Self-Attention-Based Deep Learning Network for Regional Influenza Forecasting

Seungwon Jung, Jaeuk Moon, Sungwoo Park, Eenjun Hwang

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Early prediction of influenza plays an important role in minimizing the damage caused, as it provides the resources and time needed to formulate preventive measures. Compared to traditional mechanistic approach, deep/machine learning-based models have demonstrated excellent forecasting performance by efficiently handling various data such as weather and internet data. However, due to the limited availability and reliability of such data, many forecasting models use only historical occurrence data and formulate the influenza forecasting as a multivariate time-series task. Recently, attention mechanisms have been exploited to deal with this issue by selecting valuable data in the input data and giving them high weights. Particularly, self-attention has shown its potential in various forecasting tasks by utilizing the predictive relationship between objects from the input data describing target objects. Hence, in this study, we propose a forecasting model based on self-attention for regional influenza forecasting, called SAIFlu-Net. The model exploits a long short-term memory network for extracting time-series patterns of each region and the self-attention mechanism to find the similarities between the occurrence patterns. To evaluate its performance, we conducted extensive experiments with existing forecasting models using weekly regional influenza datasets. The results show that the proposed model outperforms other models in terms of root mean square error and Pearson correlation coefficient.

Original languageEnglish
Pages (from-to)922-933
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number2
Publication statusPublished - 2022 Feb 1

Bibliographical note

Funding Information:
Manuscript received March 13, 2021; revised May 15, 2021 and June 16, 2021; accepted June 27, 2021. Date of publication July 1, 2021; date of current version February 4, 2022. This work was supported by a Government-Wide R&D Fund Project for Infectious Disease Research (GFID), Republic of Korea under Grant HG19C0682. (Corresponding author: Eenjun Hwang.) The authors are with the School of Electrical Engineering, Korea University, Seoul 02841, South Korea (e-mail: jsw161@korea.;;; ehwang04@ Digital Object Identifier 10.1109/JBHI.2021.3093897

Publisher Copyright:
© 2013 IEEE.


  • Artificial neural networks
  • deep learning
  • regional influenza forecasting
  • self-attention

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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