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 language | English (US) |
---|---|
Pages (from-to) | 1410-1426 |
Number of pages | 17 |
Journal | Journal of the American Statistical Association |
Volume | 107 |
Issue number | 500 |
DOIs | |
State | Published - 2012 |
Externally published | Yes |
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