Impact of COVID-19 forecast visualizations on pandemic risk perceptions

Two line charts stacked vertically.  The top line chart shows incident deaths due to COVID — this line starts at the x-axis (zero) and then increases rapidly. Next it varies up and down, finally peaking before ending around halfway down the graph. To the right, there are a series of line chart endings: a simple line pointing slightly down (the mean of the predicted trajectory of the line chart on the far left, the same mean line with a 50% confidence interval, three lines pointing down spanning the same 50% confidence interval, the mean line with a 95% confidence interval,  three lines pointing down spanning the same 95% confidence interval, 6 predicting models, a bunch of predicting models that overlap (too many to count). The bottom row is the same as the top but shows cumulative deaths due to COVID. The line chart on the far left starts at the x-axis (zero) and increases steadily. The same predictions continue to the right, all increases to the top right corner of the graph.
Leftmost panels show line charts displaying historical COVID-19 mortality data with incident (top) and cumulative (bottom) y-axes used in Experiment 1. The remaining panels show the uncertainty visualization forecasts that were added to the historical data. Full stimuli sets are available in the Supplementary Information. Eight visualization techniques (columns of the figure) and two axes (rows of the figure) were tested for a total of 16 stimuli types. The stimuli were generated using COVID-19 ForecastHub created by the Reich Lab from the University of Massachusetts Amherst. The CDC used these line charts at the time of publication.
People worldwide use SARS-CoV-2 (COVID-19) visualizations to make life and death decisions about pandemic risks. Understanding how these visualizations influence risk perceptions to improve pandemic communication is crucial. To examine how COVID-19 visualizations influence risk perception, we conducted two experiments online in October and December of 2020 (N = 2549) where we presented participants with 34 visualization techniques (available at the time of publication on the CDC's website) of the same COVID-19 mortality data. We found that visualizing data using a cumulative scale consistently led to participants believing that they and others were at more risk than before viewing the visualizations. In contrast, visualizing the same data with a weekly incident scale led to variable changes in risk perceptions. Further, uncertainty forecast visualizations also affected risk perceptions, with visualizations showing six or more models increasing risk estimates more than the others tested. Differences between COVID-19 visualizations of the same data produce different risk perceptions, fundamentally changing viewers' interpretation of information.
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Helia Hosseinpour
Jennifer Howell
Rumi Chunara

Khoury Vis Lab — Northeastern University
West Village H, Room 302
440 Huntington Ave, Boston, MA 02115, USA