Google Flu Trends Still Appears Sick: An Evaluation of the 2013-2014 Flu Season
نویسندگان
چکیده
منابع مشابه
Influenza Forecasting with Google Flu Trends
BACKGROUND We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system...
متن کامل1124Clinical characteristics of adult patients with influenza B and A infections during 2013/2014 flu season
Background. Human infection caused by influenza B virus (IFV-B) has been known to be less severe than that caused by influenza A virus (IFV-A). Although more recent studies did not find any significant differences between the clinical features of patients with IFV-B and those with IFV-A, clinical data are still lacking on this subject, especially in adult patients. Methods. This study was perfo...
متن کاملFluBreaks: Early Epidemic Detection from Google Flu Trends
BACKGROUND The Google Flu Trends service was launched in 2008 to track changes in the volume of online search queries related to flu-like symptoms. Over the last few years, the trend data produced by this service has shown a consistent relationship with the actual number of flu reports collected by the US Centers for Disease Control and Prevention (CDC), often identifying increases in flu cases...
متن کاملGoogle Flu Trends: Spatial Correlation with Influenza Emergency Department Visits
Introduction GFT is a surveillance tool that gathers data on local internet searches to estimate the emergence of influenza-like illness in a given geographic location in real time.3 Previously, GFT has been proven to strongly correlate with influenza incidence at the national and regional level.2,3 GFT has shown promise as an easily accessed tool to enhance influenza surveillance and forecasti...
متن کاملTracking Epidemics with State-space SEIR and Google Flu Trends
In this paper we use Google Flu Trends data together with a sequential surveillance model based on the state-space methodology, to track the evolution of an epidemic process over time. We embed a classical mathematical epidemiology model (a susceptible-exposed-infectedrecovered (SEIR) model) within the state-space framework, thereby allowing the SEIR dynamics to change through time. The impleme...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2014
ISSN: 1556-5068
DOI: 10.2139/ssrn.2408560