Work

Domain-adaptive epidemic forecasting with hybrid SEIR-N-BEATS framework for robust cross-regional prediction under distribution shifts

Year: 2026

Type: article

Abstract: Epidemic forecasting across heterogeneous regions faces significant challenges due to domain shifts in healthcare infrastructure, population demographics, and intervention policies. We present SEIR-N-BEATS, a hybrid transfer learning framework integrating epidemiological theory with neural pattern recognition for robust cross-domain prediction. Our approach combines a modified susceptible-exposed-infectious-removed (SEIR) model with neural basis expansion analysis for time series (N-BEATS) to address domain adaptation challenges. The SEIR component incorporates time-varying transmission rates and policy-aware mechanisms to capture distributional shifts, while N-BEATS learns domain-specific residual patterns through hierarchical blocks with interpretable decomposition. We evaluated cross-domain generalization using epidemic surveillance data from multiple regions with varying healthcare systems (December 2019–May 2022), comparing performance via MAE, RMSE, MAPE, and Pearson correlation under both in-distribution and out-of-distribution scenarios. SEIR-N-BEATS achieved 1.03% MAPE with 19.1% error reduction compared to mechanistic SEIR baselines, demonstrating superior transfer capability across heterogeneous domains (MAE range 1.32–576.39). The framework exhibited exceptional out-of-distribution robustness, maintaining near-zero MAPE variation ( $$0.00\% \pm 0.01\%$$ 0.00 % ± 0.01 % ) under noise perturbations, compared to substantial MAPE degradation in pure machine-learning baselines (LSTM $$39.13\% \pm 2.8\%$$ 39.13 % ± 2.8 % , N-BEATS $$243.23\% \pm 15.2\%$$ 243.23 % ± 15.2 % ). Cross-regional validation confirmed effective domain adaptation with 23.4% average MAE improvement. This framework bridges epidemiological knowledge with data-driven learning, enabling interpretable cross-domain forecasting robust to distribution shifts, offering practical solutions for resource allocation and intervention planning across diverse healthcare settings.

Source: Complex & Intelligent Systems

DOI: 10.1007/s40747-026-02510-1