Speaker: Rob Deardon, Professor of Biostatistics, University of Calgary
Abstract: One of the many difficulties in modelling epidemic spread is that caused by behavioural change in the underlying population. Such changes in the underlying population can result in major changes in transmission dynamics of the disease, making the modelling challenging. We propose a model formulation where time-varying transmission is captured by the level of alarm in the population and specified as a function of the past epidemic trajectory. 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. We consider both parametric and nonparametric approaches to modelling the alarm function, and talk about recent developments such as “lockdown fatigue” mechanisms and multivariable alarm functions.
This will be a free hybrid seminar. To register to attend virtually, please click here: https://cam-ac-uk.zoom.us/meeting/register/tZ0vceqsqDwtHNLalYuBk7GmojclSXN5wIcE