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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 <t>ode45</t> solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.
Ode45 Solver, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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ode45  (MathWorks Inc)
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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 <t>ode45</t> solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.
Ode45, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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ode solver ode45  (MathWorks Inc)
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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 <t>ode45</t> solver, and parameter optimization was carried out using MATLAB's fminsearch 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 <t>ode45</t> solver, and parameter optimization was carried out using MATLAB's fminsearch 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 <t>ode45</t> solver, and parameter optimization was carried out using MATLAB's fminsearch 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: To solve the underlying system of ordinary differential equations, we used MATLAB's ode45 solver, which is suitable for non-stiff systems.

Techniques: Comparison