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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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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