MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks

Cao，Qi1; Jiang，Renhe1; Yang，Chuang1; Fan，Zipei1; Song，Xuan1,2; Shibasaki，Ryosuke1

10.1007/978-3-031-26422-1_28

2023

0302-9743

1611-3349

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

13718 LNAI

453-468

Epidemic prediction is a fundamental task for epidemic control and prevention. Many mechanistic models and deep learning models are built for this task. However, most mechanistic models have difficulty estimating the time/region-varying epidemiological parameters, while most deep learning models lack the guidance of epidemiological domain knowledge and interpretability of prediction results. In this study, we propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting by incorporating Graph Neural Networks (GNNs) and graph learning mechanisms into Metapopulation SIR model. Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters and the underlying epidemic propagation graph from heterogeneous data in an end-to-end manner. Experiment results demonstrate our model outperforms the existing mechanistic models and deep learning models by a large margin. Furthermore, the analysis on the learned parameters demonstrates the high reliability and interpretability of our model and helps better understanding of epidemic spread. Our model and data have already been public on GitHub https://github.com/deepkashiwa20/MepoGNN.git.

COVID-19 Deep learning Epidemic forecasting Graph Neural Networks Hybrid model Metapopulation epidemic model

Others

English

2-s2.0-85150952465

Scopus

1.The University of Tokyo,Tokyo,Japan
2.Southern University of Science and Technology,Shenzhen,China

Cao，Qi,Jiang，Renhe,Yang，Chuang,et al. MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks[C],2023:453-468.