A0235
Title: A Bayesian approach to jointly modelling epidemic and behavioral dynamics
Authors: Rob Deardon - University of Calgary (Canada) [presenting]
Abstract: One of the many difficulties in modelling epidemic spread is caused by behavioral change in the underlying population. This can be a major public health issue since, as seen during the COVID-19 pandemic, behavior in the population can change drastically as infection levels vary, both due to government mandates and personal decisions. Such changes in the underlying population result in major changes in the transmission dynamics of the disease, making the modelling challenging. However, these issues arise in agriculture and public health, as changes in farming practices are often observed as disease prevalence changes. A model formulation is proposed wherein time-varying transmission is captured by the level of alarm in the population and specified as a function of recent epidemic history. The alarm function itself can also vary dynamically, allowing for phenomena such as lockdown fatigue. The model is set in a data-augmented Bayesian framework as epidemic data are often only partially observed and can be utilized prior information to help with parameter identifiability. The identifiability of the population alarm is investigated across a wide range of scenarios, using both parametric functions and non-parametric Gaussian processes and splines. The benefits and utility of the proposed approach are illustrated through applications of COVID-19 and Ebola disease.
