Epidemic Forecasting: Lessons Learned from the SARS-CoV-2 Pandemic to Balance Accuracy, Feasibility, and Impact

The COVID-19 pandemic highlighted the importance of reliable, real-time hospital forecasting. At Bordeaux University Hospital, we developed models to predict SARS-CoV-2-related hospitalizations 14 days in advance using integrated data sources. We identified six key lessons to guide future epidemic response: (1) Multimodal data improves accuracy; (2) Simple baseline models are essential for benchmarking and building trust; (3) Model and metric choices must align with decision goals which often means prioritizing absolute over relative metrics and beginning with simple models; (4) Prediction intervals should be provided to communicate the uncertainty associated with forecasts; (5) Real-world constraints such as computational cost, maintainability, and required expertise should guide model selection; (6) Forecasts must be contextualized and communicated carefully to policymakers. We advocate for a systems-level forecasting approach that balances accuracy, feasibility, and impact.