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?:abstract
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Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.
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?:doi
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10.1101/2020.12.13.20248129
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document_parses/pdf_json/dd8b838a5eb538cd6fd3984d3f86ce5c7b949d93.json; document_parses/pdf_json/ae41cba7a0327a475bfb4b3e2d6bc507c9358ead.json
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document_parses/pmc_json/PMC7755147.xml.json
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MedRxiv; Medline; PMC; WHO
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?:title
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Using Mobility Data to Understand and Forecast COVID19 Dynamics
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