Inspiration
Our WebApp was inspired by the daily health events that happen in schools—most of which never reach healthcare systems, leaving valuable insights untapped. We realized that schools are not only places for learning but also key environments for fostering health awareness among students.
By building a tool that tracks symptoms and connects them with public data on diagnoses, air quality, and weather, we aim to fill this gap. Our goal is to help health professionals act faster while encouraging students to better understand how their environment impacts their well-being.
What it does
The WebApp allows schools to track symptoms easily. Each group of students sharing a class is assigned a QR code that they can scan to submit symptoms from a predefined list. This data is then aggregated with public information on weather, air quality, and other factors, making it accessible to health professionals for analysis and outbreak prediction.
How we built it
We developed the WebApp using Django’s fullstack capabilities, leveraging its built-in templating system for the frontend. JavaScript scripts embedded in the HTML templates handle interactivity, while external APIs like MeteoCat and DadesObertes were used to retrieve real-time public data on weather and air quality.
Challenges we ran into
One major challenge was integrating data from different sources, as they used varying formats for dates and localization. Standardizing these datasets into a unified format required significant effort. Additionally, time constraints were tough as we decided on the project late, and our team faced setbacks with one member unable to join and another sick for the first two days.
Accomplishments that we're proud of
We’re proud of building a tool that could genuinely assist health professionals in predicting epidemic outbreaks. Despite the technical and logistical hurdles, we delivered a functional and impactful product. This achievement feels even more rewarding given the challenges we worked through as a team.
What we learned
We learned how to access and integrate publicly available data into a coherent dataset. Additionally, we gained valuable experience in collaborating under pressure and adapting to unexpected setbacks.
What's next for OutbreakDetector
Our next steps are to improve the WebApp’s look and feel to make it more user-friendly. We also aim to integrate more data sources to further enhance its predictive capabilities and provide even greater value to health professionals.
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