Enhancing Epidemic Forecasting: Evaluating the Role of Mobility Data and Graph Convolutional Networks

TL;DR

This study evaluates the impact of mobility data and graph convolutional networks on epidemic forecasting, finding that mortality and hospitalization data are more influential than mobility data, and proposing spatial maps as mobility indicators.

Contribution

The paper introduces a novel perspective on incorporating mobility data into epidemic forecasting models, emphasizing the use of spatial maps as sensitive mobility indicators.

Findings

Mobility data and GCN modules do not significantly improve forecasting accuracy.

Including mortality and hospitalization data markedly enhances model performance.

Spatial maps derived from GCNs correlate with lockdown measures, serving as mobility indicators.

Abstract

Accurate prediction of contagious disease outbreaks is vital for informed decision-making. Our study addresses the gap between machine learning algorithms and their epidemiological applications, noting that methods optimal for benchmark datasets often underperform with real-world data due to difficulties in incorporating mobility information. We adopt a two-phase approach: first, assessing the significance of mobility data through a pilot study, then evaluating the impact of Graph Convolutional Networks (GCNs) on a transformer backbone. Our findings reveal that while mobility data and GCN modules do not significantly enhance forecasting performance, the inclusion of mortality and hospitalization data markedly improves model accuracy. Additionally, a comparative analysis between GCN-derived spatial maps and lockdown orders suggests a notable correlation, highlighting the potential of spatial maps as sensitive indicators for mobility. Our research offers a novel perspective on mobility representation in predictive modeling for contagious diseases, empowering decision-makers to better prepare for future outbreaks.