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. 2019 Nov 25;20(Suppl 18):575.
doi: 10.1186/s12859-019-3131-8.

Attention-based recurrent neural network for influenza epidemic prediction

Xianglei Zhu  1 , Bofeng Fu  1 , Yaodong Yang  1 , Yu Ma  2 , Jianye Hao  3 , Siqi Chen  1 , Shuang Liu  1 , Tiegang Li  2 , Sen Liu  4 , Weiming Guo  4 , Zhenyu Liao  5   6
Affiliations

Attention-based recurrent neural network for influenza epidemic prediction

Xianglei Zhu et al. BMC Bioinformatics. .

Abstract

Background: Influenza is an infectious respiratory disease that can cause serious public health hazard. Due to its huge threat to the society, precise real-time forecasting of influenza outbreaks is of great value to our public.

Results: In this paper, we propose a new deep neural network structure that forecasts a real-time influenza-like illness rate (ILI%) in Guangzhou, China. Long short-term memory (LSTM) neural networks is applied to precisely forecast accurateness due to the long-term attribute and diversity of influenza epidemic data. We devise a multi-channel LSTM neural network that can draw multiple information from different types of inputs. We also add attention mechanism to improve forecasting accuracy. By using this structure, we are able to deal with relationships between multiple inputs more appropriately. Our model fully consider the information in the data set, targetedly solving practical problems of the Guangzhou influenza epidemic forecasting.

Conclusion: We assess the performance of our model by comparing it with different neural network structures and other state-of-the-art methods. The experimental results indicate that our model has strong competitiveness and can provide effective real-time influenza epidemic forecasting.

Keywords: Attention mechanism; Influenza epidemic prediction; Multi-channel LSTM neural network.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The flowchart of Attention-based multi-channel LSTM
Fig. 2
Fig. 2
The structure of single LSTM cell
Fig. 3
Fig. 3
The diagram of attention mechanism. Attention layer calculates the weighted distribution of X1, …, XT. The input of St contains the output of the attention layer. The probability of occurrence of the output sequence …, yt−1, yt, … depends on input sequence X1, X2, …, XT. hi represents the hidden vector. At,i represents the weight of ith input at time step t
Fig. 4
Fig. 4
The structure of Attention-based multi-channel LSTM
Fig. 5
Fig. 5
The results of one-week ahead prediction by using four individual models. a shows the comparison of Att-MCLSTM and real data; b shows the comparison of MCLSTM and real data; c shows the comparison of LSTM and real data; d shows the comparison of traditional RNN and real data. In each figure, the blue line denotes the actual values, and the orange line denotes the predicted values
See this image and copyright information in PMC

References

    1. Yang S, Santillana M, Kou SC. Accurate estimation of influenza epidemics using google search data via argo. Proc Natl Acad Sci. 2015;112(47):14473–8. doi: 10.1073/pnas.1515373112. - DOI - PMC - PubMed
    1. Brownstein JS, Mandl KD. Reengineering real time outbreak detection systems for influenza epidemic monitoring. In: AMIA Annual Symposium Proceedings, vol. 2006. American Medical Informatics Association: 2006. p. 866. - PMC - PubMed
    1. Organization WH, et al.Who interim global epidemiological surveillance standards for influenza. 2012:1–61.
    1. Santillana M, Zhang DW, Althouse BM, Ayers JW. What can digital disease detection learn from (an external revision to) google flu trends? Am J Prev Med. 2014;47(3):341–7. doi: 10.1016/j.amepre.2014.05.020. - DOI - PubMed
    1. Achrekar H, Gandhe A, Lazarus R, Yu S. -H., Liu B. Predicting flu trends using twitter data. In: Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference On. IEEE: 2011. p. 702–7. 10.1109/infcomw.2011.5928903.

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