[In this set of lectures, we will discuss various methods for fitting the parameters of mathematical models to data.  There are at least two issues that need to be understood:

• Choosing an appropriate statistic to assess goodness-of-fit of the model to the data
• Choosing an appropriate method to find the model parameters that optimise the goodness-of-fit statistic

The first point depends only on the data.  The second involves picking an optimisation method appropriate for the type of model being used.  Mathematical models that are often used in population biology, epidemiology, etc, are usually non-linear, and can only be solved numerically.  As we will see, the computational overhead involved in numerically solving a model considerably narrows the range in choices of appropriate optimisation methods.

Due to limited time, we will only discuss methods for finding the best-fit solution, not how to assess uncertainties on the best-fit solution]

• Probability distributions important to modelling in the life and social sciences
• Overview of methods for optimizing model parameters to data (aka inverse problems)
• Basics of the R statistical programming language
• Introduction to numerically solving ODE models in R, with examples based on an SIR model for disease transmission
• Fitting the parameters of an ODE model to data, with examples based on fitting an SIR model to data from an influenza epidemic