tetano
Editor, Senior Moderator
Am J Epidemiol
. 2023 May 6;kwad113.
doi: 10.1093/aje/kwad113. Online ahead of print. Predicting Seasonal Influenza Hospitalizations Using an Ensemble Super Learner: A Simulation Study
Jason R Gantenberg[SUP] 1 2 [/SUP], Kevin W McConeghy[SUP] 2 3 [/SUP], Chanelle J Howe[SUP] 4 [/SUP], Jon Steingrimsson[SUP] 5 [/SUP], Robertus van Aalst[SUP] 6 7 [/SUP], Ayman Chit[SUP] 6 8 [/SUP], Andrew R Zullo[SUP] 1 2 3 [/SUP]
Affiliations
Accurate forecasts can inform response to outbreaks. Most efforts in influenza forecasting have focused on predicting influenza-like activity, but fewer on influenza-related hospitalizations. We conducted a simulation study to evaluate a super learner's predictions of three 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). Ensemble predictions performed similarly to the naive predictions early in the season but consistently improved as the season progressed for all prediction targets. The best-performing prediction algorithm in each week typically had similar predictive accuracy compared to the ensemble, but the specific prediction algorithm selected varied by week. An ensemble super learner improved predictions of influenza-related hospitalizations, relative to a naive prediction. Future work should examine the super learner's performance using additional empirical data on influenza-related predictors (e.g., influenza-like illness). The algorithm should also be tailored to produce prospective probabilistic forecasts of selected prediction targets.
Keywords: epidemiologic methods; forecasting; hospitalization; influenza; machine learning; public health surveillance.
. 2023 May 6;kwad113.
doi: 10.1093/aje/kwad113. Online ahead of print. Predicting Seasonal Influenza Hospitalizations Using an Ensemble Super Learner: A Simulation Study
Jason R Gantenberg[SUP] 1 2 [/SUP], Kevin W McConeghy[SUP] 2 3 [/SUP], Chanelle J Howe[SUP] 4 [/SUP], Jon Steingrimsson[SUP] 5 [/SUP], Robertus van Aalst[SUP] 6 7 [/SUP], Ayman Chit[SUP] 6 8 [/SUP], Andrew R Zullo[SUP] 1 2 3 [/SUP]
Affiliations
- PMID: 37147861
- DOI: 10.1093/aje/kwad113
Accurate forecasts can inform response to outbreaks. Most efforts in influenza forecasting have focused on predicting influenza-like activity, but fewer on influenza-related hospitalizations. We conducted a simulation study to evaluate a super learner's predictions of three 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). Ensemble predictions performed similarly to the naive predictions early in the season but consistently improved as the season progressed for all prediction targets. The best-performing prediction algorithm in each week typically had similar predictive accuracy compared to the ensemble, but the specific prediction algorithm selected varied by week. An ensemble super learner improved predictions of influenza-related hospitalizations, relative to a naive prediction. Future work should examine the super learner's performance using additional empirical data on influenza-related predictors (e.g., influenza-like illness). The algorithm should also be tailored to produce prospective probabilistic forecasts of selected prediction targets.
Keywords: epidemiologic methods; forecasting; hospitalization; influenza; machine learning; public health surveillance.