STAN: spatio-temporal attention network for pandemic prediction using real-world evidence.

ABSTRACT:

Objective

We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients' claims data from different counties and states that capture local disease status and medical resource utilization; (2) utilizing demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into a deep learning model.

Materials and methods

We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses a graph attention network to capture spatio-temporal trends of disease dynamics and to predict the number of cases for a fixed number of days into the future. We also designed a dynamics-based loss term for enhancing long-term predictions. STAN

SUBMITTER: Gao J 

PROVIDER: S-EPMC7928935 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature