  During epidemics, people will often modify their behaviour patterns over time
in response to changes in their perceived risk of spreading or contracting the
disease. This can substantially impact the trajectory of the epidemic. However,
most infectious disease models assume stable population behaviour due to the
challenges of modelling these changes. We present a flexible new class of
models, called behavioural change individual-level models (BC-ILMs), that
incorporate both individual-level covariate information and a data-driven
behavioural change effect. Focusing on spatial BC-ILMs, we consider four
"alarm" functions to model the effect of behavioural change as a function of
infection prevalence over time. We show how these models can be estimated in a
simulation setting. We investigate the impact of misspecifying the alarm
function when fitting a BC-ILM, and find that if behavioural change is present
in a population, using an incorrect alarm function will still result in an
improvement in posterior predictive performance over a model that assumes
stable population behaviour. We also find that using spike and slab priors on
alarm function parameters is a simple and effective method to determine whether
a behavioural change effect is present in a population. Finally, we show
results from fitting spatial BC-ILMs to data from the 2001 U.K. foot and mouth
disease epidemic.
