A novel framework for inferring dynamic infectious disease transmission with graph attention: a COVID-19 case study in Korea.
Journal:
BMC public health
Published Date:
May 22, 2025
Abstract
INTRODUCTION: Epidemic modeling is crucial for understanding and predicting infectious disease spread. To capture the complexity of real-world transmission, dynamic interactions between individuals with spatial heterogeneity must be considered. This modeling requires high-dimensional epidemic parameters, which can lead to unidentifiability; therefore, integrating various data types for inference is essential to effectively address these challenges.
