Why Forecasting Infectious Diseases Is Harder Than Predicting Sales (And What Data Scientists Can Learn From It)
Forecasting infectious disease activity differs fundamentally from typical time-series problems like retail sales. Key lessons include: model performance varies significantly by country due to differing epidemiology; strongly seasonal diseases are paradoxically harder to forecast because peak timing and magnitude must all be correct; standard metrics like RMSE can mask critical errors in public health contexts; domain-informed feature engineering often outperforms algorithm-switching; and anomaly detection complements forecasting to catch unexpected divergences. The broader takeaway for data scientists is to shift focus from 'which model' to questions about validation strategy, meaningful metrics, adaptive pipelines, and knowing when a model is wrong.