Improving COVID-19 Forecasting using eXogenous Variables

by   Mohammadhossein Toutiaee, et al.
University of Georgia
Penn State University

In this work, we study the pandemic course in the United States by considering national and state levels data. We propose and compare multiple time-series prediction techniques which incorporate auxiliary variables. One type of approach is based on spatio-temporal graph neural networks which forecast the pandemic course by utilizing a hybrid deep learning architecture and human mobility data. Nodes in this graph represent the state-level deaths due to COVID-19, edges represent the human mobility trend and temporal edges correspond to node attributes across time. The second approach is based on a statistical technique for COVID-19 mortality prediction in the United States that uses the SARIMA model and eXogenous variables. We evaluate these techniques on both state and national levels COVID-19 data in the United States and claim that the SARIMA and MCP models generated forecast values by the eXogenous variables can enrich the underlying model to capture complexity in respectively national and state levels data. We demonstrate significant enhancement in the forecasting accuracy for a COVID-19 dataset, with a maximum improvement in forecasting accuracy by 64.58 GCN-LSTM model in the national level data, and 58.79 over the GCN-LSTM model in the state level data. Additionally, our proposed model outperforms a parallel study (AUG-NN) by 27.35 on average.


page 1

page 2

page 3

page 4


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

In this work, we examine a novel forecasting approach for COVID-19 case ...

Predicting the outcomes of policy diffusion from U.S. states to federal law

In the United States, national policies often begin as state laws, which...

Deep COVID-19 Forecasting for Multiple States with Data Augmentation

In this work, we propose a deep learning approach to forecasting state-l...

United We Stand: Transfer Graph Neural Networks for Pandemic Forecasting

The recent outbreak of COVID-19 has affected millions of individuals aro...

A Deep Learning Approach for COVID-19 Trend Prediction

In this work, we developed a deep learning model-based approach to forec...

Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information

Modeling the spatiotemporal nature of the spread of infectious diseases ...

Multiwave COVID-19 Prediction via Social Awareness-Based Graph Neural Networks using Mobility and Web Search Data

Recurring outbreaks of COVID-19 have posed enduring effects on global so...

Please sign up or login with your details

Forgot password? Click here to reset