Enhancing Influenza-Like Illness forecasting: An ensemble approach combining mathematical and deep learning models amidst the COVID-19 pandemic.

BACKGROUND

Timely and accurate short-term forecasting of Influenza-Like Illness (ILI) is crucial for guiding outbreak response, optimizing healthcare resource allocation, and informing public health interventions. The COVID-19 pandemic, which disrupted seasonal ILI dynamics due to widespread nonpharmaceutical interventions (NPI), underscored the urgent need for adaptive and reliable forecasting frameworks.

METHOD: In this study, we present a novel ensemble modeling approach that combines a mechanistic n-subepidemic model with a Monte Carlo Dropout Long Short-Term Memory (LSTM) neural network to improve age-specific ILI forecasting performance in South Korea. By capturing both the structured dynamics of disease spread and nonlinear temporal dependencies, our ensemble method adapts to pandemic-altered transmission patterns while offering robust uncertainty quantification. Age-stratified forecasting allows the framework to capture heterogeneity in vulnerability and transmission across demographic groups, providing more targeted insights for policy and planning.

RESULT: We evaluated forecasting performance across four epidemic waves using the Weighted Interval Score (WIS), Mean Absolute Error (MAE), consistently finding that the ensemble models outperformed individual approaches.

CONCLUSION

These findings underscore the power of hybrid forecasting approaches to improve epidemic preparedness and response, providing a flexible data-driven framework that can evolve with changing transmission dynamics and extend to other emerging infectious threats.