A novel framework for inferring dynamic infectious disease transmission with graph attention

A Covid-19 case study in Korea

Bibliographic Data

ID15380701
AuthorsMinji Lee (0000-0002-3287-3290, Ulsan National Institute of Science and Technology, corresponding author), Heejin Choi (0000-0001-5093-2859, Ulsan National Institute of Science and Technology), Chang Hyeong Lee (0000-0003-3247-5791)
Year2025
Volume25
Issue1
Pages1884-1884
Publication date2025-05-22
Open AccessYes
TypeARTICLE
VenueBMC Public Health (JOURNAL)
Journal identifiersISSN: 1471-2458 • E-ISSN: 1471-2458
PublisherBioMed Central (PUBLISHER • GB)
DOI10.1186/s12889-025-23059-7
PMID40405112
OpenAlexW4410613151
LanguageEN
References cited52

MPUGAT offers a novel approach for effectively integrating easily accessible, low-dimensional, non-epidemic-related data into epidemic modeling frameworks. Our findings highlight the importance of incorporating dynamic data and utilizing graph attention mechanisms to enhance accuracy of infectious disease modeling and the analysis of policy interventions. This study underscores the potential of leveraging diverse data sources and advanced deep learning techniques to improve epidemic forecasting and inform public health strategies

2019-20 coronavirus outbreak · Biostatistics · Coronavirus disease 2019 (COVID-19 · Disease · Disease transmission · Environmental health · Infectious disease (medical specialty · Outbreak · Pandemic · Pathology · Public health · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 · Transmission (telecommunications · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Medicine · Misinformation and Its Impacts · Epidemiology · Virology

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