Frequency-Modulated Transformer Self-Attention for Advanced Infectious Disease Prediction

Time series forecasting of infectious diseases is crucial to addressing significant global health challenges. The application of AI, particularly Deep Learning (DL) algorithms, has demonstrated substantial success in sequence modeling; however, their performance in epidemiological forecasting remains constrained by the non-stationary nature and complex frequency dynamics of disease transmission. This research introduces a novel Modulated Transformer (FMT) framework to address these challenges. The FMT framework decomposes time series data into distinct frequency-modulated signals, utilizing self-attention mechanisms to capture temporal frequencies. A transformer encoder–decoder architecture then predicts and captures multi-scale temporal dependencies and makes accurate predictions. The novelty of the proposed approach lies in decomposing the input time series into Intrinsic Mode Functions (IMFs) and integrating the frequency-specific components into a Transformer architecture via entropy-based feature selection. The FMT framework significantly reduces Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) by approximately 50% and 65%, respectively, compared to conventional methods. Additionally, it achieves an 8% increase in the R2 score, demonstrating enhanced predictive accuracy. The proposed methodology is evaluated using COVID-19 datasets from multiple countries along with an influenza dataset and benchmarked against statistical, machine learning, and state-of-the-art DL baselines. The contribution of entropy-based IMF integration is systematically examined by comparing results with and without this component, underscoring its importance in improving predictive accuracy. This work highlights substantial improvements in predictive accuracy and computational efficiency, advancing epidemiological forecasting and supporting real-time public health decision-making and AI-driven disease surveillance systems.