Fever Oracle

Our Solution
Technical Architecture
Buisness Model
Core Strengths
UI/UX
UI/UX
UI/UX
UI/UX

Inspiration

Fever outbreaks often catch communities and hospitals off guard, leading to delayed interventions and preventable complications. Witnessing the gaps in early warning systems—fragmented data sources, siloed insights, and privacy concerns—inspired us to create FEVER ORACLE: a platform that anticipates outbreaks and deteriorations before they become crises, empowering clinicians and public health officials to act decisively.

What It Does

FEVER ORACLE integrates environmental signals, such as wastewater viral assays; pharmacy OTC sales; and patient clinical data to predict fever outbreaks 10–14 days in advance. It uses advanced simulation to model personalized patient risk trajectories and provides cross-institutional early alerts while preserving data privacy through federated learning.

How We Built It

We leveraged lightweight but robust ML models—LightGBM ensembles for outbreak prediction and a GRU-based sequential model for patient risk simulation—wrapped inside a modern cloud-native architecture. FastAPI powers our backend with real-time APIs, while React and Leaflet.js enable interactive and intuitive visualizations. Privacy-preserving federated learning enables collaboration without ever pooling sensitive data centrally.

Challenges We Ran Into

Access to timely, high-quality wastewater and OTC datasets was limited, requiring us to innovate synthetic data generators and proxy signals for development. Balancing model complexity with explainability was critical—clinicians require transparent, actionable insights, so we integrated SHAP-driven interpretability. Ensuring seamless cross-patient alerting demanded efficient similarity search algorithms and robust privacy techniques.

Accomplishments We're Proud Of

Our work uniquely fuses multimodal data streams to offer up to a two weeks' early warning on outbreaks—a critical lead time rarely seen by existing systems. We've developed a scalable, privacy-first architecture deployable in real healthcare settings and demonstrated real-time cross-institutional alert transfer, multiplying predictive power as the network grows.

What We Learned

We have learned that the only way true innovation in health tech can be achieved is by breaking down silos: technical excellence in machine learning is not enough without considering patient-centric design, privacy assurance, and seamless clinician workflows. Early and continuous stakeholder engagement is paramount, as is flexibility to adapt the models for evolving pathogens and data realities.

What's Next for Fever Oracle

We will pilot FEVER ORACLE with hospital networks partnered with Micro Labs, refining live data integration and clinical workflows. We will scale across multiple cities, further enriching outbreak models with mobility and weather data and integrating new clinical endpoints. Ultimately, FEVER ORACLE will seek to become the standard tool globally for proactive fever management that saves lives through timely, data-driven action.