tetano
Editor, Senior Moderator
J Med Syst. 2016 Aug;40(8):189. Epub 2016 Jul 2.
[h=1]Regional Level Influenza Study with Geo-Tagged Twitter Data.[/h] Wang F[SUP]1[/SUP], Wang H[SUP]2[/SUP], Xu K[SUP]2[/SUP], Raymond R[SUP]2[/SUP], Chon J[SUP]2[/SUP], Fuller S[SUP]2[/SUP], Debruyn A[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
[h=4]KEYWORDS:[/h] Geo-tagged twitter stream; Influenza; Partial differential equation modeling; Regional level
PMID: 27372953 DOI: 10.1007/s10916-016-0545-y
[PubMed - as supplied by publisher]
[h=1]Regional Level Influenza Study with Geo-Tagged Twitter Data.[/h] Wang F[SUP]1[/SUP], Wang H[SUP]2[/SUP], Xu K[SUP]2[/SUP], Raymond R[SUP]2[/SUP], Chon J[SUP]2[/SUP], Fuller S[SUP]2[/SUP], Debruyn A[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
[h=4]KEYWORDS:[/h] Geo-tagged twitter stream; Influenza; Partial differential equation modeling; Regional level
PMID: 27372953 DOI: 10.1007/s10916-016-0545-y
[PubMed - as supplied by publisher]