The recent global health challenges highlight the need of developing rapid visualizations of the spread of diseases for time-sensitive public health decision-making. This workshop attempts to discuss the robust techniques available in Python and associated geographic visualization tools to manipulate, analyze, and visualize epidemic data, bridging the gap between complex data sets and actionable insights.

Workshop Highlights:

• Python for Data Manipulation: Leveraging Python libraries like Pandas for effective data cleaning and organization.
• Geospatial Data Analysis: Hands-on training in using Geopandas and related Python libraries to integrate geographic information into epidemic data analysis.
• Advanced Data Visualization: Focusing on creating both static and interactive visualizations with Python libraries such as Matplotlib, Seaborn, and Plotly to depict epidemic trends effectively.
• Case Studies and Practical Applications: Utilizing real-world epidemic scenarios to apply learning in mapping and visualizing disease spread and impact.
• Spatial Analysis Techniques: Exploring advanced methods like hot spot detection and spatial clustering, crucial for understanding geographical patterns in disease spread.

This workshop promises to be a blend of theoretical understanding and practical application, ensuring that participants leave with a strong foundation in using Python and GIS for epidemiological data visualization.