Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 25, 2009 erstellt
Description:
In the second half of 2009 the world experienced an intense influenza activity. The new 2009 H1N1 virus, formerly known as the swine flu, has in only five months found its way from Mexico to a majority of the countries on the planet. The fears of a large second-wave pandemic and its potential impact on health and economic outcomes have underlined the importance of accurate and fast disease surveillance mechanisms capable of suggesting timely public health interventions. 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 \cite{ONeill/Roberts:1999,Elderdetal:2006,Leman2009} and perfect sampling \cite{Fearnhead:2004} 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 and time-varying 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. We also outline several future directions, including the development of a PL methodology for on-line spatial surveillance as a tool for guiding public health interventions and emergency resource allocation