Seminars: Dept Seminar

Tracking Flu Epidemics Using Google Trends and Particle Learning Algorithms

Abstract:

In this paper we introduce a state-space tracking approach, based on particle learning (PL) for classic compartmental epidemics models (such as, for example, the susceptible-exposed-infected-recovered (SEIR)). The proposed approach is particularly well-suited to on-line learning and surveillance of infectious diseases as it is capable of assessing the odds of an epidemic at each time point, while simultaneously accounting for uncertainty in disease parameters and producing real-time predictive distributions. As compared to the now widely used MCMC-based methods, the PL method, which is based on a clever use of an essential state vector, is easier to implement, computationally faster, as well as more readily generalizable to problems with complex dynamics. In particular, we show how the PL approach, in combination with Bayes Factors, can be used as an on-line diagnostic and surveillance tool for tracking influenza using the Google Flu Trends data. We take a closer look at the spread of flu in the US during 2003-2009, and in New Zealand during 2006-2009, with a special emphasis on the recent epidemic season.