{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T01:38:45Z","timestamp":1788745125352,"version":"build-2803163510"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The COVID-19 pandemic and influenza outbreaks have underscored the critical need for predictive models that can effectively integrate spatial and temporal dynamics to enable accurate epidemic forecasting. Traditional time-series analysis approaches have fallen short in capturing the intricate interplay between these factors. Recent advancements have witnessed the incorporation of graph neural networks and machine learning techniques to bridge this gap, enhancing predictive accuracy and providing novel insights into disease spread mechanisms. Notable endeavors include leveraging human mobility data, employing transfer learning, and integrating advanced models such as Transformers and Graph Convolutional Networks (GCNs) to improve forecasting performance across diverse geographies for both influenza and COVID-19. However, these models often face challenges related to data quality, model transferability, and potential overfitting, highlighting the necessity for more adaptable and robust approaches. This paper introduces the Graph Attention-based Spatial Temporal (GAST) model, which employs graph attention networks (GATs) to overcome these limitations by providing a nuanced understanding of epidemic dynamics through a sophisticated spatio-temporal analysis framework. Our contributions include the development and validation of the GAST model, demonstrating its superior forecasting capabilities for influenza and COVID-19 spread, with a particular focus on short-term, daily predictions. The model\u2019s application to both influenza and COVID-19 datasets showcases its versatility and potential to inform public health interventions across a range of infectious diseases.<\/jats:p>","DOI":"10.1371\/journal.pone.0307159","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T17:26:10Z","timestamp":1721064370000},"page":"e0307159","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":33,"title":["Modeling epidemic dynamics using Graph Attention based Spatial Temporal networks"],"prefix":"10.1371","volume":"19","author":[{"given":"Xiaofeng","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoru","family":"Ying","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanning","family":"Chi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4704-7072","authenticated-orcid":true,"given":"Guanqun","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingxia","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,7,15]]},"reference":[{"issue":"1","key":"pone.0307159.ref001","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1186\/1471-2105-15-276","article-title":"Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks","volume":"15","author":"MJ Kane","year":"2014","journal-title":"BMC Bioinformatics"},{"key":"pone.0307159.ref002","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1186\/s12859-019-3131-8","article-title":"Attention-based recurrent neural network for influenza epidemic prediction","volume":"20","author":"X Zhu","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"pone.0307159.ref003","doi-asserted-by":"crossref","first-page":"114077","DOI":"10.1016\/j.eswa.2020.114077","article-title":"A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece","volume":"166","author":"C Katris","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"pone.0307159.ref004","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.patrec.2021.07.027","article-title":"Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics","volume":"151","author":"S Dash","year":"2021","journal-title":"Pattern Recognition Letters"},{"issue":"2","key":"pone.0307159.ref005","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.ijforecast.2020.11.010","article-title":"COVID-19: Forecasting confirmed cases and deaths with a simple time series model","volume":"38","author":"F Petropoulos","year":"2022","journal-title":"International Journal of Forecasting"},{"issue":"2","key":"pone.0307159.ref006","doi-asserted-by":"crossref","first-page":"100064","DOI":"10.1016\/j.stae.2023.100064","article-title":"Sustainable and intelligent time-series models for epidemic disease forecasting and analysis","volume":"3","author":"A Chhabra","year":"2024","journal-title":"Sustainable Technology and Entrepreneurship"},{"issue":"6","key":"pone.0307159.ref007","doi-asserted-by":"crossref","first-page":"4838","DOI":"10.1609\/aaai.v35i6.16616","article-title":"Transfer Graph Neural Networks for Pandemic Forecasting","volume":"35","author":"G Panagopoulos","year":"2021","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"16","key":"pone.0307159.ref008","doi-asserted-by":"crossref","first-page":"10393","DOI":"10.3390\/su141610393","article-title":"Integrating Transformer and GCN for COVID-19 Forecasting","volume":"14","author":"Y Li","year":"2022","journal-title":"Sustainability"},{"key":"pone.0307159.ref009","unstructured":"Nguyen VB, Hy TS, Tran-Thanh L, Nghiem N. 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