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A stochastic dengue transmission model: Noise-induced extinction and optimal control
Journal article   Peer reviewed

A stochastic dengue transmission model: Noise-induced extinction and optimal control

Anita Mandal, Moumita Ghosh, Subhash C. Bagui, Pritha Das, Dibakar Ghosh and Matjaž Perc
Applied mathematics and computation, Vol.530, 130156
12/01/2026
DOI: https://doi.org/10.1016/j.amc.2026.130156
Web of Science ID: WOS:001780730600001

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Abstract

Basic reproduction number Model calibration Noise-induced extinction Sensitivity analysis Stochastic dengue model Stochastic optimal control Mathematical Models Public Health
Dengue fever is a mosquito-borne viral disease that continues to pose a substantial public health burden in tropical and subtropical regions. Although most existing studies are based on deterministic formulations, environmental variability and behavioral responses can significantly influence transmission dynamics. In this work, we formulate and analyze a stochastic dengue transmission model that incorporates media-driven awareness, nonlinear treatment responses, and environmental noise. The stochastic system is shown to be well posed by establishing the existence, uniqueness, and positivity of global solutions. The model parameters are estimated by calibrating the deterministic counterpart of the stochastic system against the reported data on dengue incidence, demonstrating good agreement between simulations and observations. The basic reproduction number is derived and sensitivity of the parameters is assessed using partial rank correlation coefficients under uncertainty to identify key drivers of transmission. Analytical conditions for noise-induced disease extinction are obtained, revealing that sufficiently strong environmental fluctuations can suppress endemic persistence. The impact of varying noise intensities on long-term dynamics is further characterized. Finally, we develop a stochastic optimal control framework that integrates awareness-based prevention and treatment interventions, providing a theoretical basis for evaluating control strategies under environmental uncertainty.

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