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Updated: Mar 2, 2026

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A climate-informed dengue transmission model with Bayesian decision support.

Priyanka Harjule1, Harshit1, Divyansh Ramola2

  • 1Department of Mathematics, Malaviya National Institute of Technology, Jaipur 302017, India.

Acta Tropica
|February 28, 2026
PubMed
Summary

This study developed a climate-sensitive dengue model to improve disease spread predictions. Findings show interventions must adapt to weather changes for effective dengue control.

Keywords:
Basic reproduction numberBayesian analysisClimate-sensitive dengue modelingControl strategy evaluationEpidemiologyMathematical modelingSEIR-SI model

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Climate Science

Background:

  • Dengue transmission is influenced by weather, necessitating climate-sensitive models for accurate forecasting.
  • Traditional models often fail to capture the dynamic impact of environmental factors, limiting their predictive power and policy relevance.

Purpose of the Study:

  • To develop a non-autonomous, climate-sensitive SEIR-SI dengue model incorporating weather variables.
  • To analyze the impact of temperature, humidity, and precipitation on dengue transmission dynamics.
  • To integrate the model within a Bayesian framework for uncertainty quantification and intervention assessment.

Main Methods:

  • Formulated a climate-sensitive SEIR-SI dengue model where key parameters vary with temperature, humidity, and precipitation.
  • Analytically determined the basic reproduction number (R0) using a next-generation operator to establish disease persistence thresholds.
  • Employed Bayesian inference with affine-invariant MCMC for estimating intervention effectiveness and climate-response coefficients.

Main Results:

  • The climate-informed model demonstrated a 20%-25% improvement in fit over static models, accurately predicting seasonal dengue peaks.
  • Posterior analysis revealed synergistic effects of interventions: larval source management (0.28), nets (0.12), and spraying (0.19).
  • Warmer, humid conditions significantly reduced intervention efficacy, increasing incidence by ~19% with +2°C/ +5% humidity, or ~10% per factor.

Conclusions:

  • A climate-responsive dengue model enhances prediction accuracy and policy utility.
  • Intervention strategies must be adaptive, integrating larval source management, personal protection, and targeted spraying during high-risk climatic periods.
  • Climate change necessitates dynamic, portfolio-based approaches for effective dengue control.