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Twitter data reduced influenza forecasting error

November 24, 2014

An influenza forecasting model that includes Twitter data could reduce errors in models that rely solely on historical records, according to recent data.

Researchers examined outbreak surveillance data from the CDC’s influenza-like illness (ILINet) system, along with prediction models based on the database. Using an influenza surveillance system based on Twitter data that was developed by others and filtered out media awareness campaigns and other confounders, the researchers created a predictive model combining the two. Prediction data also was collected from Google Flu Trends, a surveillance system based on Google search volume, for additional comparison.

Walter Orenstein Meeting News Coverage

Global polio eradication requires domestic, international support

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Pediatric Perspectives on the Management of Diarrhea in Infants and Children - Monograph

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This activity is supported by an educational grant from Lupin Pharmaceuticals, Inc.

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Review emphasizes careful surveillance in the diagnosis of dermatologic infections

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Advances in Pharmacokinetic Enhancers in HIV Treatment: What You Need to Know to Optimize Outcomes

Advances in Pharmacokinetic Enhancers in HIV Treatment: What You Need to Know to Optimize Outcomes

This activity is supported by an educational grant from Bristol-Myers Squibb.

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