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-te

SUBMITTER: Wang Y 

PROVIDER: S-EPMC8349400 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature