Hybrid modeling and forecasting of COVID-19: integrating SEAIQHRD and GPR for improved predictions
Abstract
This study introduces a dual-hybrid COVID-19 forecasting modeling approach that integrates an eight-compartment SEAIQHRD model with Gaussian Process Regression (GPR) and ARIMA-based residual learning to enhance predictive performance. A central methodological contribution is the incorporation of convergence and stability diagnostics, demonstrating reliable parameter estimation through multi-start optimization and bootstrap analysis. Although the SEAIQHRD model captures core disease progression, it is limited in representing nonlinear multi-wave patterns and reporting inconsistencies. The SEAIQHRD–ARIMA hybrid improves short-term linear adjustments, while the SEAIQHRD–GPR hybrid effectively models nonlinear residual structure and provides uncertainty-aware forecasts. Using COVID-19 data from India, both hybrids outperform the standalone model, with the GPR variant yielding the greatest accuracy. Forecast superiority, confirmed by DM, CW, GW, Wilcoxon, and Friedman tests, underscores the robustness and applicability of the proposed modeling approach for public-health.Clinical trial Not applicable.
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