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Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's <t>fminsearch</t> function based on the Nelder–Mead simplex method.
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Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's <t>fminsearch</t> function based on the Nelder–Mead simplex method.
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Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's <t>fminsearch</t> function based on the Nelder–Mead simplex method.
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Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's <t>fminsearch</t> function based on the Nelder–Mead simplex method.
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Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's <t>fminsearch</t> function based on the Nelder–Mead simplex method.
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Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's <t>fminsearch</t> function based on the Nelder–Mead simplex method.
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Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.

Journal: Infectious Disease Modelling

Article Title: Modelling the potential impact of TB-funded prevention programs on the transmission dynamics of TB

doi: 10.1016/j.idm.2025.05.010

Figure Lengend Snippet: Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.

Article Snippet: The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.

Techniques: Comparison