Journal of Modern Technology and Engineering

The aim of this study is to introduce a wavelet-based numerical approach to explore the dynamics of alcohol consumption among diabetic individuals and quantify the sensitivity of key model parameters. We consider a six-compartment model of alcohol-induced health complications and analyze the impact of the model parameters on the system using the Gegenbauer wavelet method. This technique is highly efficient and results in accurate solutions without the need for linearization. We validate its precision by calculating residual errors (O(10^-13)) and comparing the results with established solvers (RK4 and NDSolve). We observe excellent agreement among these methods. Our main contribution is a quantitative analysis of the model parameters using normalized sensitivity coefficients. We justify the model parameters using recent epidemiological data. We observe that social interaction parameters show moderate to high sensitivity, which confirms their important role in driving the progression to harmful drinking. The treatment enrollment parameters how protective effects. Early intervention in the moderate drinking stage shows benefits across multiple compartments, while treatment access for severe complications shows the highest sensitivity coefficient. These findings show that interventions focusing on reducing social interactions and improving treatment accessibility are effective strategies to mitigate alcohol-related harm in diabetic populations.