Leveraging graph neural networks and mobility data for COVID-19 forecasting

2025-01-20

AI Summary

This study addresses the insufficient modeling of human mobility dynamics in cross-national spatiotemporal forecasting of COVID-19 cases. We propose a mobility-aware prediction framework based on graph neural networks (GCRN/GCLSTM). Our method introduces three key innovations: (1) a mobility-network backbone extraction strategy to enhance the stability of dynamic graph structures; (2) a variable sliding-window mechanism to improve generalization across heterogeneous regions—specifically Brazil and China; and (3) reformulation of the regression task as binary classification to significantly improve interpretability and decision support. Experiments demonstrate that our approach reduces RMSE by approximately 80% over baseline models. It achieves stable, qualitatively consistent predictions on both national datasets, with smoother and more reliable classification boundaries.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The COVID-19 pandemic has victimized over 7 million people to date, prompting diverse research efforts. Spatio-temporal models combining mobility data with machine learning have gained attention for disease forecasting. Here, we explore Graph Convolutional Recurrent Network (GCRN) and Graph Convolutional Long Short-Term Memory (GCLSTM), which combine the power of Graph Neural Networks (GNN) with traditional architectures that deal with sequential data. The aim is to forecast future values of COVID-19 cases in Brazil and China by leveraging human mobility networks, whose nodes represent geographical locations and links are flows of vehicles or people. We show that employing backbone extraction to filter out negligible connections in the mobility network enhances predictive stability. Comparing regression and classification tasks demonstrates that binary classification yields smoother, more interpretable results. Interestingly, we observe qualitatively equivalent results for both Brazil and China datasets by introducing sliding windows of variable size and prediction horizons. Compared to prior studies, introducing the sliding window and the network backbone extraction strategies yields improvements of about 80% in root mean squared errors.

Problem

• COVID-19 prediction
• virus spread
• human mobility data

Innovation

• Graph Neural Networks
• COVID-19 Spread Prediction
• Accuracy Optimization