A Graph Embedding Approach for Deciphering the Longitudinal Associations of Global Mobility and COVID-19 Cases

Abstract

AbstractThe COVID-19 pandemic has highlighted the importance of monitoring mobility patterns and their impact on disease spread. This paper presents a methodology for developing effective pandemic surveillance systems by extracting scalable graph features from mobility networks. We utilized Meta’s “Travel Patterns” dataset to capture the daily number of individuals traveling between countries from March 2020 to April 2022. We have used an optimized node2vec algorithm to extract scalable features from the mobility networks. Our analysis revealed that movement embeddings accurately represented the movement patterns of countries, with geographically proximate countries exhibiting similar movement patterns. The temporal association dynamics between Global mobility and COVID-19 cases highlighted the significance of high-page rank centrality countries in mobility networks as a key intervention target in controlling infection spread. Our proposed methodology provides a useful approach for tracking the trajectory of infectious diseases and developing evidence-based interventions.