Tracking the Rhythm of Heat: Seasonal SEIR Modelling and Machine Learning for Heat Wave Forecasting.

Tracking the Rhythm of Heat: Seasonal SEIR Modelling and Machine Learning for Heat Wave Forecasting. | 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 Tracking the Rhythm of Heat: Seasonal SEIR Modelling and Machine Learning for Heat Wave Forecasting. MD SHAHIDUL ISLAM, Pabel shahrear, Ummey Habiba, Farzana Hussain, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7735328/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 Heatwaves pose serious risks to human health, infrastructure, and the environment, with rising frequency, intensity, and duration linked to increased morbidity and mortality – particularly among vulnerable populations. While numerous studies have analyzed heatwaves statistically, few have applied mechanistic modeling. In this study, we extend the classical SEIR framework by introducing a temperature-dependent transmission function ϕ(T) to model heatwave impacts on disease dynamics in Dhaka, Bangladesh. Seasonal forcing was incorporated via sinusoidal modulation of the reproduction number R_0, capturing oscillatory behavior driven by annual temperature cycles. Analytical derivations identified two equilibria – Heat Impacted Free Equilibrium (HIFE) and Heat-Impacted Equilibrium (HIE) – representing heatwave-free and heatwave-endemic states. To evaluate model robustness, we performed local sensitivity and Partial Rank Correlation Coefficient (PRCC) analyses, identifying transmission rate (β) and progression rate (γ) as the most influential parameters. Simulations revealed that higher β and faster progression (γ) amplified outbreak size, whereas increased recovery (α) and reduced immunity loss (σ) substantially mitigated transmission and delayed epidemic peaks. These findings underscore that if β>α, outbreaks expand rapidly, but when α≥β, the system stabilizes near equilibrium. Complementing the mechanistic approach, meteorological data (January 2014 – February 2024) were analyzed using statistical tools and machine learning models, including Logistic Regression, SVM, and ensemble regressors. Logistic Regression achieved 95.8% accuracy, while SVM yielded the highest cross-validation score (0.9311) and AUC (0.9972). XGBRegressor provided robust short-term temperature forecasts, whereas Decision Trees offered smoother long-term projections. Taken together, these results demonstrate that integrating temperature-modulated SEIR modeling with machine learning provides a comprehensive framework for predicting heat-related morbidity, identifying early-warning thresholds, and informing public health preparedness under climate variability. Health sciences/Diseases/Infectious diseases Physical sciences/Mathematics and computing/Applied mathematics Heat Compartmental system Environmental Impact Machine Learning Forecasting Basic Reproduction Number 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-7735328","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":522464830,"identity":"6c0a55a7-b9d5-400b-9b90-d59c3ff4c8f5","order_by":0,"name":"MD SHAHIDUL 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While numerous studies have analyzed heatwaves statistically, few have applied mechanistic modeling. In this study, we extend the classical SEIR framework by introducing a temperature-dependent transmission function ϕ(T) to model heatwave impacts on disease dynamics in Dhaka, Bangladesh. Seasonal forcing was incorporated via sinusoidal modulation of the reproduction number R_0, capturing oscillatory behavior driven by annual temperature cycles. Analytical derivations identified two equilibria – Heat Impacted Free Equilibrium (HIFE) and Heat-Impacted Equilibrium (HIE) – representing heatwave-free and heatwave-endemic states. To evaluate model robustness, we performed local sensitivity and Partial Rank Correlation Coefficient (PRCC) analyses, identifying transmission rate (β) and progression rate (γ) as the most influential parameters. Simulations revealed that higher β and faster progression (γ) amplified outbreak size, whereas increased recovery (α) and reduced immunity loss (σ) substantially mitigated transmission and delayed epidemic peaks. These findings underscore that if β\u003eα, outbreaks expand rapidly, but when α≥β, the system stabilizes near equilibrium. Complementing the mechanistic approach, meteorological data (January 2014 – February 2024) were analyzed using statistical tools and machine learning models, including Logistic Regression, SVM, and ensemble regressors. Logistic Regression achieved 95.8% accuracy, while SVM yielded the highest cross-validation score (0.9311) and AUC (0.9972). XGBRegressor provided robust short-term temperature forecasts, whereas Decision Trees offered smoother long-term projections. 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