TRAGIC: An Advanced Transformer–GRU Fusion Model with Self-Attention for Monkeypox Mortality Forecasting

Monkeypox (Mpox), a zoonotic Orthopoxvirus disease, was declared a Public Health Emergency of International Concern by the WHO in 2022. While the global fatality rate is approximately 0.7%, mortality risks vary significantly across regions and remain high for vulnerable groups, necessitating precise predictive models for public health resource allocation. This study proposes TRAGIC, a novel fusion model integrating Transformer, GRU, and Quick Attention mechanisms to predict monkeypox death cases. Utilizing a global dataset with a comprehensive set of 16 input features, the TRAGIC model was benchmarked against traditional GRU, LSTM, Transformer, and Trans-GRU architectures. Experimental results demonstrate that TRAGIC consistently outperforms existing deep learning models, particularly in capturing non-linear patterns and long-term dependencies in epidemic time-series data. The findings suggest that the TRAGIC model offers superior accuracy and stability, providing a robust tool for forecasting infectious disease mortality and supporting global health policymaking.