A Spatial-temporal Graph based Hybrid Infectious Disease Model with Application to COVID-19

Yunling Zheng, Zhijian Li, Jack Xin, Guofa Zhou

2021

Abstract

As the COVID-19 pandemic evolves, reliable prediction plays an important role in policymaking. The classical infectious disease model SEIR (susceptible-exposed-infectious-recovered) is a compact yet simplistic temporal model. The data-driven machine learning models such as RNN (recurrent neural networks) can suffer in case of limited time series data such as COVID-19. In this paper, we combine SEIR and RNN on a graph structure to develop a hybrid spatio-temporal model to achieve both accuracy and efficiency in training and forecasting. We introduce two features on the graph structure: node feature (local temporal infection trend) and edge feature (geographic neighbor effect). For node feature, we derive a discrete recursion (called I-equation) from SEIR so that gradient descend method applies readily to its optimization. For edge feature, we design an RNN model to capture the neighboring effect and regularize the landscape of loss function so that local minima are effective and robust for prediction. The resulting hybrid model (called IeRNN) improves the prediction accuracy on state-level COVID-19 new case data from the US, out-performing standard temporal models (RNN, SEIR, and ARIMA) in 1-day and 7-day ahead forecasting. Our model accommodates various degrees of reopening and provides potential outcomes for policymakers.

Paper Citation

in Harvard Style

Zheng Y., Li Z., Xin J. and Zhou G. (2021). A Spatial-temporal Graph based Hybrid Infectious Disease Model with Application to COVID-19.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 357-364. DOI: 10.5220/0010349003570364

in Bibtex Style

@conference{icpram21,
author={Yunling Zheng and Zhijian Li and Jack Xin and Guofa Zhou},
title={A Spatial-temporal Graph based Hybrid Infectious Disease Model with Application to COVID-19},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2021},
pages={357-364},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010349003570364},
isbn={978-989-758-486-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Spatial-temporal Graph based Hybrid Infectious Disease Model with Application to COVID-19
SN - 978-989-758-486-2
AU - Zheng Y.
AU - Li Z.
AU - Xin J.
AU - Zhou G.
PY - 2021
SP - 357
EP - 364
DO - 10.5220/0010349003570364