People will often respond to changes in epidemic trajectories by adjusting their behaviours - either by taking additional protective measures when case counts are high, or relaxing those behaviours when case counts are low. Additionally, the degree to which people change their behaviours may depend on the amount of time passed in the epidemic, or on whether the cases are currently increasing or decreasing. We will present a framework for modelling the effect of these dynamic behavioural changes in epidemic models fitted within a Bayesian framework. The application of these dynamic behavioural change models will be illustrated by comparing the behaviour changes across different locations throughout the COVID-19 pandemic.