Efficient real-time monitoring of an emerging influenza epidemic: how feasible?

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 and potentially biased, and originating from multiple sources, seriously questioning 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 algorithm is developed to synthesise multiple imperfect data streams and iterate epidemic inferences amidst rapidly evolving epidemic environments, heuristically minimising computation time to ensure timely delivery of real-time epidemic assessments.