Assessing North American Influenza Dynamics with a Statistical SIRS Model

• Type of Material: Article
• Authors: Mevin B. Hooten, Utah State University; Jessica Anderson, Utah State University; Lance Waller, Emory University
• Language: English
• Date: 2010-07-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2010 Elsevier Inc. All rights reserved.
• License: Creative Commons Attribution-NonCommerical-NoDerivs 3.0 Unported
• Final Published Version (URL): http://dx.doi.org/10.1016/j.sste.2010.03.003
• Title of Journal or Parent Work: Spatial and Spatio-temporal Epidemiology
• ISSN: 1877-5845
• Volume: 1
• Issue: 2-3
• Start Page: 177
• End Page: 185
• Keywords: Agent-Based Model; Hierarchical Model; Dynamical Model; Flu; Epidemic; Spatio-Temporal Statistics
• Research Categories: Health Sciences, Epidemiology; Biology, Biostatistics

Abstract

We present a general statistical modeling framework to characterize continental-level influenza dynamics in the United States for the purposes of examining state-level epidemiological sources and sinks. The methods we describe depend directly on state-level influenza data that are prepared on a weekly basis by Google Flu Trends. The Google Flu Trends team has provided a powerful new approach to collecting and reporting epidemiological data and, when used in conjunction with sophisticated statistical models, can allow for the identification and quantification of the flow of influenza across the continental United States. Our proposed methods, when conditioned on such a comprehensive search query product, can provide unprecedented scientific learning about large-scale pathways and barriers to disease transmission which can ultimately be helpful for policy, remediation, and response efforts.