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

Architectural Decisions

Why an ARIMA/Prophet/LSTM ensemble instead of a single model?No single model reliably captured all the patterns present in longitudinal healthcare data. ARIMA handled short-term autoregressive trends with well-calibrated uncertainty intervals — important for near-term resource planning. Prophet modeled seasonality, holiday effects, and changepoints that ARIMA's fixed seasonal terms could not. LSTM captured non-linear patterns and long-range dependencies that neither linear model could express. The ensemble averaged across these complementary strengths, which consistently reduced out-of-sample forecast error and guarded against overfitting to any single model's structural assumptions — a validation outcome confirmed on held-out test sets.

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