  We propose a framework for Bayesian non-parametric estimation of the rate at
which new infections occur assuming that the epidemic is partially observed.
The developed methodology relies on modelling the rate at which new infections
occur as a function which only depends on time. Two different types of prior
distributions are proposed namely using step-functions and B-splines. The
methodology is illustrated using both simulated and real datasets and we show
that certain aspects of the epidemic such as seasonality and super-spreading
events are picked up without having to explicitly incorporate them into a
parametric model.
