Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations

Lace Padilla, Racquel Fygenson, Spencer C. Castro, Enrico Bertini

View presentation: 2022-10-18T16:15:00Z GMT-0600 Change your timezone on the schedule page
Exemplar figure, described by caption below
Multiple forecast visualizations (MFVs) used in Experiments 1 and 2 showing COVID-19 mortality forecasts for November 13, 2021 in the US. Each line depicts a different group’s forecast, and the experiments examined the impact of the number of forecasts shown on trust and predictions of the COVID-19 trends. Each participant was shown the 16 stimuli in one row of this figure in a randomized order.

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Fast forward

The prevalence of inadequate SARS-COV-2 (COVID-19) responses may indicate a lack of trust in forecasts and risk communication. However, no work has empirically tested how multiple forecast visualization choices impact trust and task-based performance. The three studies presented in this paper (N = 1299) examine how visualization choices impact trust in COVID-19 mortality forecasts and how they influence performance in a trend prediction task. These studies focus on line charts populated with real-time COVID-19 data that varied the number and color encoding of the forecasts and the presence of best/worst-case forecasts. The studies reveal that trust in COVID-19 forecast visualizations initially increases with the number of forecasts and then plateaus after 6-9 forecasts. However, participants were most trusting of visualizations that showed less visual information, including a 95% confidence interval, single forecast, and grayscale encoded forecasts. Participants maintained high trust in intervals labeled with 50% and 25% and did not proportionally scale their trust to the indicated interval size. Despite the high trust, the 95% CI condition was the most likely to evoke predictions that did not correspond with the actual COVID-19 trend. Qualitative analysis of participants’ strategies confirmed that many participants trusted both the simplistic visualizations and those with numerous forecasts. This work provides practical guides for how COVID-19 forecast visualizations influence trust, including recommendations for identifying the range where forecasts balance trade-offs between trust and task-based performance.