaccination, and other activities.

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Mask Display
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COVID-19 Projection Tool


Learn more about COVID-19 modeling with an animated explanation of IHME's models.

COVID-19 Log Tool

We use animations to help people understand linear and log scaled graphs.

Image by Giacomo Carra
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An NSF Rapid Grant

Our National Science Foundation RAPID grant (DUE- 2032688) incorporates a diverse project team to investigate how people interpret media used quantitative data representations (QDRs) of COVID-19 data. Drawing on our respective areas of expertise, we also produce novel QDRs to support individuals in making data-informed decisions regarding their behavior, personal health risk, and the health risk of others. We accomplish project goals in three phases.

In Phase I, the project team investigates a diverse population to produce differentiated models of participants’ QDR interpretations and juxtapositions of these models that reveal key conceptual categories across participants. In Phase II, the project team applies findings from Phase I and STEM education research to create research-based, project-designed QDRs while simultaneously investigating the extent these QDRs better support individuals in understanding the pandemic. In Phase III, the project team enacts an active dissemination plan in order to draw attention to project generated knowledge and products.

The results of the project will lead to the following outcomes: (1) a knowledge base on individuals’ understandings of media used COVID-19 QDRs; (2) research-based project-generated COVID-19 QDRs; (3) a knowledge base on individuals’ understanding of project generated COVID-19 QDRs; (4) descriptions of research-based principles (with examples) for designing improved pandemic and epidemic QDRs; and (5) a website resource designed for education, media, and public communities. These outcomes advance the field’s knowledge on how to support undergraduate students (as well as the broader populous) in leveraging important STEM concepts to make data-informed decisions

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