Estimating the Parameters of a Disease Model from Clinical Data
Journal Title: Journal of Advances in Mathematics and Computer Science - Year 2017, Vol 24, Issue 3
Abstract
Estimation of parameters (rate constants) in infectious disease models can be done either through literature or from clinical data. This article presents parameter estimation of a disease model from clinical data using the numerical integration followed by minimization of the error function. The error function is the overall sum of squared distances between the model-fitted points and the corresponding clinical data points at certain time points. Numerical integration was done using written Mat lab code using ode15s solver because of stiff nature of the disease models. Minimization of the error function was also done through a written Mat lab code using Mat lab routine “fmincon”.
Authors and Affiliations
George Theodore Azu-Tungmah, Francis T. Oduro, Gabriel A. Okyere
Keywords
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- EP ID EP322126
- DOI 10.9734/JAMCS/2017/34641
- Views 117
- Downloads 0
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