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Title: Using Mobility Data to Understand and Forecast COVID19 Dynamics
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.
Authors:
; ; ; ; ; ; ; ; ; ; ; ; ;
Award ID(s):
1633028 1916805 1918656 2028004 2027541
Publication Date:
NSF-PAR ID:
10213763
Journal Name:
medRxiv
Sponsoring Org:
National Science Foundation
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