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.