EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting

Aron, J.L., Schwartz, I.B.: Seasonality and period-doubling bifurcations in an epidemic model. Journal of theoretical biology (1984)

1984

Earlier work this paper cites.

Zhang, G.P.: Time series forecasting using a hybrid arima and neural network model. Neurocomputing (2003)

2003

Earlier work this paper cites.

Wang, Z., Chakraborty, P., Mekaru, S.R., Brownstein, J.S., Ye, J., Ramakrishnan, N.: Dynamic poisson autoregression for influenza-like-illness case count prediction. In: Proc. of KDD (2015)

2015

Earlier work this paper cites.

Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

Original

2016

Earlier work this paper cites.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Proc. of NeurIPS (2017)

2017

Earlier work this paper cites.

Won, M., Marques-Pita, M., Louro, C., Gonçalves-Sá, J.: Early and real-time detection of seasonal influenza onset. PLoS computational biology (2017)

2017

Earlier work this paper cites.

Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)

Original

2017

Earlier work this paper cites.

Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: Proc. of AAAI (2018)

2018

Cited alongside, same era.

Wu, Y., Yang, Y., Nishiura, H., Saitoh, M.: Deep learning for epidemiological predictions. In: Proc. of SIGIR (2018)

2018

Cited alongside, same era.

Adhikari, B., Xu, X., Ramakrishnan, N., Prakash, B.A.: Epideep: Exploiting embeddings for epidemic forecasting. In: Proc. of KDD (2019)

2019

Cited alongside, same era.

Chakraborty, T., Chattopadhyay, S., Ghosh, I.: Forecasting dengue epidemics using a hybrid methodology. Physica A: Statistical Mechanics and its Applications (2019)

2019

Cited alongside, same era.

Shih, S.Y., Sun, F.K., Lee, H.y.: Temporal pattern attention for multivariate time series forecasting. Machine Learning (2019)

2019

Cited alongside, same era.

Han, X., Xu, Y., Fan, L., Huang, Y., Xu, M., Gao, S.: Quantifying covid-19 importation risk in a dynamic network of domestic cities and international countries. Proceedings of the National Academy of Sciences (2021)

2021

Later among the works it cites.

Jin, X., Wang, Y.X., Yan, X.: Inter-series attention model for covid-19 forecasting. In: Proc. of SDM (2021)

2021

Later among the works it cites.

Jung, S., Moon, J., Park, S., Hwang, E.: Self-attention-based deep learning network for regional influenza forecasting. IEEE JBHI (2021)

2021

Later among the works it cites.

Panagopoulos, G., Nikolentzos, G., Vazirgiannis, M.: Transfer graph neural networks for pandemic forecasting. In: Proc. of AAAI (2021)

2021

Later among the works it cites.

Zhang, H., Xu, Y., Liu, L., Lu, X., Lin, X., Yan, Z., Cui, L., Miao, C.: Multi-modal information fusion-powered regional covid-19 epidemic forecasting. In: Proc. of BIBM (2021)