Tracking Flu Epidemics -- Google Flu Trends and Particle Learning Algorithms

In this talk we introduce a state-space tracking algorithm, based on combined particle learning (PL) and sequential Bayesian inference. The proposed algorithm is particularly well-suited to on-line learning and surveillance of infectious diseases -- it is capable of assessing the probability of an pandemic, while simultaneously accounting for uncertainty in disease parameters and producing predictions in real-time. As compared to the now widely used MCMC-based methods, this PL method, which is based on efficient use of an essential state vector, is easier to implement, computationally faster, as well as more readily generalizable to problems with complex non-linear dynamics. We illustrate this algorithm for tracking influenza with Google Flu Trends data, taking a closer look at the spread of flu in the US during 2003-2009, and in New Zealand during 2006-2009.