The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatiotemporal forecasting of epidemic dynamics is crucial. Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting. Here we focus on the design and analysis of deep learning-based models for COVID-19 forecasting. We implement multiple recurrent neural network-based deep learning models and combine them using the stacking ensemble technique. In order to incorporate the effects of multiple factors in COVID-19 spread, we consider multiple sources such as COVID-19more »
This content will become publicly available on December 15, 2021
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.
- Publication Date:
- NSF-PAR ID:
- 10213763
- Journal Name:
- medRxiv
- Sponsoring Org:
- National Science Foundation
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