Causality-Aware Deep Learning for Climate-Sensitive Infectious Disease Forecasting via Causal Graphs and Counterfactual Simulation

Causality-Aware Deep Learning for Climate-Sensitive Infectious Disease Forecasting via Causal Graphs and Counterfactual Simulation | 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 Causality-Aware Deep Learning for Climate-Sensitive Infectious Disease Forecasting via Causal Graphs and Counterfactual Simulation Dang Tuan, Pham Vu Nhat Uyen This is a preprint; it has not been peer reviewed by a journal.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-6790525/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 Accurately forecasting infectious disease outbreaks under accelerating climate change presents a profound challenge that demands both predictive accuracy and mechanistic interpretability. This study introduces a novel Causality-Aware Deep Learning (CADL) framework that systematically integrates causal structure learning, deep temporal forecasting, counterfactual simulation, and explainability into a unified architecture for forecasting climate-sensitive infectious diseases such as dengue fever. By combining directed acyclic graph discovery, hybrid graph neural networks and transformers, and structural causal models, the proposed framework simultaneously delivers high forecasting accuracy and actionable causal insights. Theoretical analysis establishes stability and convergence guarantees, while synthetic experiments demonstrate superior forecasting performance — achieving a 26.8% reduction in mean absolute error compared to standard deep learning baselines including LSTM, Transformer, and GNN models. The framework further enables counterfactual forecasting under alternative climate change scenarios (e.g., RCP 4.5 and RCP 8.5), providing policymakers with scenario-based risk quantification to support adaptive public health strategies. CADL represents a significant advance in explainable AI for epidemic forecasting, with broad implications for climate-resilient global health preparedness. Health sciences/Diseases/Infectious diseases/Viral infection Health sciences/Medical research/Experimental models of disease Causal Inference Climate Change Climate-Sensitive Diseases Counterfactual Forecasting Deep Learning Dengue Fever Epidemiological Modeling Explainable AI Graph Neural Networks Infectious Disease Forecasting Public Health Policy 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6790525","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":465151943,"identity":"2e82a875-43c5-4cc7-b828-0835fb46d537","order_by":0,"name":"Dang 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","correspondingAuthor":true,"prefix":"","firstName":"Dang","middleName":"","lastName":"Tuan","suffix":""},{"id":465151944,"identity":"6e44faf7-e28c-4ce9-81f3-942aa2f9578d","order_by":1,"name":"Pham Vu Nhat Uyen","email":"","orcid":"","institution":"Industrial University of Ho Chi Minh City","correspondingAuthor":false,"prefix":"","firstName":"Pham","middleName":"Vu Nhat","lastName":"Uyen","suffix":""}],"badges":[],"createdAt":"2025-05-31 10:55:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6790525/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6790525/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84946090,"identity":"fdebdea4-2226-4cc4-a2d0-0b34f271787c","added_by":"auto","created_at":"2025-06-19 06:12:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":443491,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6790525/v1_covered_0c87e6fc-98f8-4d7e-ae90-acb1e65935cf.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Causality-Aware Deep Learning for Climate-Sensitive Infectious Disease Forecasting via Causal Graphs and Counterfactual Simulation","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":"Causal Inference, Climate Change, Climate-Sensitive Diseases, Counterfactual Forecasting, Deep Learning, Dengue Fever, Epidemiological Modeling, Explainable AI, Graph Neural Networks, Infectious Disease Forecasting, Public Health Policy","lastPublishedDoi":"10.21203/rs.3.rs-6790525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6790525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurately forecasting infectious disease outbreaks under accelerating climate change presents a profound challenge that demands both predictive accuracy and mechanistic interpretability. 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