Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks.

ABSTRACT: The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regarding positivity and hospitalization, across different levels of municipal entities. In this work, we present a method for predicting the number of positive and hospitalized cases via a novel multi-scale graph neural network, integrating information from fine-scale geographical zones of a few thousand inhabitants. By leveraging population mobility data and other features, the model utilizes message passing to model interaction between areas. Our proposed model manages to outperform baselines and deep learning models, presenting low errors in both predictio

SUBMITTER: Skianis K 

PROVIDER: S-EPMC10066232 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature