AI-Based Prediction of Dengue Incidence using Climatic, Environmental, and Socio-Demographic Factors: An Ensemble Random Forest Approach with Agile System Development

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AI-Based Prediction of Dengue Incidence using Climatic, Environmental, and Socio-Demographic Factors: An Ensemble Random Forest Approach with Agile System Development | 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 AI-Based Prediction of Dengue Incidence using Climatic, Environmental, and Socio-Demographic Factors: An Ensemble Random Forest Approach with Agile System Development Tri Baskoro, Nur Alvira Pascawati, Roger Frutos, Erizal Erizal, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7952186/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Dengue fever remains a major public health concern across tropical regions, particularly in Indonesia, where climatic, environmental, and socio-demographic factors interact in complex and dynamic ways. Conventional forecasting systems often fail to adapt to these changing conditions, underscoring the need for intelligent and flexible predictive frameworks. This study aimed to develop an adaptive AI–Agile framework using an ensemble Random Forest (RF) model to predict monthly dengue incidence by integrating climatic, environmental, and socio-demographic variables in Yogyakarta, Indonesia. Methods An ensemble RF model was constructed within an Agile System Development framework to forecast dengue incidence from 2017 to 2022. Multi-domain datasets encompassing climatic (temperature, rainfall, humidity, wind speed, pressure), environmental (built-up area, vegetation, stagnant water, agricultural land), and socio-demographic (population density) factors were incorporated. A Negative Binomial Generalised Linear Model (GLM) was initially employed to identify significant predictors before applying the non-linear RF model for enhanced accuracy and adaptability.. Results The RF model demonstrated strong predictive performance (R² = 0.86, RMSE = 5.72), outperforming the GLM baseline (R² = 0.64). Rainfall with a one-month lag, relative humidity, and temperature were dominant climatic predictors, while built-up area and population density had substantial effects. The Agile approach enabled iterative refinement and seamless data integration, improving responsiveness to emerging transmission patterns. Conclusion The proposed AI–Agile framework effectively integrates multi-domain predictors, enhancing dengue forecasting accuracy and interpretability. This adaptive system provides a scalable foundation for early warning and data-driven vector control in rapidly changing tropical environments. Dengue Artificial Intelligence Random Forest Agile System Development Climatic and Environmental Predictors Indonesia Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviews received at journal 17 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 30 Oct, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 26 Oct, 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. 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-7952186","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545565690,"identity":"d7fc4f57-a04c-4abd-858e-e7640b477c2f","order_by":0,"name":"Tri Baskoro","email":"","orcid":"","institution":"Universitas Gadjah Mada","correspondingAuthor":false,"prefix":"","firstName":"Tri","middleName":"","lastName":"Baskoro","suffix":""},{"id":545565691,"identity":"107b1866-4f18-48a0-a462-8f7fe6d6c8da","order_by":1,"name":"Nur Alvira 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dengue, Artificial Intelligence, Random Forest, Agile System Development, Climatic and Environmental Predictors, Indonesia","lastPublishedDoi":"10.21203/rs.3.rs-7952186/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7952186/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDengue fever remains a major public health concern across tropical regions, particularly in Indonesia, where climatic, environmental, and socio-demographic factors interact in complex and dynamic ways. Conventional forecasting systems often fail to adapt to these changing conditions, underscoring the need for intelligent and flexible predictive frameworks. This study aimed to develop an adaptive \u003cem\u003eAI\u0026ndash;Agile\u003c/em\u003e framework using an ensemble Random Forest (RF) model to predict monthly dengue incidence by integrating climatic, environmental, and socio-demographic variables in Yogyakarta, Indonesia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAn ensemble RF model was constructed within an Agile System Development framework to forecast dengue incidence from 2017 to 2022. Multi-domain datasets encompassing climatic (temperature, rainfall, humidity, wind speed, pressure), environmental (built-up area, vegetation, stagnant water, agricultural land), and socio-demographic (population density) factors were incorporated. A Negative Binomial Generalised Linear Model (GLM) was initially employed to identify significant predictors before applying the non-linear RF model for enhanced accuracy and adaptability..\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe RF model demonstrated strong predictive performance (R\u0026sup2; = 0.86, RMSE\u0026thinsp;=\u0026thinsp;5.72), outperforming the GLM baseline (R\u0026sup2; = 0.64). Rainfall with a one-month lag, relative humidity, and temperature were dominant climatic predictors, while built-up area and population density had substantial effects. The Agile approach enabled iterative refinement and seamless data integration, improving responsiveness to emerging transmission patterns.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe proposed \u003cem\u003eAI\u0026ndash;Agile\u003c/em\u003e framework effectively integrates multi-domain predictors, enhancing dengue forecasting accuracy and interpretability. 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