Data Assimilation Substitutes for Biological Complexity in Hybrid Influenza Forecasting Models

Infectious-disease forecasts increasingly inform public-health preparedness, yet it is unclear whether hybrid mechanistic-statistical models gain more accuracy from added biological complexity or from assimilating richer data, a choice that affects how forecasting programs invest limited resources. Using eight retrospective influenza seasons in North Carolina, we evaluate whether training on historical data and assimilating auxiliary emergency department (ED) visit data improves four-week-ahead hospital admission forecasts more than adding biological complexity (multi-subtype structure and cross-season immunity). Hierarchical Bayesian training on historical data improves accuracy by 22.4 % (95 % CI: 16.4 -28.1 %), and inclusion of ED visit data yields a further 5.3 % (95 % CI: 3.0-7.6 %) improvement, whereas added biological complexity produces diminishing or null gains. We further observe a substitution effect in which ED visit data partially compensates for omitted biological structure. We deployed a simplified model variant in the 2025-2026 CDC FluSight Challenge and ranked among the top ensemble performers, supporting the robustness of Bayesian hierarchical training in real time. Together, we find that short-term forecast accuracy is driven more by historical learning and assimilating auxiliary signals than by biological fidelity.