Localized Climate-Aware Causal AI for Predictive Epidemiology: A Unified Framework of Transferable Graph Neural Networks

Localized Climate-Aware Causal AI for Predictive Epidemiology: A Unified Framework of Transferable Graph Neural Networks | 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 Article Localized Climate-Aware Causal AI for Predictive Epidemiology: A Unified Framework of Transferable Graph Neural Networks Dang Anh Tuan, Pham Vu Nhat Uyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6796201/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change has fundamentally disrupted the stability of infectious disease transmission, especially for vector-borne epidemics such as dengue, malaria, and Zika. Existing AI forecasting models often suffer from poor transferability, limited interpretability, and weak generalization under heterogeneous climatic conditions. We propose a unified Localized Climate-Aware Causal AI (LCCAI) framework that integrates structural causal modeling, hierarchical graph neural networks, and climate-sensitive transfer learning to address these challenges. The framework formalizes a novel Localized Causal Transferability Theorem, enabling robust generalization across distinct geographic regions while preserving mechanistic epidemiological validity. In multi-regional synthetic experiments simulating Vietnam’s diverse climatic zones, LCCAI reduced predictive instability by over 30% compared to non-causal deep learning models and outperformed ARIMA, LSTM, and GNN baselines in both accuracy and cross-regional stability. Beyond theoretical innovation, LCCAI offers immediate translational relevance for climate-adaptive public health policies and precision epidemic forecasting, providing a scalable foundation for real-time global health security in the Anthropocene. Health sciences/Diseases/Infectious diseases/Viral infection Health sciences/Medical research/Translational research Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-6796201","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":465481570,"identity":"7b472918-f8a2-4639-8ba9-d5f3d6322d18","order_by":0,"name":"Dang Anh Tuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYLACHgYbOX72BiDLwIIY1WAizViy5wBIiwTRWg4nbriRAGISocVeIvnZgzc1zIwNN59f3fCjQIKBv707Ab8tEmnmhnOOsTEzzs4pu9kDdJjEmbMbCGhJMJPmYeNhY5bOSbvBA9RiIJFLSEv6N2mefxI8bJJn0m7+IU5Ljpk0b5uBBI8E+7HbxNly5k2Z5Ny+BKCeHLbbMiCthPzC3p6+TeLNt//1+48ff3bzzR9gnLb34teCbKEBmCRWOdjCB6SoHgWjYBSMghEEADYfQbU/srS0AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3320-4072","institution":"Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam","correspondingAuthor":true,"prefix":"","firstName":"Dang","middleName":"Anh","lastName":"Tuan","suffix":""},{"id":465481571,"identity":"40cae4e4-f1c3-4463-a024-16b8674e7437","order_by":1,"name":"Pham Vu Nhat Uyen","email":"","orcid":"","institution":"Industrial University of Ho Chi Minh","correspondingAuthor":false,"prefix":"","firstName":"Pham","middleName":"Vu Nhat","lastName":"Uyen","suffix":""}],"badges":[],"createdAt":"2025-06-01 14:35:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6796201/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6796201/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84575894,"identity":"b22235a7-c316-45fa-b461-a492154d0e9e","added_by":"auto","created_at":"2025-06-13 16:48:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":386071,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6796201/v1_covered_0ee8b6b3-335b-4b6a-ae1d-d6571cc80ff0.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Localized Climate-Aware Causal AI for Predictive Epidemiology: A Unified Framework of Transferable Graph Neural Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6796201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6796201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Climate change has fundamentally disrupted the stability of infectious disease transmission, especially for vector-borne epidemics such as dengue, malaria, and Zika. 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