Epidemic Demand Forecasting for Regional Healthcare

Problem

A regional healthcare system in a developing market lacked reliable case forecasting to plan ICU capacity, oxygen supply, and public-health interventions. Off-the-shelf global models did not generalise to local epidemiological data.

Approach

Led the machine-learning track in a globally distributed team of 15+ contributors. Implemented XGBoost time-series models with feature engineering on public-health data, and built an interactive dashboard for non-technical stakeholders to explore forecasts.

Result

Model evaluation metrics (MAE, RMSE, R²) validated on held-out data. Dashboard deployed for ongoing use by regional health planners.

• ◆MAE, RMSE, R² validated on held-out data
• ◆Interactive dashboard deployed for health planners
• ◆Team of 15+ across data science and engineering
• ◆Feature engineering on public-health time series