Abstract
Effective infectious disease prediction supports the success of infection prevention and control. Several attention-based predictive models can be applied to undertake the prediction task. However, using a single attention mechanism can only capture local information, i.e. part of the temporal dynamics from time series. In this paper, we take for the hypothesis that using multiple attention from different aspects could improve prediction accuracy. An oriented attention model (OAM) is proposed to draw temporal dynamics in several aspects, via oriented attention units and their aggregation. Firstly, time series are represented as oriented transformations. And then those representations are consolidated to connect with outputs. Intensive experiments on two real infectious disease datasets show OAM’s effectiveness.
Supported in part by the Natural Science Foundation of Fujian Province (CN) (no. 2021J01859) and the Innovation School Project of Guangdong Province (CN) (no. 2017KCXTD015).
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References
Overview of the national epidemic situation of notifiable infectious diseases in 2020 (2022). http://www.nhc.gov.cn/jkj/s3578/202103/f1a448b7df7d4760976fea6d55834966.shtml. Accessed Jan 2022
Hoseinzade, E., Haratizadeh, S.: CNNPRED: CNN-based stock market prediction using a diverse set of variables. Expert Syst. Appl. 129, 273–285 (2019)
Hu, D.: An introductory survey on attention mechanisms in NLP problems. In: INTELLISYS 2019, vol. 1038, pp. 432–448, September 2019
Hua, Y., Zhao, Z., Li, R., Chen, X., Liu, Z., Zhang, H.: Deep learning with long short-term memory for time series prediction. IEEE Commun. Mag. 57(6), 114–119 (2019)
Huang, S., Wang, D., Wu, X., Tang, A.: DSANet: dual self-attention network for multivariate time series forecasting. In: CIKM 2019, pp. 2129–2132, November 2019
Keddy, K.H., et al.: Using big data and mobile health to manage diarrhoea disease in children in low-income and middle-income countries: societal barriers and ethical implications. Lancet Infect. Dis. (2021)
Lai, G., Chang, W., Yang, Y., Liu, H.: Modeling long- and short-term temporal patterns with deep neural networks. In: SIGIR 2018, pp. 95–104 (2018)
Mabrouk, A.B., Abdallah, N.B., Dhifaoui, Z.: Wavelet decomposition and autoregressive model for time series prediction. Appl. Math. Comput. 199(1), 334–340 (2008)
Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., Cottrell, G.W.: A dual-stage attention-based recurrent neural network for time series prediction. In: IJCAI 2017, pp. 2627–2633 (2017)
Shah, W., et al.: A machine-learning-based system for prediction of cardiovascular and chronic respiratory diseases. J. Healthc. Eng. (2021)
Shih, S., Sun, F., Lee, H.: Temporal pattern attention for multivariate time series forecasting. Mach. Learn. 108(8–9), 1421–1441 (2019)
Vaswani, A., et al.: Attention is all you need. CoRR abs/1706.03762 (2017)
Wang, Y., Gu, J., Zhou, Z., Wang, Z.: Diarrhoea outpatient visits prediction based on time series decomposition and multi-local predictor fusion. Knowl.-Based Syst. 88, 12–23 (2015)
Wang, Y., Li, J., Gu, J., Zhou, Z., Wang, Z.: Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China). Appl. Soft Comput. 35, 280–290 (2015)
Wang, Z., Cai, B.: COVID-19 cases prediction in multiple areas via shapelet learning. Appl. Intell. 52(1), 595–606 (2021). https://doi.org/10.1007/s10489-021-02391-6
Wang, Z., Huang, Y., Cai, B., Ma, R., Wang, Z.: Stock turnover prediction using search engine data. J. Circuits Syst. Comput. 30(7), 2150122:1–2150122:18 (2021)
Wang, Z., Huang, Y., He, B.: Dual-grained representation for hand, foot, and mouth disease prediction within public health cyber-physical systems. Softw. Pract. Exp. 51, 2290–2305 (2021)
Wang, Z., Huang, Y., He, B., Luo, T., Wang, Y., Fu, Y.: Short-term infectious diarrhea prediction using weather and search data in Xiamen, China. Sci. Program. 2020, 8814222:1–8814222:12 (2020)
Wang, Z., Huang, Y., He, B., Luo, T., Wang, Y., Lin, Y.: TDDF: HFMD outpatients prediction based on time series decomposition and heterogenous data fusion in Xiamen, China. In: ADMA 2019, Dalian, China, pp. 658–667, November 2019
Wang, Z., Su, Q., Chao, G., Cai, B., Huang, Y., Fu, Y.: A multi-view time series model for share turnover prediction. Appl. Intell. Early View (2022)
Wang, Z., et al.: Prediction of HFMD cases by leveraging time series decomposition and local fusion. Wirel. Commun. Mob. Comput. 2021, 5514743:1–5514743:10 (2021)
Zhu, X., et al.: Attention-based recurrent neural network for influenza epidemic prediction. BMC Bioinform. 20-S(18), 575:1–575:10 (2019)
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Zhang, P., Wang, Z., Chao, G., Huang, Y., Yan, J. (2022). An Oriented Attention Model for Infectious Disease Cases Prediction. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_11
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