Traffic-driven spatio-temporal prediction for fine-scale epidemic outbreaks in China.

ABSTRACT:

Background

As the high connectivity creates favorable conditions for the infectious diseases, understanding the dynamics of epidemic spread across regions is significant. However, existing models often focus on single dimensions and specific factors under strict controls, but generally overlook regional differences and interactions.

Methods

In this research, we constructed a spatio-temporal forecasting model based on multiple modeling architectures, specifically designed to predict the post-lockdown epidemic's transmission at a fine scale across prefecture-level cities nationwide. The model integrates comprehensive factors and constructs a structured graph based on transportation patterns and characteristic variables across municipal regions in China. To further enhance its

SUBMITTER: Li H 

PROVIDER: S-EPMC12519872 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature