@conference {1548848,
	title = {Using Mobility Data to Understand and Forecast COVID19 Dynamics},
	booktitle = {IJCAI 2021 Workshop on AI for Social Good},
	year = {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.},
	author = {Lijing Wang and Xue Ben and Aniruddha Adiga and Adam Sadilek and Ashish Tendulkar and Srinivasan Venkatramanan and Anil Vullikanti}
}
