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.

翻译：在流行病暴发期间，人们往往会因其感知到疾病传播或感染的风险变化而随时间调整自身行为模式。这会对流行病的演变轨迹产生显著影响。然而，由于模拟这些变化存在挑战，大多数传染病模型假设人群行为保持稳定。我们提出了一个灵活的新型模型类别，称为行为变化个体层面模型（BC-ILMs），该模型同时纳入个体层面协变量信息和数据驱动的行为变化效应。聚焦于空间BC-ILM，我们考虑了四种“警报”函数，用于将行为变化效应建模为感染流行率随时间变化的函数。我们展示了如何在模拟场景中估计这些模型。我们研究了在拟合BC-ILM时错误设定警报函数的影响，并发现在人群中存在行为变化的情况下，即使使用错误的警报函数，其后验预测性能仍优于假设人群行为稳定的模型。我们还发现，在警报函数参数上使用尖峰-平板先验，是判断人群中是否存在行为变化效应的一种简单有效的方法。最后，我们展示了将空间BC-ILM拟合到2001年英国口蹄疫疫情数据的结果。