Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

2020-07-06 · Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais, Shawn O'Banion

In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecasting models, the proposed approach learns from a single large-scale spatio-temporal graph, where nodes represent the region-level human mobility, spatial edges represent the human mobility based inter-region connectivity, and temporal edges represent node features through time. We evaluate this approach on the US county level COVID-19 dataset, and demonstrate that the rich spatial and temporal information leveraged by the graph neural network allows the model to learn complex dynamics. We show a 6% reduction of RMSLE and an absolute Pearson Correlation improvement from 0.9978 to 0.998 compared to the best performing baseline models. This novel source of information combined with graph based deep learning approaches can be a powerful tool to understand the spread and evolution of COVID-19. We encourage others to further develop a novel modeling paradigm for infectious disease based on GNNs and high resolution mobility data.

📄 PDF Abstract BibTeX arXiv:2007.03113

Code (1)

wpower12/COVID-19-USSTN pytorch

Tasks

Graph Neural NetworkTime SeriesTime Series Analysis Time Series Forecasting

Methods 이 논문이 사용한 방법론

Graph Neural Network 설명 없음

Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting

2020-10-27 · Lijing Wang, Aniruddha Adiga, Srinivasan Venkatramanan, Jiangzhuo Chen 외

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 spatio-temporal forecasting of epidemic dynamics is c…

Clustering Deep Learning Spatio-Temporal Forecasting Time Series+1

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

2021-09-24 · Benjamin Lucas, Behzad Vahedi, Morteza Karimzadeh

With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeli…

Spatio-temporal stochastic graph-based learning for infectious disease forecasting

2026-05-28 · Luz Stefani Sotomayor Valenzuela, Susanna Cramb, Darren Wraith arxiv

Spatio-temporal graph-based models have typically been used to forecast new cases of infectious diseases such as COVID-19 and chickenpox outbreaks. However, the use of stochastic modelling into their learning process has…

Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19

2021-08-15 · Shuhui Gong, Xiaopeng Mo, Rui Cao, Yu Liu 외

Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours. However, previous …

Demand Forecasting Epidemiology graph construction Spatio-Temporal Forecasting

TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Data

2024-08-29 · Zakaria Elabid, Lena Sasal, Daniel Busby, Abdenour Hadid

Accurately forecasting dynamic processes on graphs, such as traffic flow or disease spread, remains a challenge. While Graph Neural Networks (GNNs) excel at modeling and forecasting spatio-temporal data, they often lack …

Graph Neural Network Prediction