title: Assessing North American influenza dynamics with a statistical SIRS model.
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.
authors.affiliation: Department of Mathematics and Statistics, Utah State University, Logan, UT 84322, USA. Mevin.Hooten@usu.edu
authors.forename: Mevin B
authors.initials: MB
authors.lastname: Hooten
authors.forename: Jessica
authors.initials: J
authors.lastname: Anderson
authors.forename: Lance A
authors.initials: LA
authors.lastname: Waller
mesh_terms: D001499:Bayes Theorem; D004196:Disease Outbreaks; D005260:Female; D006801:Humans; D015994:Incidence; D007251:Influenza, Human / Q000453:epidemiology* / Q000821:virology; D008297:Male; D015233:Models, Statistical; D009656:North America / Q000453:epidemiology; D062486:Public Health Surveillance; D062211:Spatio-Temporal Analysis