Optimizing the number of models included in outbreak forecasting ensembles
Fox, S. J.; Kim, M.; Meyers, L. A.; Reich, N. G.; Ray, E. L.
Show abstract
Based on historical influenza and COVID-19 forecasts, we quantify the relationship between the number of models in an ensemble and its accuracy and introduce an ensemble approach that can outperform the current standard. Our results can assist collaborative forecasting efforts by identifying target participation rates and improving ensemble forecast performance.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- FedWeight: Mitigating Covariate Shift of Federated Learning on Electronic Health Records Data through Patients Re-weighting 92%
- Clinical Knowledge Extraction via Sparse Embedding Regression (KESER) with Multi-Center Large Scale Electronic Health Record Data 90%
- Zero Shot Health Trajectory Prediction Using Transformer 90%
Similar papers in this journal
Similar papers in this journal
- Comparative evaluation of methodologies for estimating the effectiveness of non-pharmaceutical interventions in the context of COVID-19: a simulation study 91%
- Using LASSO regression to estimate the population-level impact of pneumococcal conjugate vaccines 91%
- Insights into COVID-19 epidemiology and control from temporal changes in serial interval distributions in Hong Kong 90%
Similar papers in this journal
- Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub 95%
- An adaptive weight ensemble approach to forecast influenza activity in the context of irregular seasonality 93%
- Trade-offs between individual and ensemble forecasts of an emerging infectious disease 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.