Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume 107, Issue 500, Pages 1410-1426Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/01621459.2012.713876
Keywords
Flu; Google correlate; Google insights; Google searches; Google trends; H1N1; Infectious Diseases; Influenza; IP surveillance; Nowcasting; Online surveillance; Particle filtering
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Funding
- Google.org
- NSF CNH [GEO-1211668]
- NSF EID
- NIH NIGMS [U01GM087729, R01GM096655]
- NIH NIDA [R12DA027624-01]
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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 SEW 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.
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