Modeling Transmission Dynamics of Tuberculosis: Parameter Estimation and Sensitivity Analysis Using Real-World Data
Moksina Seyid, Abdu Mohammed Seid, Yassin Tesfaw Abebe

TL;DR
This study models TB transmission in Ethiopia using real data, estimating parameters and analyzing sensitivity to inform effective control strategies to reduce TB prevalence.
Contribution
It introduces a novel SVEITRS compartmental model tailored to Ethiopian data and compares parameter estimation methods for accuracy.
Findings
Maximum likelihood estimation yields more reliable parameters.
Disease-free equilibrium occurs when R0<1, indicating potential for TB elimination.
Sensitivity analysis identifies key factors influencing TB spread.
Abstract
Tuberculosis (TB) continues to pose a major public health challenge, particularly in high-burden regions such as Ethiopia, necessitating a more profound understanding of its transmission dynamics. In this study, we developed an SVEITRS compartmental model to investigate the transmission dynamics of TBs, utilizing real data from Ethiopia from 2011-2021. Model parameters were estimated via two methods: nonlinear least squares and maximum likelihood, with maximum likelihood providing more accurate and reliable results, as confirmed by a test case. The model's stability analysis indicated that there is a disease-free equilibrium in areas where the basic reproduction number () is less than one. The results suggest that optimal conditions could lead to the elimination of TB. On the other hand, there is an endemic equilibrium in areas where is greater than one,…
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