# Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility

arXiv: https://arxiv.org/abs/2306.14857 · HTML: https://mlsanity.com/paper/2306.14857/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2023.

## Built on by (most cited first, 6 of 6)

- [Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks](https://mlsanity.com/paper/2503.23167/) (2025)
- [Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting](https://mlsanity.com/paper/2602.22270/) (2026)
- [Backbone-based Dynamic Graph Spatio-Temporal Network for Epidemic Forecasting](https://mlsanity.com/paper/2312.00485/) (2023)
- [Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecasting](https://mlsanity.com/paper/2411.17372/) (2024)
- [Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks](https://mlsanity.com/paper/2501.02043/) (2025)
- [On the Nonasymptotic Scaling Guarantee of Hyperparameter Estimation in Inhomogeneous, Weakly-Dependent Complex Network Dynamical Systems](https://mlsanity.com/paper/2601.15603/) (2026)

## Abstract (arXiv)

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.