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EpiRiskNet: incorporating graph structure and static data as prior knowledge for improved time-series forecasting

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Abstract

EpiRiskNet combines time-series data with graph and static information to enhance forecasting accuracy. This model features the SCI-Block for improved feature extraction and interaction learning, leveraging the capabilities of SCINet and Triformer to manage diverse feature scales. The model’s standout attribute, scalability, is driven by Triformer’s Patch Attention mechanism, ensuring efficient processing of large-scale data. EpiRiskNet was tested across several locations, including Liaoning, Chongqing, Heilongjiang, and Guangxi, where it demonstrated greater accuracy than other methods. This accuracy is crucial for effectively forecasting disease risks. The model’s adaptability to various regional conditions underscores its significance in public health and epidemiology. Moreover, its modular and flexible design makes EpiRiskNet suitable for a wide range of applications that require advanced data processing and predictive analytics.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by grants from the (i) Technical Field Fund of the Basic Strengthening Plan of the Military Science and Technology Commission (2021-JCJQ-JJ-0528) (ii) The Project of Beijing Science and Technology Characteristics" (Z181100001718007). (iii) Construction Project of Military Teachings of PLA Medical College (145bx1090009000x). (iv) Central Military Health Care Commission (20BJZ46).

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Author notes

  1. Yayong Shi and Qiao Chen contributed equally to this work.

Authors and Affiliations

  1. Department of Epidemiology and Statistics, The Graduate School Chinese PLA General Hospital, Beijing, 100853, China

    Yayong Shi, Qiao Chen, Qiongxuan Li, Hengyu Luan, Qiao Wang, Yeyuan Hu & Xiaoyong Sai

  2. Department of Nutrition, Third Medical Center of PLA General Hospital, Beijing, 100039, P. R., China

    Qiao Chen

  3. School of Computer and Communication Engineering, University of Science and Technology, Beijing, 100083, China

    Yayong Shi

  4. Health Care Division, Security Bureau of Headquarters of the General Staff, Beijing, 100017, P. R., China

    Feng Gao

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  1. Yayong Shi
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Correspondence to Xiaoyong Sai.

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Shi, Y., Chen, Q., Li, Q. et al. EpiRiskNet: incorporating graph structure and static data as prior knowledge for improved time-series forecasting. Appl Intell 54, 7864–7877 (2024). https://doi.org/10.1007/s10489-024-05514-x

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