STAGCN: Spatial-Temporal Attention Based Graph Convolutional Networks for COVID-19 ForecastingDownload PDF

Published: 02 Mar 2023, Last Modified: 25 Apr 20232023 ICLR - MLGH OralReaders: Everyone
Keywords: covid-19 forecasting, attention, graph neural networks, spatio-temporal, graph convolution, graph modeling
TL;DR: We propose to consider the proximity of infection hotspots into accout when forecasting covid-19 infections. To this end, we model the problem on graphs, and propose spatio-temporal attention based GCN to solve this problem.
Abstract: The recent outbreak of the novel coronavirus known as the COVID-19 pandemic has harmed the lives of millions of people across the globe and has imposed a sig- nificant threat to global healthcare due to its severe transmission capacity. It’s of utmost importance to be able to accurately forecast the COVID-19 pandemic and to provide the necessary precautionary measures to protect the health of individu- als and prevent the spread of this deadly widespread virus. In this paper, we pro- pose to forecast the upcoming newly infected patients that are likely to be affected by COVID-19 in prior using a novel deep learning framework, Spatio-Temporal Attention Based Graph Convolution Networks (STAGCN) to effectively make use of spatial and temporal relationships. Instead of using traditional time-series fore- casting techniques at a single city using raw data, we model the problem using graphs and aim at taking into account the dependency that an infection in one city has on its neighbors. Our experiments show that STAGCN effectively captures this dependency and consistently outperforms the other conventional methods.
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