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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Achrekar, H. Gandhe, A. Lazarus, R. Ssu-Hsin Yu Benyuan Liu |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Computer Science, University of Massachusetts Lowell, Lowell, 01854, USA (Achrekar, H.; Benyuan Liu) || Scientific Systems Company Inc, 500 West Cummings Park, Woburn, MA 01801, USA (Gandhe, A.; Ssu-Hsin Yu) || Department of Population Medicine, Harvard Medical School, Boston, MA 02101, USA (Lazarus, R.) |
| Abstract | Reducing the impact of seasonal influenza epidemics and other pandemics such as the H1N1 is of paramount importance for public health authorities. Studies have shown that effective interventions can be taken to contain the epidemics if early detection can be made. Traditional approach employed by the Centers for Disease Control and Prevention (CDC) includes collecting influenza-like illness (ILI) activity data from “sentinel” medical practices. Typically there is a 1–2 week delay between the time a patient is diagnosed and the moment that data point becomes available in aggregate ILI reports. In this paper we present the Social Network Enabled Flu Trends (SNEFT) framework, which monitors messages posted on Twitter with a mention of flu indicators to track and predict the emergence and spread of an influenza epidemic in a population. Based on the data collected during 2009 and 2010, we find that the volume of flu related tweets is highly correlated with the number of ILI cases reported by CDC. We further devise auto-regression models to predict the ILI activity level in a population. The models predict data collected and published by CDC, as the percentage of visits to “sentinel” physicians attributable to ILI in successively weeks. We test models with previous CDC data, with and without measures of Twitter data, showing that Twitter data can substantially improve the models prediction accuracy. Therefore, Twitter data provides real-time assessment of ILI activity. |
| Starting Page | 702 |
| Ending Page | 707 |
| File Size | 529314 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457702495 |
| e-ISBN | 9781457702488 |
| DOI | 10.1109/INFCOMW.2011.5928903 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-04-10 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Real time systems Correlation Medical services Predictive models Twitter Data models Delay |
| Content Type | Text |
| Resource Type | Article |
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