Using Mobility Data to Understand and Forecast COVID19 Dynamics

Citation:

Wang L, Ben X, Adiga A, Sadilek A, Tendulkar A, Venkatramanan S, Vullikanti A. Using Mobility Data to Understand and Forecast COVID19 Dynamics, in IJCAI 2021 Workshop on AI for Social Good. ; 2021.

Abstract:

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 forecast- ing. 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 exist- ing 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.