Title |
Regional Level Influenza Study with Geo-Tagged Twitter Data
|
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Published in |
Journal of Medical Systems, July 2016
|
DOI | 10.1007/s10916-016-0545-y |
Pubmed ID | |
Authors |
Feng Wang, Haiyan Wang, Kuai Xu, Ross Raymond, Jaime Chon, Shaun Fuller, Anton Debruyn |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 2 | 29% |
United States | 1 | 14% |
Unknown | 4 | 57% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 71% |
Practitioners (doctors, other healthcare professionals) | 2 | 29% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 1 | 1% |
Portugal | 1 | 1% |
Unknown | 66 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 18 | 26% |
Student > Ph. D. Student | 13 | 19% |
Researcher | 9 | 13% |
Student > Bachelor | 6 | 9% |
Student > Doctoral Student | 3 | 4% |
Other | 5 | 7% |
Unknown | 14 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 20 | 29% |
Medicine and Dentistry | 11 | 16% |
Social Sciences | 5 | 7% |
Engineering | 4 | 6% |
Business, Management and Accounting | 3 | 4% |
Other | 7 | 10% |
Unknown | 18 | 26% |