EpiCenter - An Influenza Anomaly Tracker

• 

  Front-page

• 

• 

Inspiration

Preventing misrepresentation of outbreak such as in seen in Covid

What it does

Using prior CDC Health Records and current user given professionally verified illness report, it calculates the probability of an anomaly in influenza data.

How we built it

Via Python, Flask, SQL and Docker with API usage of ChatGPT, Google Pollen API, and Open-Meteo.

Challenges we ran into

Getting commits to work and integrating all code from all group members code.

Accomplishments that we're proud of

Proud of our formula, storage methods, API handling, and scalability.

What we learned

Improved GitHub skills, Python Experience, API usage, Front-end development, and teamwork.

What's next for Influenza Anomaly Tracker

We plan to expand EpiCenter beyond influenza to support full Viral Outbreak reporting including, and introduce personalized individual risk profiles based on travel, demographics, and vaccination status.

Built With

• cdc
• css3
• docker
• fluview
• github
• lovable
• open-meteo
• openai
• python
• react
• typescript
• vite
• weather