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Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast
IEEE Transactions on Visualization and Computer Graphics
|September 27, 2022
Summary
Understanding COVID-19 forecast visualizations is key. Simpler visuals, like grayscale or single forecasts, build trust but may not improve prediction accuracy for SARS-CoV-2 (COVID-19) trends.
Area of Science:
- Public Health
- Data Visualization
- Risk Communication
Background:
- Inadequate responses to SARS-CoV-2 (COVID-19) may stem from low trust in forecasts and risk communication.
- Empirical research on how visualization choices affect trust and performance in forecasting is lacking.
Purpose of the Study:
- To investigate the impact of COVID-19 mortality forecast visualization choices on user trust and task performance.
- To identify optimal visualization strategies for enhancing trust and accuracy in public health forecasts.
Main Methods:
- Three studies (N=1299) analyzed trust and performance using line charts with real-time COVID-19 data.
- Visualizations varied in number/color of forecasts and inclusion of best/worst-case scenarios.
- Task involved predicting COVID-19 trends based on presented visualizations.
Main Results:
- Trust in COVID-19 forecast visualizations plateaus after 6-9 forecasts.
- Visualizations with less information (single forecast, grayscale, 95% CI) garnered higher trust.
- High trust in 95% CI did not correlate with accurate trend prediction; simpler visuals were trusted more.
Conclusions:
- Visualization design significantly impacts trust in COVID-19 mortality forecasts.
- Simpler visualizations may enhance trust but not necessarily task performance.
- Recommendations are provided for balancing trust and performance in forecast visualization.
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