Background:

Mathematical modelling can be a helpful tool in planning public health responses against infectious diseases. However, for models to be credible, it is critical that they are shown to capture real-world data. The process of adjusting model parameters to align model behaviour with empirical data is known as ‘calibration’. In this workshop we will cover basic concepts of model calibration, with a practical focus. Beginning with simple approaches such as least-squares calibration, we will eventually cover Bayesian methods for capturing uncertainty. This workshop is the first in a series that will concentrate on the practical application of mathematical modelling, to address questions of public health relevance in India. Future workshops will address modelling interventions and heterogeneity, before beginning independent research projects.

Objectives:

By the end of this workshop, participants will: 

• Understand concepts of model calibration, including likelihood

• Conduct simple model calibrations using least-squares or maximum likelihood approaches

• Be familiar with the role of uncertainty in model calibrations, including Bayesian calibration methods 

Join us on Google classroom for guidance notes and workshop material