Dynamics of Kawasaki Disease Pathogenesis under Stochastic Perturbations and Time-Delay Effects
Ali Raza1,*, Umar Shafique1, Marek Lampart1, Dumitru Baleanu2, Emad Fadhal3, Hadil Alhazmi4
1 IT4Innovations, VSB-Technical University of Ostrava, 17 Listopadu 2172/15, Ostrava, Czech Republic
2 Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
3 Department of Mathematics and Statistics, College of Science, King Faisal University, Al Ahsa, Saudi Arabia
4 Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
* Corresponding Author: Ali Raza. Email: 
Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.084939
Received 01 May 2026; Accepted 11 June 2026; Published online 09 July 2026
Abstract
Kawasaki disease (KD) is an acute, self-limited pediatric vasculitis of unknown etiology and is one of the leading causes of acquired coronary artery complications in children. Endothelial dysfunction, vascular endothelial growth factor (VEGF) activity, adhesion molecule/chemokine activation, and inflammatory cytokine responses play important roles in its pathogenesis. This paper presents a delay differential equation model with stochastic perturbations to study lesion-level inflammatory mechanisms involved in Kawasaki disease pathogenesis. The model describes interactions among healthy endothelial cells, vascular endothelial growth factor (VEGF), adhesion molecules/chemokines, and inflammatory cytokine activity. Mathematically, endothelial-cell injury promotes VEGF production, VEGF contributes to adhesion molecule and chemokine activation, and the combined adhesion molecule/chemokine activity stimulates inflammatory cytokine production after a time delay. The variables are interpreted as aggregated biological activities, not as individual molecular species. The model is not designed to represent the acute, subacute, and convalescent clinical phases of Kawasaki disease separately, and coronary artery inflammation is not included as an independent state variable. Instead, endothelial dysfunction and inflammatory cytokine activity are used as indirect mechanistic indicators of vascular inflammatory progression. The model is shown to preserve positivity and boundedness under suitable dissipativity assumptions. Equilibrium points and an inflammatory feedback threshold quantity are discussed, and local stability is analyzed through the characteristic equations of the delayed system. Reported incidence data from 2020–2025 are used only as qualitative motivation for considering variability and delayed biological responses. A stochastic extension is then formulated to represent random biological and environmental fluctuations, and a stochastic nonstandard finite difference scheme is proposed to preserve positivity and boundedness in numerical simulations. The results provide a mathematical framework for studying delayed stochastic inflammatory interactions in Kawasaki disease, while highlighting that explicit modeling of clinical phases and coronary artery involvement remains an important direction for future work.
Keywords
Kawasaki disease; stochastic delay differential equations; inflammatory feedback threshold; stability analysis; NSFD scheme; real data analysis; graphical analysis
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