05: Joint Modelling of Epidemic and Population Behavioural Dynamics.

Music City Center 

One of the many difficulties in modelling epidemic spread is that caused by behavioural change in the underlying population. This can be a major issue in public health since behaviour can change drastically as infection levels vary, both due to government mandates and personal decisions. Such changes behaviour may subsequently produce major changes in disease transmission dynamics. However, these issues arise in agriculture and public health, as changes in farming practice are also often observed as disease prevalence changes. We propose a model formulation wherein time-varying transmission is captured by the level of alarm in the population and specified as a function of recent epidemic history. This alarm function may be parametric or a non-parametric function such as a Gaussian process. The alarm function itself can also vary dynamically, allowing for phenomena such as "lockdown fatigue", or depend upon multiple epidemic metrics (e.g. case counts, hospitalisations and/or death counts). The models are set in a data-augmented Bayesian framework as epidemic data are often only partially observed, and we can utilize prior information to help with parameter identifiability. The benefit and utility of the proposed approach is illustrated via COVID-19 data.