Paul Birrell (MRC, Cambridge): Efficient real-time statistical modelling for pandemic influenza

During the 2009 A/H1N1pdm outbreak, much attention was devoted to capturing the dynamics of the epidemic through real-time modelling. The goal was to provide up-to-the-moment assessments of the state of the epidemic at any time, as well as predictions of its future course based upon streams of information updated at regular intervals. In the UK, existing and expanding surveillance consists of a multiplicity of data sources, typically noisy and providing only indirect evidence on the epidemic characteristics of interest. Models capable of reconstructing a pandemic on the basis of this type of information are, therefore, necessarily complex, as they need to link the unobserved transmission process to the intricate mechanisms generating the observed data (e.g. healthcare-seeking behaviour and reporting). As the volume and type of data expand, so does the model complexity and the attendant computational burden, limiting the capacity for real-time inference. This problem is exaggerated when multiple model runs are required to adapt the model to sudden changes in data patterns due, for example, to modifications in population behaviour following an intervention.

Here we extend the modelling of Birrell et al. 2011, focussing on the capability to perform real-time inference. Originally, the model was implemented within the Bayesian statistical framework using Markov Chain-Monte Carlo (MCMC). The real-time utility of MCMC is limited by its requirement to consider all relevant data in their entirety each time the analysis is iterated. We investigate sequential methods for Bayesian analysis that form a hybrid of MCMC and particle filtering that prove capable of drastically reducing the required computation time without losing the intrinsic accuracy of the “gold-standard” MCMC techniques. We illustrate this using both simulated data and data from the 2009 pandemic in England, highlighting how reconstructions and projections of the epidemic curve evolve over the course of the epidemic.


Keywords Epidemic modelling; transmission modelling; MCMC; resample-move; particle learning; real-time modelling