TransCode: Uncovering COVID-19 transmission patterns via deep learning.

ABSTRACT:

Background

The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning.

Methods

We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiote

SUBMITTER: Ren J 

PROVIDER: S-EPMC9971690 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature