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

Area of Science:

• Public Health Informatics
• Data Science
• Human-Computer Interaction

Background:

• The COVID-19 pandemic necessitated rapid data analysis and communication.
• Effective data visualization became a critical tool for understanding and responding to the pandemic.

Purpose of the Study:

• To review design experiences and challenges in interdisciplinary COVID-19 data visualization projects.
• To characterize user communities, tasks, and data types encountered.
• To extract lessons learned for future pandemic preparedness.

Main Methods:

• Review of design experience from multiple interdisciplinary COVID-19 projects.
• Characterization of user communities, goals, tasks, data types, and visual media.
• Presentation of case studies to illustrate findings.
• Analysis of visual analysis lessons learned.

Main Results:

• Identified diverse user needs and challenges in pandemic data visualization.
• Documented various data types and visual media employed.
• Detailed specific project case studies and their outcomes.
• Synthesized key lessons for improving future pandemic response through data visualization.

Conclusions:

• Effective data visualization design requires understanding user communities and specific tasks.
• Interdisciplinary collaboration is essential for successful pandemic data visualization.
• Lessons learned can guide the development of more robust and responsive visualization tools for future health crises.