During an epidemic, people will often modify their behaviour over time as their perceived risk of contracting or spreading the disease changes. This can have a substantial impact on the trajectory of the epidemic, however most infectious disease models assume stable population behaviour due to the challenges of modelling these changes. We will present a class of infectious disease transmission models where behavioural change is incorporated by modelling it as a function of a direct epidemic metric, such as prevalence. We consider several different mechanisms by which the effect of behaviour changes may vary according to these metrics, and results from modelling on both the population-averaged and individual levels will be shown. Model fitting in a Bayesian framework will be illustrated through simulation studies and applications to data from foot and mouth disease and COVID-19.