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Visualizing epidemiological models for policy: design principles for effective communication
Liza Hadley1,2, Nick Holliman3, Kai Xu4
1University of Colorado Boulder, Boulder, CO, United States.
Frontiers in Public Health
|June 1, 2026
Summary
Effective scientific communication, particularly in epidemiological modeling, relies on clear data visualizations. This study applies vision science principles to help modelers create better graphics for policymakers, improving understanding of complex, uncertain evidence.
Area of Science:
- Epidemiology
- Vision Science
- Scientific Communication
Background:
- Communicating uncertain evidence from epidemiological modeling to policymakers is challenging.
- Visualizations (figures, plots, charts) are central to conveying complex modeling concepts.
- Effective visualizations must be clear, simple, and easily understood.
Purpose of the Study:
- To equip modelers with vision science theory to improve their assessment and creation of epidemiological visualizations.
- To provide practical guidance for enhancing the clarity of model graphics for policy actors.
- To address common failures in epidemiological visualizations.
Main Methods:
- Application of vision science fundamentals to epidemiological modeling contexts.
- Classification of common visualization failures in epidemiological modeling.
- Provision of theoretical frameworks and practical examples for improvement.
Main Results:
- Modelers can leverage vision science to enhance the clarity and impact of their visualizations.
- Understanding how visuals fail allows for targeted improvements.
- Improved visualizations facilitate better communication of uncertain epidemiological evidence.
Conclusions:
- Vision science offers valuable principles for creating effective epidemiological visualizations.
- Modelers and designers can improve communication with policymakers by applying these principles.
- Addressing visualization failures enhances the utility of epidemiological models in policy.
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