ClimAID: An AI-integrated Global Hybrid Climate-Disease Modelling Framework

ClimAID: An AI-integrated Global Hybrid Climate-Disease Modelling Framework | 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 Method Article ClimAID: An AI-integrated Global Hybrid Climate-Disease Modelling Framework Avik Kumar Sam, Harish C. Phuleria This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9394047/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 This study presents ClimAID, an AI-integrated, calibration-enhanced climate–disease modelling framework designed to produce reproducible, climate-informed infectious disease predictions at fine administrative scales, with a central emphasis on its deterministic reporting module. The framework integrates a terminal-based reproducibility wizard, browser interface, and a deterministic AI-assisted reporter that generates standardized, transparent, and fully reproducible documentation across datasets and geographic contexts. By combining epidemiological data, high-resolution meteorological variables, and CMIP6 climate projections, ClimAID ensures consistency in both analytical outputs and their interpretation. Demonstrated in a dengue-endemic district in India, the workflow includes data preprocessing, integration of lagged predictors, model calibration, and scenario-based projections. The deterministic reporter minimizes subjectivity in scientific reporting, reduces manual effort, and ensures consistency across analyses. Coupled with dual-baseline outbreak risk flagging, ClimAID extends beyond prediction toward reliable early warning. Its scalable design supports global application and future enhancements in climate–health surveillance systems. Infectious Diseases Artificial Intelligence and Machine Learning Climate Analysis and Modeling Epidemiology Climate change infectious disease modeling machine learning climate–health interactions artificial intelligence Full Text Additional Declarations The authors declare no competing interests. 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. 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