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

Researchers found that a model combining Twitter and historical data outperformed one that only relied on the latter. Using the Twitter model reduced nowcasting error by 29.6%, which dipped to 6.09% when using the CDC’s final estimates. The Twitter model was regularly more accurate than the baseline when forecasting outbreak estimates, with 10-week predictions that had fewer errors than the baseline model of 4 weeks earlier.