  Infectious diseases have severe health and economic consequences for society.
It is important in controlling the spread of an emerging infectious disease to
be able to both estimate the parameters of the underlying model and identify
those individuals most at risk of infection in a timely manner. This requires
having a mechanism to update inference on the model parameters and the
progression of the disease as new data becomes available. However, Markov chain
Monte Carlo (MCMC), the gold standard for statistical inference for infectious
disease models, is not equipped to deal with this important problem. Motivated
by the need to develop effective statistical tools for emerging diseases and
using the 2001 UK Foot-and-Mouth disease outbreak as an exemplar, we introduce
a Sequential Monte Carlo (SMC) algorithm to enable real-time analysis of
epidemic outbreaks. Naive application of SMC methods leads to significant
particle degeneracy which are successfully overcome by particle perturbation
and incorporating MCMC-within-SMC updates.
