v1.0.0

The reproduction number, the mean number of secondary cases infected by each primary case, gives an indication of the effort required to control the disease. Beyond the well-known \emph{basic} reproduction number, there are two natural extensions, namely the \emph{control} and \emph{effective} reproduction numbers. As behaviour, population immunity and viral characteristics can change with time, these reproduction numbers can vary over time.

Real world data can be complex, so in this work we consider a Generalised Additive Model to smooth surveillance data through the explicit incorporation of day-of-the-week effects, to provide a simple measure of the time-varying growth rate associated with the data.

Converting the resulting spline into an estimator for both the control and effective reproduction numbers requires assumptions on a model structure, which we here assume to be a compartmental model. The reproduction numbers calculated are based on both simulated and real world data, and are compared with estimates from an already existing tool.

This method for estimating the time-varying reproduction number is coded here and comparable to other methods (EpiEstim). It provides a useful alternative approach, which can be included as part of a toolbox of models, that is particularly apt at smoothing out day-of-the-week effects in surveillance.