A Novel Framework for Inferring Dynamic Infectious Disease Transmission with Graph Attention: A COVID-19 Case Study in Korea

A Novel Framework for Inferring Dynamic Infectious Disease Transmission with Graph Attention: A COVID-19 Case Study in Korea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Novel Framework for Inferring Dynamic Infectious Disease Transmission with Graph Attention: A COVID-19 Case Study in Korea Minji Lee, Heejin Choi, Chang Hyeong Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5577184/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 May, 2025 Read the published version in BMC Public Health → Version 1 posted 12 You are reading this latest preprint version 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. Methods: We introduce a novel hybrid framework, Multi-Patch Model Update with Graph Attention Network (MPUGAT), that combines a multi-patch compartmental model with a spatio-temporal deep learning model. MPUGAT employs a GAT (Graph Attention Mechanism) to transform static traffic matrices into dynamic transmission matrices by analyzing patterns in diverse time series data from each city. Results: We demonstrate the effectiveness of MPUGAT through its application to COVID-19 data from South Korea. By accurately estimating time-varying transmission rates, MPUGAT outperforms traditional models and aligns with actual policies such as social distancing. Conclusion: 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. Deep learning Multi-patch model Epidemic modeling Compartment model Transmission matrix Contact pattern Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 May, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Accepted 06 May, 2025 Reviews received at journal 26 Apr, 2025 Reviews received at journal 25 Apr, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers invited by journal 22 Apr, 2025 Submission checks completed at journal 08 Apr, 2025 First submitted to journal 03 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5577184","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446351595,"identity":"401e5fd4-6ce5-40b9-a429-38528183eebd","order_by":0,"name":"Minji Lee","email":"","orcid":"","institution":"Ulsan National Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Minji","middleName":"","lastName":"Lee","suffix":""},{"id":446351596,"identity":"64d1c964-93e2-472c-a1de-63f46566c876","order_by":1,"name":"Heejin Choi","email":"","orcid":"","institution":"Ulsan National Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Heejin","middleName":"","lastName":"Choi","suffix":""},{"id":446351597,"identity":"5c764284-558d-4800-81ae-6c81084d70ee","order_by":2,"name":"Chang Hyeong Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDACCeYGBoYKBgY2KN+ACC2MQC1nQFqYSdHC2AZiEauFf3Zj44OP8w7n8fGfP8Dwo4bB2LyBkCV3DjYbztx2uJhNIpmBsecYg5nMAQJaDCQS26R5tx1ObJMAOoy3gcFGgpDDgFraf/+dA9TCf5iB8S+RWtqYGRuAWhiSGZiBtpgR1CJxI7FZsudYOtBhyQaHZY5JGBPUwj8j+eCHHzXWifP7Dz58+KbGxnAGIS0o4ADQVpI0jIJRMApGwSjAAQB9KDnJEwEZcQAAAABJRU5ErkJggg==","orcid":"","institution":"Ulsan National Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Chang","middleName":"Hyeong","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2024-12-04 06:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5577184/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5577184/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-23059-7","type":"published","date":"2025-05-22T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83460084,"identity":"c1f81e9a-cb77-4282-83bd-ceedde78e240","added_by":"auto","created_at":"2025-05-26 16:10:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2359934,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptBMCRevision.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5577184/v1_covered_fb9113f2-01d5-4bf3-b4aa-8ed11bf12517.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Framework for Inferring Dynamic Infectious Disease Transmission with Graph Attention: A COVID-19 Case Study in Korea","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep learning, Multi-patch model, Epidemic modeling, Compartment model, Transmission matrix, Contact pattern","lastPublishedDoi":"10.21203/rs.3.rs-5577184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5577184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Epidemic modeling is crucial for understanding and predicting infectious disease spread. 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