  This paper is concerned with the application of recent statistical advances
to inference of infectious disease dynamics. We describe the fitting of a class
of epidemic models using Hamiltonian Monte Carlo and Variational Inference as
implemented in the freely available Stan software. We apply the two methods to
real data from outbreaks as well as routinely collected observations. Our
results suggest that both inference methods are computationally feasible in
this context, and show a trade-off between statistical efficiency versus
computational speed. The latter appears particularly relevant for real-time
applications.
