Optimizing tuberculosis dynamics through a comparative evaluation of mathematical models

Abstract

Tuberculosis (TB) remains a major public health challenge, requiring precise mathematical modeling to enhance understanding and inform control strategies. Analyzing the dynamics of TB involves studying key model parameters to improve accuracy. This study evaluates six TB models using statistical criteria, including the Sum of Squared Errors (SSE), Akaike Information Criterion (AIC), corrected AIC (AICc), Bayesian Information Criterion (BIC), the difference in AIC (∆AIC), and Akaike weight. The results show that proposed model 2 outperforms the others, achieving the lowest AIC, AICc, and BIC values while having the highest Akaike weight. These findings underscore the importance of selecting an optimal model for TB dynamics to ensure reliable predictions and effective policymaking.