An Intuitionistic Fuzzy Co-Infection Model for Chikungunya and COVID-19 Using Enhanced Adaptive Fuzzy Membership Functions

Chikungunya–COVID-19 co-infection; Mathematical epidemiology; Nonlinear dynamical system; Intuitionistic fuzzy set; Uncertainty modelling; Enhanced Adaptive Fuzzy Membership Function; Stability analysis; Sensitivity analysis; Robustness

Vijayalakshmi G.M
Department of Mathematics, Vel Tech
Rangarajan Dr. Sagunthala R & D Institute of
Science and Technology, Avadi, Tamil Nadu,India - 600062,
ORCID ID: https://orcid.org/0000-0002-7475242X,
Email id- drvijayalakshmi@veltech.edu.in

Vikram R
Department of Mathematics, Vel Tech
Rangarajan Dr. Sagunthala R & D Institute of
Science and Technology, Avadi, Tamil Nadu,
India - 600062,
Department of Mathematics, DRBCCC Hindu
College, Pattabiram,
Tamil Nadu, India - 600072,
ORCID ID: https://orcid.org/0009-0001-27547941,
Email id- rvikrammaths@gmail.com

Received:13-06-2026
Revised:17-07-2026
Accepted:28-07-2026

European Journal of Prosthodontics and Restorative Dentistry (2026) 34 (7s), 554–571

An Intuitionistic Fuzzy Co-Infection Model for Chikungunya and COVID-19 Using Enhanced Adaptive Fuzzy Membership Functions

This paper introduces a novel mathematical framework to study the coinfection dynamics of Chikungunya and COVID-19 under uncertainty. A nonlinear compartmental model is formulated using a system of ordinary differential equations incorporating saturated incidence, vaccination (with waning immunity), and recovery mechanisms. To overcome the limitations of deterministic approaches, an Enhanced Adaptive Fuzzy Membership Function (EAFMF), developed based on intuitionistic fuzzy set theory, is proposed to capture asymmetric uncertainty and hesitation in epidemiological parameters. The analysis includes the characterization of feasible regions, equilibrium points, and local stability through Jacobianbased eigenvalue analysis. Simulation results indicate that the deterministic model exhibits high sensitivity and oscillatory behaviour under increased transmission across multiple scenarios with baseline parameters. In contrast, the EAFMF-based model produces smoother dynamics, reduced infection peaks, and improved convergence stability. Sensitivity and robustness analyses further demonstrate that the proposed framework effectively moderates parameter perturbations. These findings highlight the importance of uncertainty-aware modelling for realistic epidemic prediction, and provide a robust framework for analysing complex co-infection systems.

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