Estimating the Parameters of a Disease Model from Clinical Data

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”.