COVID-19 is a global pandemic, and “flattening the curve” is a critical approach for slowing the spread of the disease so as to not overwhelm healthcare systems.

Alberta Innovates, together with Cybera, are holding a virtual open data science hackathon from April 10 to May 10. The goal is to:

1. Build data driven insights into "flattening the curve" of COVID-19 infections
2. Map the eventual economic recovery

This online, sprint-like event will see data scientists, economists, students, and everyday citizens collaborate on a data science project specific to Alberta or Canada. This is about providing a better understanding of the story through data!

Participants will collect and curate worldwide open data, and refine, transform, and link that data to provide visualizations, utilizing the Alberta Data Institute's data analytics platform (Dataxch.ai). Ideally, these visualizations will assist health researchers with an understanding on how our efforts are working to “flatten the curve,” and/or provide leaders with a data-driven context for economic recovery for Alberta and Canada.

See the Hackathon page for more info

Prizes

• Grand Alberta Innovates “Best Approach” Award $2,000
• Elevate Consulting “Economic Recovery Analysis” Award $1,500
• Health City “Best Flatten the Curve Analysis” Award $1,500
• ThinkDataWorks “Best Individual Effort” Award 3D Printer
• Alberta Innovates “Post-Secondary Student” Award $500
• Cybera-Callysto “Under 18” Award – Award Micro:bit Go kit

Judging Criteria

1. Quality of idea (includes creativity and originality) Weighting: 30%
2. Implementation of idea (includes how well the idea was executed by the developer) Weighting: 30%
3. Visual impact (includes ability to communicate and user experience, if applicable). Weighting: 40%

Project Timeline

Stage 1: Collect and Curate. Goal is to find, curate and ingest primary open datasets, then do the same for related secondary datasets that could be helpful to the overall goal. Secondary datasets can include geospatial, demographic, economic data, etc.

Stage 2: Refine, clean up and link datasets. Goal is to create “master datasets” (or linked datasets) that add dimension and could be used to generate a better understanding of the impact and spread, and possible recovery.

Stage 3: Visualize. Goal is to use open source tools to create charts, infographics, visuals, etc. that help others “see” the data in action.

Final stage: MISSION ACCOMPLISHED! Teams are ranked on best overall approach to accomplish the mission.

See the Hackathon rules

Organized by:

With support from: