Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic.

ABSTRACT: Background: Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate and comprehensive data. Complex epidemiological systems such as COVID-19 are especially challenging to the commonly used mechanistic model when our understanding of this pandemic rapidly refreshes. Objective: We aim to develop a data-driven workflow to extract, process, and develop deep learning (DL) methods to model the COVID-19 epidemic. We provide an alternative modeling approach to complement the current mechanistic modeling paradigm. Method: We extensively searched, extracted, and annotated relevant datasets from over 60 official press releases in Hubei, China, in 2020. Multivariate long short-term memory (

SUBMITTER: Chen S 

PROVIDER: S-EPMC8287417 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature