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

• 成果类型: Article
• 署名作者: Dukic, Vanja; Lopes, Hedibert F.; Polson, Nicholas G.
• 署名单位: University of Colorado System; University of Colorado Boulder; University of Chicago
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2012.713876
• 发表日期: 2012
• 页码: 1410-1426
• 关键词: pandemic influenza; Particle filters; bayesian-inference; monte-carlo; transmissibility; smallpox; simulation; parameter; selection; number

摘要

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.