Case Study•2023
FluCast Outbreak Forecasting
Respiratory illness forecasting platform that fuses CDC feeds with LSTM ensembles and interactive dashboards.
Next.jsFastAPIPyTorchAzure MLPostgreSQL
Context & Goals
Built FluCast during a public health research initiative to help epidemiologists anticipate respiratory surges. Needed reliable ingestion from CDC ILINet APIs, configurable regional models, and dashboards for health departments.
Architecture
- Data ingestion: Scheduled ETLs pull CDC feeds, sanitize anomalies, and persist to PostgreSQL + S3 history buckets.
- Modeling: PyTorch LSTM ensemble with feature engineering for seasonality, weather, and mobility metrics. Azure ML orchestrates retrains.
- Serving: FastAPI inference service with caching and guardrails that expose forecasts and confidence bands to the Next.js frontend.
- Visualization: Next.js dashboard with map overlays, scenario comparisons, and PDF export for weekly reporting.
Challenges & Wins
- Automated monitoring detects concept drift and sends alerts if confidence intervals widen beyond thresholds.
- Implemented reproducible experiment tracking using MLflow, enabling quick audits of model changes.
- Added region-level overrides so public health analysts can tweak interventions and see projections instantly.
What I’d do next
- Expand to include wastewater and school absence data for better leading indicators.
- Introduce uncertainty-aware ensembles and quantile regression for richer decision support.