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The spread of COVID-19 has coincided with the rise of Graph Neural Networks (GNNs), leading to several studies proposing their use to better forecast the evolution of the pandemic.
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Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2008
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Inductive representation learning on large graphs
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Li, Y., Yu, R., Shahabi, C., and Liu, Y · 2017
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Gc-lstm: Graph convolution embedded lstm for dynamic link prediction
Chen, J., Xu, X., Wu, Y., and Zheng, H · 2018
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Structured sequence modeling with graph convolutional recurrent networks
Seo, Y., Defferrard, M., Vandergheynst, P., and Bresson, X · 2018
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Covid-19 prediction and detection using deep learning
Alazab, M., Awajan, A., Mesleh, A., Abraham, A., Jatana, V., and Alhyari, S · 2020
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Artificial intelligence and machine learning to fight covid-19
Alimadadi, A., Aryal, S., Manandhar, I., Munroe, P. B., Joe, B., and Cheng, X · 2020
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Prediction and analysis of covid-19 positive cases using deep learning models: A descriptive case study of india
Arora, P., Kumar, H., and Panigrahi, B. K · 2020
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Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods
Barstugan, M., Ozkaya, U., and Ozturk, S · 2020
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Aggregated mobility data could help fight covid-19
Buckee, C. O., Balsari, S., Chan, J., Crosas, M., Dominici, F., Gasser, U., Grad, Y. H., Grenfell, B., Halloran, M. E., Kraemer, M. U. G., Lipsitch, M., Metcalf, C. J. E., Meyers, L. A., Perkins, T. A., Santillana, M., Scarpino, S. V., Viboud, C., Wesolowski, A., and Schroeder, A · 2020
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Time series forecasting of covid-19 transmission in canada using lstm networks
Chimmula, V. K. R. and Zhang, L · 2020
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Predictions for covid-19 with deep learning models of lstm, gru and bi-lstm
Shahid, F., Zameer, A., and Muneeb, M · 2020
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The covid-19 epidemic
Velavan, T. P. and Meyer, C. G · 2020
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Spectral temporal graph neural network for multivariate time-series forecasting
Cao, D., Wang, Y., Duan, J., Zhang, C., Zhu, X., Huang, C., Tong, Y., Xu, B., Bai, J., Tong, J., et al · 2021
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An interactive web-based dashboard to track covid-19 in real time
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The scale and dynamics of covid-19 epidemics across europe
Dye, C., Cheng, R. C., Dagpunar, J. S., and Williams, B. G · 2020
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Covid-19 — navigating the uncharted
Fauci, A. S., Lane, H. C., and Redfield, R. R · 2020
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Early transmission dynamics in wuhan, china, of novel coronavirus–infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S., Lau, E. H., Wong, J. Y., Xing, X., Xiang, N., Wu, Y., Li, C., Chen, Q., Li, D., Liu, T., Zhao, J., Liu, M., Tu, W., Chen, C., Jin, L., Yang, R., Wang, Q., Zhou, S., Wang, R., Liu, H., Luo, Y., Liu, Y., Shao, G., Li, H., Tao, Z., Yang, Y., Deng, Z., Liu, B., Ma, Z., Zhang, Y., Shi, G., Lam, T. T., Wu, J. T., Gao, G. F., Cowling, B. J., Yang, B., Leung, G. M., and Feng, Z · 2020
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Covid-19 in europe: the italian lesson
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