Tracking epidemics with Google Flu trends data and a state-space SEIR model

Vanja Dukic, Hedibert F. Lopes, Nicholas G. Polson

Research output: Contribution to journalArticlepeer-review

84 Scopus citations

Abstract

In this article, we use Google Flu Trends data together with a sequential surveillance model based on state-space methodology to track the evolution of an epidemic process over time. We embed a classical mathematical epidemiology model [a susceptible-exposed-infected-recovered (SEIR) model] within the state-space framework, thereby extending the SEIR dynamics to allow changes through time. The implementation of this model is based on a particle filtering algorithm, which learns about the epidemic process sequentially through time and provides updated estimated odds of a pandemic with each new surveillance data point. We show how our approach, in combination with sequential Bayes factors, can serve as an online diagnostic tool for influenza pandemic. We take a close look at the Google Flu Trends data describing the spread of flu in the United States during 2003-2009 and in nine separate U. S. states chosen to represent a wide range of health care and emergency system strengths and weaknesses. This article has online supplementary materials.

Original languageEnglish (US)
Pages (from-to)1410-1426
Number of pages17
JournalJournal of the American Statistical Association
Volume107
Issue number500
DOIs
StatePublished - 2012
Externally publishedYes

Keywords

  • Flu
  • Google correlate
  • Google insights
  • Google searches
  • Google trends
  • H1N1
  • Infectious diseases
  • Influenza
  • Ip surveillance
  • Nowcasting
  • Online surveillance
  • Particle filtering

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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