  Over the last ten years, the US Centers for Disease Control and Prevention
(CDC) has organized an annual influenza forecasting challenge with the
motivation that accurate probabilistic forecasts could improve situational
awareness and yield more effective public health actions. Starting with the
2021/22 influenza season, the forecasting targets for this challenge have been
based on hospital admissions reported in the CDC's National Healthcare Safety
Network (NHSN) surveillance system. Reporting of influenza hospital admissions
through NHSN began within the last few years, and as such only a limited amount
of historical data are available for this signal. To produce forecasts in the
presence of limited data for the target surveillance system, we augmented these
data with two signals that have a longer historical record: 1) ILI+, which
estimates the proportion of outpatient doctor visits where the patient has
influenza; and 2) rates of laboratory-confirmed influenza hospitalizations at a
selected set of healthcare facilities. Our model, Flusion, is an ensemble that
combines gradient boosting quantile regression models with a Bayesian
autoregressive model. The gradient boosting models were trained on all three
data signals, while the autoregressive model was trained on only the target
signal; all models were trained jointly on data for multiple locations. Flusion
was the top-performing model in the CDC's influenza prediction challenge for
the 2023/24 season. In this article we investigate the factors contributing to
Flusion's success, and we find that its strong performance was primarily driven
by the use of a gradient boosting model that was trained jointly on data from
multiple surveillance signals and locations. These results indicate the value
of sharing information across locations and surveillance signals, especially
when doing so adds to the pool of available training data.
