Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model

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.

Keywords:

• Flu
• Google correlate
• Google insights
• Google searches
• Google trends
• H1N1
• Infectious Diseases
• Influenza
• IP surveillance
• Nowcasting
• Online surveillance
• Particle filtering.

Acknowledgments

The authors thank Google.org, NSF CNH (GEO-1211668), NSF EID and NIH NIGMS (U01GM087729 and R01GM096655), and NIH NIDA (R12DA027624-01) for partial support, as well as the Editor, Associate Editor, and two anonymous reviewers. Special thanks to Drs. David Bortz, Greg Dwyer, and John Younger for helpful discussions. All code is available from the authors.