Predicting Seasonal Influenza Hospitalizations Using an Ensemble Super Learner: A Simulation Study.

ABSTRACT: Accurate forecasts can inform response to outbreaks. Most efforts in influenza forecasting have focused on predicting influenza-like activity, with fewer on influenza-related hospitalizations. We conducted a simulation study to evaluate a super learner's predictions of 3 seasonal measures of influenza hospitalizations in the United States: peak hospitalization rate, peak hospitalization week, and cumulative hospitalization rate. We trained an ensemble machine learning algorithm on 15,000 simulated hospitalization curves and generated weekly predictions. We compared the performance of the ensemble (weighted combination of predictions from multiple prediction algorithms), the best-performing individual prediction algorithm, and a naive prediction (median of a simulated outcome distribution).

SUBMITTER: Gantenberg JR 

PROVIDER: S-EPMC10558190 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature