Tracking Flu Epidemics Using Google Flu Trends and Particle Learning

33 Pages Posted: 27 Nov 2009 Last revised: 27 Oct 2012

Vanja M. Dukic

University of Chicago

Hedibert F. Lopes

University of Chicago - Booth School of Business

Nick Polson

University of Chicago - Booth School of Business

Date Written: November 25, 2009

Abstract

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.

Keywords: Epidemics, Particle learning, influenza, Google, IP surveillance, SEIR, H1N1 virus

Suggested Citation

Dukic, Vanja M. and Lopes, Hedibert F. and Polson, Nick, Tracking Flu Epidemics Using Google Flu Trends and Particle Learning (November 25, 2009). Available at SSRN: https://ssrn.com/abstract=1513705 or http://dx.doi.org/10.2139/ssrn.1513705

Vanja M. Dukic (Contact Author)

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

Hedibert F. Lopes

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

Nick Polson

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
773-702-7513 (Phone)
773-702-0458 (Fax)

Paper statistics

Downloads
392
Rank
60,113
Abstract Views
2,002