Improved Discrimination of Influenza Forecast Accuracy Using Consecutive Predictions

The ability to predict the growth and decline of infectious disease incidence has advanced considerably in recent years. In particular, accurate forecasts of influenza epidemiology have been developed using a number of approaches.

Within our own group we produce weekly operational real-time forecasts of influenza at the municipal and state level in the U.S. These forecasts are generated using ensemble simulations depicting local influenza transmission dynamics, which have been optimized prior to forecast with observations of influenza incidence and data assimilation methods. The expected accuracy of a given forecast can be inferred in real-time through quantification of the agreement (e.g. the variance) among the ensemble of simulations.

Here we show that forecast expected accuracy can be further discriminated with the additional consideration of the streak or persistence of the forecast-the number of consecutive weeks the forecast has converged to the same outcome.

The findings indicate that the use of both the streak and ensemble agreement provides a more detailed and informative assessment of forecast expected accuracy.