Leprosy exhibits substantial clinical variability, ranging from localized immune-controlled forms to progressive nerve-damaging disease. Although Toll-like receptor 2 (TLR2) variability has been associated with susceptibility to Mycobacterium leprae infection, its role in fluctuation-driven immune dynamics remains incompletely understood. In this study, we develop a nonlinear stochastic differential equation (SDE) framework to investigate TLR2-associated immune variability in leprosy progression. The proposed stochastic model incorporates multiplicative Wiener-driven perturbations to capture immune fluctuations during host–pathogen dynamics. Analytical results establish global positivity and ergodic stationary behavior of the system, indicating the possibility of long-term stochastic persistence. Numerical simulations reveal bimodal pathogen-load distributions and variability in cytokine and infected Schwann cell dynamics while recurrent topological immune patterns are identified through topological data analysis. Integrating stochastic analysis with topological, molecular, and neural-network-based approaches, the proposed framework further demonstrates strong performance in classifying clinical subtypes of leprosy. Overall, the study provides a systems-level computational framework for investigating immune variability and stochastic persistence in chronic infectious diseases.
BT - Communications in Nonlinear Science and Numerical Simulation DA - 06/2026 DO - 10.1016/j.cnsns.2026.110434 LA - ENG M3 - Article N2 -Leprosy exhibits substantial clinical variability, ranging from localized immune-controlled forms to progressive nerve-damaging disease. Although Toll-like receptor 2 (TLR2) variability has been associated with susceptibility to Mycobacterium leprae infection, its role in fluctuation-driven immune dynamics remains incompletely understood. In this study, we develop a nonlinear stochastic differential equation (SDE) framework to investigate TLR2-associated immune variability in leprosy progression. The proposed stochastic model incorporates multiplicative Wiener-driven perturbations to capture immune fluctuations during host–pathogen dynamics. Analytical results establish global positivity and ergodic stationary behavior of the system, indicating the possibility of long-term stochastic persistence. Numerical simulations reveal bimodal pathogen-load distributions and variability in cytokine and infected Schwann cell dynamics while recurrent topological immune patterns are identified through topological data analysis. Integrating stochastic analysis with topological, molecular, and neural-network-based approaches, the proposed framework further demonstrates strong performance in classifying clinical subtypes of leprosy. Overall, the study provides a systems-level computational framework for investigating immune variability and stochastic persistence in chronic infectious diseases.
PB - Elsevier BV PY - 2026 T2 - Communications in Nonlinear Science and Numerical Simulation TI - Decoding TLR2-mediated Genetic Variability in Leprosy: A Stochastic Foundation Integrating Molecular, Topological and Deep Learning Insights to Investigate the Inherent Uncertainties SN - 1007-5704 ER -