Selected article for: "early forecast and forecasting performance"
Author: Wang, Lijing; Ben, Xue; Adiga, Aniruddha; Sadilek, Adam; Tendulkar, Ashish; Venkatramanan, Srinivasan; Vullikanti, Anil; Aggarwal, Gaurav; Talekar, Alok; Chen, Jiangzhuo; Lewis, Bryan; Swarup, Samarth; Kapoor, Amol; Tambe, Milind; Marathe, Madhav
Title: Using Mobility Data to Understand and Forecast COVID19 Dynamics
  • Cord-id: ojturyub
  • Document date: 2020_12_15
    • ID: ojturyub
    Snippet: 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 me
    Document: 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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