Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

Abstract

Introduction Multivariate time series prediction of infectious diseases is significant to public health, and the deep learning method has attracted increasing attention in this research field. Material and Methods An adaptively temporal graph convolution (ATGCN) model, which learns the contact patterns of multiple age groups in a graph-based approach, was proposed for COVID- 19 and influenza prediction. We compared ATGCN with autoregressive models, deep sequence learning models, and experience- based ATGCN models in short-term and long-term prediction tasks. Results Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term (12.5% and 10% improvements on RMSE) and long-term (12.4% and 5% improvements on RMSE) prediction tasks. And the RMSE of ATGCN predictions fluctuated least in different age groups of COVID- 19 (0.029 ± 0.003) and influenza (0.059±0.008). Compared with the Ones-ATGCN model or the Pre-ATGCN model, the ATGCN model was more robust in performance, with RMSE of 0.0293 and 0.06 on two datasets when horizon is one. Discussion Our research indicates a broad application prospect of deep learning in the field of infectious disease prediction. Transmission characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature selection. Conclusions The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups, indicating its great potentials for exploring the implicit interactions of multivariate variables.

Forecasting infections with spatio-temporal graph neural networks: a case study of the Dutch SARS-CoV-2 spread

“…Numerous studies on the application of GNNs in the field of epidemiology have been performed. These studies are focused either on forecasting epidemic spreading [3,10,12,16,[22][23][24][25][26], extracting the full state of a spreading epidemic [27], reconstructing their evolution [28,29], generating mobility-control policies [30], or prioritizing vaccine or test receivers [4,31]. Below, the existing methods on location-based spatio-temporal forecasting of the number of infections are discussed.…”