  A prompt public health response to a new epidemic relies on the ability to
monitor and predict its evolution in real time as data accumulate. The 2009
A/H1N1 outbreak in the UK revealed pandemic data as noisy, contaminated,
potentially biased, and originating from multiple sources. This seriously
challenges the capacity for real-time monitoring. Here we assess the
feasibility of real-time inference based on such data by constructing an
analytic tool combining an age-stratified SEIR transmission model with various
observation models describing the data generation mechanisms. As batches of
data become available, a sequential Monte Carlo (SMC) algorithm is developed to
synthesise multiple imperfect data streams, iterate epidemic inferences and
assess model adequacy amidst a rapidly evolving epidemic environment,
substantially reducing computation time in comparison to standard MCMC, to
ensure timely delivery of real-time epidemic assessments. In application to
simulated data designed to mimic the 2009 A/H1N1 epidemic, SMC is shown to have
additional benefits in terms of assessing predictive performance and coping
with parameter non-identifiability.
