Online social network flu tracker: A novel sensory approach to predict flu trends.

详细信息   

• 作者：Achrekar ; Harshavardhan.
• 学历：Ph.D.
• 年：2013
• 导师：Liu, Benyuan,eadvisor
• 毕业院校：University of Massachusetts
• ISBN：9781267961822
• CBH：3537104
• Country：USA
• 语种：English
• FileSize：3447183
• Pages：84

文摘

Seasonal influenza epidemics cause several million cases of illnesses cases and about 250,000 to 500,000 deaths worldwide each year. Other pandemics like the 1918 "Spanish Flu" may change into devastating event. Reducing the impact of these threats is of paramount importance for health authorities, and studies have shown that effective interventions can be taken to contain the epidemics, if early detection can be made. In this thesis, we introduce Social Network Enabled Flu Trends SNEFT), a continuous data collection framework which monitors flu related messages on online social networks OSNs) such as Twitter and Facebook and track the emergence and spread of influenza within United States across different regions and among different age groups. We expect these flu related messages posted by OSN users to be highly correlated to the number of Influenza like Illness ILI) cases provided by Centers for Disease Control and Prevention CDC). We attempt to validate our model by measuring how well it fits the CDC ILI rates over a course of two years from 2009 to 2011. We show that text mining significantly enhances the correlation between OSN data and the ILI rates. For accurate prediction, we implemented an auto-regression with exogenous input ARX) model which uses current OSN data and CDC ILI rates from previous weeks to predict current influenza statistics. Our results show that, while previous ILI data from the CDC offer a true but delayed) assessment of a flu epidemic, OSN data provides a real-time assessment of the current epidemic condition and can be used to compensate for the lack of current ILI data. We observe that the OSN data is highly correlated with the ILI rates across different regions within USA and can be used to effectively improve the accuracy of our prediction. Therefore, OSN data can act as supplementary indicator to gauge influenza levels within the population and helps to discover flu trends ahead of CDC. Using our approach to achieve faster, near real time prediction of the emergence and spread of influenza epidemic, through continuous tracking of flu related OSN messages originating within United States, we intend to provide a snapshot of the current epidemic condition and a preview of what to expect, on a daily basis. This would significantly enhances public health awareness and preparedness against the influenza epidemic and other large scale pandemics.