Speaker
Dr
Matthew Betti
(York University)
Description
The basic reproduction number, $R_0$, derived from ordinary differential equation models is a powerful predictor of the severity of an infection and can help inform prevention and mitigation strategies. Many of the parameters used in ODE models are mean values of time-dependent distributions. Here, we show how we can incorporate properties of these distributions to refine estimates of $R_0$ for a series of ubiquitous models used in epidemiology. These corrections are applied to the $R_0$ estimate as opposed to the model itself, allowing simple models to be used, and better predictions to be made post-hoc as more data becomes available. Moreover, we address some difficulties in trying to extend these corrections to more complex models.
Primary authors
Dr
Matthew Betti
(York University)
Prof.
Jane Heffernan
(York University)
