MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks-猫眼课题宝

DOI：

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

发表时间：

2023-06

期刊：

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其他

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作者：

Qi Cao;Renhe Jiang;Chuang Yang;Z. Fan;Xuan Song;R. Shibasaki

疫情预测是疫情防控的基础性工作。许多机械模型和深度学习模型都是为了这个任务而建立的。然而，大多数机制模型难以估计随时间/区域变化的流行病学参数，而大多数深度学习模型缺乏流行病学领域知识的指导和预测结果的可解释性。在这项研究中，我们提出了一种新的混合模型称为MepoGNN的多步骤多区域流行病预测，将图神经网络（GNNs）和图学习机制整合到MetapopulsSIR模型。我们的模型不仅可以预测确诊病例的数量，还可以以端到端的方式从异构数据中显式地学习流行病学参数和潜在的流行病传播图。实验结果表明，我们的模型优于现有的机械模型和深度学习模型的大幅度。此外，对学习参数的分析表明，我们的模型具有很高的可靠性和可解释性，有助于更好地理解流行病的传播。我们的模型和数据已经在GitHub https://github.com/deepkashiwa20/MepoGNN.git上公开。

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.

MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks

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