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

The 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 scal- able graph features from mobility networks. We utilized 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 control- ling infection spread. Our proposed methodology provides a useful approach for tracking the trajectory of infectious diseases and developing evidence-based interventions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement We acknowledge the funding support from Facebook (Meta), the Delhi Cluster-Delhi Research Implementation and Innovation (DRIIV) Project, which is supported by the Principal Scientific Advisor Office under grant number Prn.SA/Delhi/Hub/2018(C), and the Center of Excellence in Healthcare, which is supported by the Delhi Knowledge Development Foundation (DKDF) under grant number Prn. SRP206 at IIIT-Delhi. Additionally, we would like to acknowledge the support received from MongoDB. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Travel Patterns data are available to nonprofits and researchers who sign data-sharing agreements with Meta.