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Oriented transformer for infectious disease case prediction

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Abstract

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.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Fujian Province (CN) (nos. 2021J01857, 2021J01859, and 2022J01335). Thanks to the Xiamen City Center for Disease Control and Prevention (XMCDC) for sharing the data. We gratefully appreciate the editor and anonymous reviewers for their valuable insights and suggestions which enormously benefited this paper.

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Authors and Affiliations

  1. College of Computer Engineering, Jimei University, Yinjiang Road 185, Xiamen, 361021, China

    Zhijin Wang & Yonggang Fu

  2. School of Science, Jimei University, Yinjiang Road 185, Xiamen, 361021, China

    Pesiong Zhang

  3. College of Electronic Information, Guangxi Minzu University, Daxue East Road 188, Nanning, 530006, China

    Yaohui Huang

  4. School of Computer Science and Technology, Harbin Institute of Technology, 2 West Culture Road, Weihai, 264209, People’s Republic of China

    Guoqing Chao

  5. School of Information Science and Engineering, Ningbo University, Fenghua Road 818, Ningbo, 315211, China

    Xijiong Xie

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  1. Zhijin Wang
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Wang, Z., Zhang, P., Huang, Y. et al. Oriented transformer for infectious disease case prediction. Appl Intell 53, 30097–30112 (2023). https://doi.org/10.1007/s10489-023-05101-6

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