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The characteristics of influenza seasons varies substantially from year toyear, posing challenges for public health preparation and response. Influenzaforecasting is used to inform seasonal outbreak response, which can in turnpotentially reduce the societal impact of an epidemic. The United StatesCenters for Disease Control and Prevention, in collaboration with externalresearchers, has run an annual prospective influenza forecasting exercise,known as the FluSight challenge. A subset of participating teams has workedtogether to produce a collaborative multi-model ensemble, the FluSight Networkensemble. Uniting theoretical results from the forecasting literature withdomain-specific forecasts from influenza outbreaks, we applied parametricforecast combination methods that simultaneously optimize individual modelweights and calibrate the ensemble via a beta transformation. We used thebeta-transformed linear pool and the finite beta mixture model to produceensemble forecasts retrospectively for the 2016/2017 to 2018/2019 influenzaseasons in the U.S. We compared their performance to methods currently used inthe FluSight challenge, namely the equally weighted linear pool and the linearpool. Ensemble forecasts produced from methods with a beta transformation wereshown to outperform those from the equally weighted linear pool and the linearpool for all week-ahead targets across in the test seasons based on average logscores. We observed improvements in overall accuracy despite thebeta-transformed linear pool or beta mixture methods' modest under-predictionacross all targets and seasons. Combination techniques that explicitly adjustfor known calibration issues in linear pooling should be considered to improveensemble probabilistic scores in outbreak settings.

Comparison of Combination Methods to Create Calibrated Ensemble Forecasts for Seasonal Influenza in the U.S