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ARTICLE

Demographic Heterogeneities in a Stochastic Chikungunya Virus Model with Poisson Random Measures and Near-Optimal Control under Markovian Regime Switching

Maysaa Al-Qurashi¹, Ayesha Siddiqa², Shazia Karim³, Yu-Ming Chu^4,5,*, Saima Rashid^2,*

1 Department of Mathematics, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
2 Department of Mathematics, Government College University, Faisalabad, 38000, Pakistan
3 Department of Basic Sciences and Humanities, University of Engineering and Technology Lahore, Faisalabad Campus, Faisalabad, 38000, Pakistan
4 Institute for Advanced Study, Honoring Chen Jian Gong Hangzhou Normal University, Hangzhou, 311121, China
5 Department of Mathematics, Huzhou University, Huzhou, 313000, China

* Corresponding Authors: Yu-Ming Chu. Email: ; Saima Rashid. Email:

(This article belongs to the Special Issue: Recent Developments on Computational Biology-II)

Computer Modeling in Engineering & Sciences 2025, 145(2), 2057-2129. https://doi.org/10.32604/cmes.2025.071629

Received 08 August 2025; Accepted 22 October 2025; Issue published 26 November 2025

Abstract

Chikungunya is a mosquito-borne viral infection caused by the chikungunya virus (CHIKV). It is characterized by acute onset of high fever, severe polyarthralgia, myalgia, headache, and maculopapular rash. The virus is rapidly spreading and may establish in new regions where competent mosquito vectors are present. This research analyzes the regulatory dynamics of a stochastic differential equation (SDE) model describing the transmission of the CHIKV, incorporating seasonal variations, immunization efforts, and environmental fluctuations modeled through Poisson random measure noise under demographic heterogeneity. The model guarantees the existence of a global positive solution and demonstrates periodic dynamics driven by environmental factors. A key contribution of this study is the formulation of a stochastic threshold parameter, , which characterizes the conditions for disease persistence or extinction under random environmental influences. Although our analysis highlights age-specific heterogeneities to illustrate differential transmission risks, the framework is general and can incorporate other vulnerable demographic groups, ensuring broader applicability of the results. Using the Monte Carlo Markov Chain (MCMC) method, we estimate = 1.4978 (95% CI: 1.4968–1.5823) based on CHIKV data from Florida, USA, spanning 2005 to 2017, suggesting that the outbreak remains active and requires targeted control strategies. The effectiveness of immunization, screening, and treatment strategies varies depending on the prioritized demographic groups, due to substantial differences in CHIKV incidence across age categories in the USA. Numerical simulations were conducted using the truncated Euler–Maruyama method to robustly capture the stochastic dynamics of CHIKV transmission with Poisson-driven jumps. Employing an iterative approach and assuming mild convexity conditions, we formulated and solved a parameterized near-optimality problem using the Ekeland variational principle. Our findings indicate that vaccination campaigns are significantly more effective when focused on vulnerable adults over the age of 66, as well as individuals aged 21 to 25. Furthermore, enhancements in vaccine efficacy, diagnostic screening, and treatment protocols all contribute substantially to minimizing infection rates compared to current standard approaches. These insights support the development of targeted, age-specific public health interventions that can significantly improve the management and control of future CHIKV outbreaks.

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