Oriented transformer for infectious disease case prediction.

Accurate prediction of infectious disease cases plays a crucial role in achieving effective infection prevention and control. However, the inherent variability of incubation periods and progression dynamics of infectious diseases pose significant challenges to the accuracy of predicting multiple diseases. Multiple representation fusion (MRF) methods would improve the performance of prediction models, due to their capability to capture diverse temporal dependencies that reflect potential disease transmission patterns. But the traditional fusion approach for infectious disease prediction still faces many challenges, including the requirement of auxiliary data, vulnerability to disease evolution, and lack of intuitive explanation. To address these challenges, this paper proposes an oriented transformer (ORIT) for infectious diseases case prediction. Contrary to traditional MRF structures that integrate representations from multiple data sources, the MRF in the proposed ORIT combines multi-orientation context vectors solely by capturing multi-dimensional temporal relationships within disease case data. Furthermore, this paper considers the heterogeneity of the incubation period in the prediction of different infectious disease cases. Lastly, this paper conducts comprehensive experiments to evaluate the proposed method using two real datasets of infectious diseases, and compares it with 21 well-known prediction methods. The experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]