Temporal Multiresolution Graph Neural Networks For Epidemic Prediction

Truong Son Hy, Viet Bach Nguyen, Long Tran-Thanh, Risi Kondor
Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022, PMLR 184:21-32, 2022.

Abstract

In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph structures and incorporates the time-series signals to capture the temporal changes of the dynamic graphs. We have applied our proposed model to the task of predicting future spreading of epidemic and pandemic based on the historical time-series data collected from the actual COVID-19 pandemic and chickenpox epidemic in several European countries, and have obtained competitive results in comparison to other previous state-of-the-art temporal architectures and graph learning algorithms. We have shown that capturing the multiscale and multiresolution structures of graphs is important to extract either local or global information that play a critical role in understanding the dynamic of a global pandemic such as COVID-19 which started from a local city and spread to the whole world. Our work brings a promising research direction in forecasting and mitigating future epidemics and pandemics. Our source code is available at https://github.com/bachnguyenTE/temporal-mgn.

Cite this Paper

BibTeX
@InProceedings{pmlr-v184-hy22a, title = {Temporal Multiresolution Graph Neural Networks For Epidemic Prediction}, author = {Hy, Truong Son and Nguyen, Viet Bach and Tran-Thanh, Long and Kondor, Risi}, booktitle = {Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022}, pages = {21--32}, year = {2022}, editor = {Xu, Peng and Zhu, Tingting and Zhu, Pengkai and Clifton, David A. and Belgrave, Danielle and Zhang, Yuanting}, volume = {184}, series = {Proceedings of Machine Learning Research}, month = {22 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v184/hy22a/hy22a.pdf}, url = {https://proceedings.mlr.press/v184/hy22a.html}, abstract = {In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph structures and incorporates the time-series signals to capture the temporal changes of the dynamic graphs. We have applied our proposed model to the task of predicting future spreading of epidemic and pandemic based on the historical time-series data collected from the actual COVID-19 pandemic and chickenpox epidemic in several European countries, and have obtained competitive results in comparison to other previous state-of-the-art temporal architectures and graph learning algorithms. We have shown that capturing the multiscale and multiresolution structures of graphs is important to extract either local or global information that play a critical role in understanding the dynamic of a global pandemic such as COVID-19 which started from a local city and spread to the whole world. Our work brings a promising research direction in forecasting and mitigating future epidemics and pandemics. Our source code is available at https://github.com/bachnguyenTE/temporal-mgn.} }
Endnote
%0 Conference Paper %T Temporal Multiresolution Graph Neural Networks For Epidemic Prediction %A Truong Son Hy %A Viet Bach Nguyen %A Long Tran-Thanh %A Risi Kondor %B Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022 %C Proceedings of Machine Learning Research %D 2022 %E Peng Xu %E Tingting Zhu %E Pengkai Zhu %E David A. Clifton %E Danielle Belgrave %E Yuanting Zhang %F pmlr-v184-hy22a %I PMLR %P 21--32 %U https://proceedings.mlr.press/v184/hy22a.html %V 184 %X In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph structures and incorporates the time-series signals to capture the temporal changes of the dynamic graphs. We have applied our proposed model to the task of predicting future spreading of epidemic and pandemic based on the historical time-series data collected from the actual COVID-19 pandemic and chickenpox epidemic in several European countries, and have obtained competitive results in comparison to other previous state-of-the-art temporal architectures and graph learning algorithms. We have shown that capturing the multiscale and multiresolution structures of graphs is important to extract either local or global information that play a critical role in understanding the dynamic of a global pandemic such as COVID-19 which started from a local city and spread to the whole world. Our work brings a promising research direction in forecasting and mitigating future epidemics and pandemics. Our source code is available at https://github.com/bachnguyenTE/temporal-mgn.
APA
Hy, T.S., Nguyen, V.B., Tran-Thanh, L. & Kondor, R.. (2022). Temporal Multiresolution Graph Neural Networks For Epidemic Prediction. Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022, in Proceedings of Machine Learning Research 184:21-32 Available from https://proceedings.mlr.press/v184/hy22a.html.

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