Optimizing Disease Outbreak Forecast Ensembles.

Area of Science:

• Epidemiology
• Public Health
• Computational Biology

Background:

• Accurate infectious disease forecasting is crucial for public health preparedness.
• Ensemble modeling is a common strategy to improve forecast accuracy.

Purpose of the Study:

• To determine the optimal number of models for robust ensemble forecasting of infectious diseases.
• To evaluate the impact of additional models on ensemble accuracy and diminishing returns.

Main Methods:

• Analysis of historical influenza and COVID-19 forecast data.
• Evaluation of ensemble model performance with varying numbers of contributing models.

Main Results:

• Ensemble accuracy significantly improves with more than three forecast models.
• Diminishing returns in accuracy gains are observed as the number of models increases beyond an optimal point.
• Identifying the threshold for robust ensemble performance is key.

Conclusions:

• Future collaborative infectious disease forecasting should incorporate more than three models for enhanced accuracy.
• Resource allocation for developing new models should consider the point of diminishing returns.
• This research provides a framework for designing effective infectious disease forecasting systems.