Empowering Communities: Tailored Pandemic Data Visualization for Varied Tasks and Users

Abstract

Data visualization methodologies were intensively leveraged during the COVID-19 pandemic. We review our design experience working on a set of interdisciplinary COVID-19 pandemic projects. We describe the challenges we met in these projects, characterize the respective user communities, the goals and tasks we supported, and the data types and visual media we worked with. Furthermore, we instantiate these characterizations in a series of case studies. Finally, we describe the visual analysis lessons we learned, considering future pandemics.

FIGURE 1.

August 2020: Luxembourg ministers Franz Fayot and Claude Meisch view a pandemic Cross-Functional Dashboard demonstration, implemented in DEBORAH. A tabletop control panel sets the what-if scenario analyzed on the wall display.

FIGURE 2.

COVis’s charts display the temporal evolution of actual/projected cases/deaths, tests, and total cases/deaths × last week’s cases/deaths. The event panel displays information and source references. The events time chart presents policy changes over time. Countries can be excluded or included into analysis.

FIGURE 3.

(A) Contact Tracing Model: Epidemiological compartments. (B) Example output at the population scale for a simulated policy. (C) Example of model output of complex transmission chains at the individual scale at time (t). (D) Comparison of impact of two control policies on individual transmission chains using representative trees. Trees are split into two sides to represent individual policies; green colors indicate the proportions of prevented infection routes.

FIGURE 4.

Custom encodings in MOTIV, a web application for analyzing the U.S. population response to Stay-at-Home (SAH) orders from a moral frame perspective, based on social media. (Left) Barcharts show the distribution of moral frames, with Care being most predominant. (Right) Custom glyphs show the distribution of electoral votes along with SAH support across the U.S.

FIGURE 5.

Part of the SmICS Visualization Dashboard. (Left) Timeline of individual patients, showing their infection status, test results, and ward stays. (Right) Storyline visualization of individual patients, encoding patient contacts as line bundles to show possible transmission pathways via patient contact.

FIGURE 6.

SARS-CoV-2 Infection Prognosis for High-Risk Activities: The red traffic light visual indicates a high infection risk related to activities lasting over 10 min, with a 5% chance of at least one person becoming infected.