Population-Scale Forecasting Pipelines

Longitudinal healthcare data presents a unique forecasting challenge: millions of rows spanning years, with complex seasonal patterns, non-linear trends, and the need for rigorous uncertainty quantification. This work, published in The Lancet and JAMA, demonstrates production-grade forecasting pipelines that meet the statistical rigor of peer-reviewed research while operating at population scale.

The Challenge

Manual reporting cycles consumed 20+ analyst hours per week. Existing dashboards showed historical data only — no forward-looking projections. Stakeholders needed decision-ready forecasts with quantified uncertainty to plan resource allocation, budget planning, and policy interventions.

Solution Architecture

• Multi-model ensemble: ARIMA for short-term trends, Prophet for seasonality + holiday effects, LSTM for non-linear patterns
• Mixed-effects panel models (GEE) for clustered longitudinal data with within-group correlation
• Automated model selection based on information criteria (AIC / BIC) and out-of-sample validation
• Confidence intervals validated against held-out test sets — not heuristic widening bands

Results

• 5M+ rows of longitudinal data modeled
• 95%+ reporting turnaround reduction
• 20 hrs/week reclaimed for analysis, not reporting
• Peer-reviewed methodology published in The Lancet and JAMA