The idea of combining predictive analytics with the real-time mapping of coronnavirus that has taken off in recent weeks. We wanted a place for users to be able to track their own data to understand their risk of contracting or spreading the virus.

What it does

The map uses both pre-existing population movement data and user-submitted information (both anonymized) to reveal current danger spots along with feeding a prediction model of future spots of concern.

How I built it

Used ReactJS + leaflet to visualize map in frontent. AWS serverless, dynamodb, S3, and lambda. R/Python for Machine Learning

Challenges I ran into

We had 3 teams : User-Interface, Machine Learning, and Databases to structure the core of the POC product. The three teams communicated regularly over shared Slack channels, and designed their workflow according to one another’s needs.

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