Summary
- Collaborative forecast efforts for infectious disease outbreaks involve combining individual forecasts into an ensemble prediction that historically outperforms individual models.
- Analyzing data from recent US-based collaborative outbreak forecast hubs revealed that including more models in an ensemble improves forecast performance.
- Testing two approaches for model selection based on past performance found that selecting models based on ensemble performance outperformed randomly assembled ensembles and individual model selection.
- It is recommended for hub organizers to target a minimum of 4 validated forecast models to ensure robust performance compared to baseline models.
- Using past ensemble performance rather than individual performance when selecting models for forecast ensembles is suggested to improve forecast performance.
Are you wondering how experts forecast the spread of diseases like COVID-19? A recent study analyzed data from different collaborative forecast hubs to see how the number and types of models used in these forecasts affect their accuracy. This study found that including more models in the forecasts improved the overall performance.
The researchers compared different ensemble forecasts to see which ones performed better. They found that ensembles composed of more than 3 models outperformed the baseline model. Additionally, increasing the ensemble size helped to improve the average forecast performance and reduce the variability in performance across different ensembles.
To select the best models for the forecasts, the researchers tested two different approaches. They either ranked models based on their individual performance or compared the performance of different ensemble combinations. The study found that choosing models based on ensemble performance outperformed randomly assembled ensembles most of the time.
Overall, the study provides valuable insights for future collaborative forecast efforts. Organizers should aim to include at least 4 validated forecast models in order to ensure robust performance. It’s also important to use past ensemble performance when selecting models for forecasts, as this approach is likely to lead to improved accuracy.
In summary, this research highlights the importance of using multiple models in disease outbreak forecasts and selecting models based on their past ensemble performance. By following these guidelines, forecasters can improve the accuracy of their predictions and better prepare for future outbreaks.
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Infectious Diseases, Public Health & Prevention