  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. The
multi-source epidemic-related data and mobility data of Japan are collected and
processed to form the dataset for experiments. The experimental results
demonstrate our model outperforms the existing mechanistic models and deep
learning models by a large margin. Furthermore, the analysis on the learned
parameters illustrate the high reliability and interpretability of our model
and helps better understanding of epidemic spread. In addition, a mobility
generation method is presented to address the issue of unavailable mobility
data, and the experimental results demonstrate effectiveness of the generated
mobility data as an input to our model.
