title: Regional Level Influenza Study with Geo-Tagged Twitter Data.
abstract: The rich data generated and read by millions of users on social media tells what is happening in the real world in a rapid and accurate fashion. In recent years many researchers have explored real-time streaming data from Twitter for a broad range of applications, including predicting stock markets and public health trend. In this paper we design, implement, and evaluate a prototype system to collect and analyze influenza statuses over different geographical locations with real-time tweet streams. We investigate the correlation between the Twitter flu counts and the official statistics from the Center for Disease Control and Prevention (CDC) and discover that real-time tweet streams capture the dynamics of influenza cases at both national and regional level and could potentially serve as an early warning system of influenza epidemics. Furthermore, we propose a dynamic mathematical model which can forecast Twitter flu counts with high accuracy.
authors.affiliation: School of Mathematical and Natural Sciences, New College of Interdisciplinary Arts and Sciences, Arizona State University, Glendale, Arizona, USA. fwang25@asu.edu.
authors.forename: Feng
authors.identifier: http://orcid.org/0000-0003-1427-5964
authors.initials: F
authors.lastname: Wang
authors.affiliation: School of Mathematical and Natural Sciences, New College of Interdisciplinary Arts and Sciences, Arizona State University, Glendale, Arizona, USA.
authors.forename: Haiyan
authors.initials: H
authors.lastname: Wang
authors.affiliation: School of Mathematical and Natural Sciences, New College of Interdisciplinary Arts and Sciences, Arizona State University, Glendale, Arizona, USA.
authors.forename: Kuai
authors.initials: K
authors.lastname: Xu
authors.affiliation: School of Mathematical and Natural Sciences, New College of Interdisciplinary Arts and Sciences, Arizona State University, Glendale, Arizona, USA.
authors.forename: Ross
authors.initials: R
authors.lastname: Raymond
authors.affiliation: School of Mathematical and Natural Sciences, New College of Interdisciplinary Arts and Sciences, Arizona State University, Glendale, Arizona, USA.
authors.forename: Jaime
authors.initials: J
authors.lastname: Chon
authors.affiliation: School of Mathematical and Natural Sciences, New College of Interdisciplinary Arts and Sciences, Arizona State University, Glendale, Arizona, USA.
authors.forename: Shaun
authors.initials: S
authors.lastname: Fuller
authors.affiliation: School of Mathematical and Natural Sciences, New College of Interdisciplinary Arts and Sciences, Arizona State University, Glendale, Arizona, USA.
authors.forename: Anton
authors.initials: A
authors.lastname: Debruyn
mesh_terms: D002487:Centers for Disease Control and Prevention, U.S.; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D062486:Public Health Surveillance / Q000379:methods*; D061108:Social Media; D062206:Spatial Analysis; D014481:United States
keywords: Geo-tagged twitter stream; Influenza; Partial differential equation modeling; Regional level