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European COVID-19 Forecast Hub Evaluation Report for Karlen-pypm

Latest forecasts

Forecasts of cases/deaths per week per 100,000. Click the Forecast tab above to view all past forecasts.

Cases

Deaths

Hospitalisations

Predictive performance

Skill is shown as weighted interval score.

Cases

1 week ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

2 weeks ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

3 weeks ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

4 weeks ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

Deaths

1 week ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

2 weeks ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

3 weeks ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

4 weeks ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

Hospitalisations

1 week ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

2 weeks ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

3 weeks ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

4 weeks ahead horizon

Overall predictive performance

The table shows the skill of the Karlen-pypm model relative to the baseline (vs. baseline) and the ensemble model (vs. ensemble) for the past 10 weeks in terms of the Weighted Interval Score (WIS) and Absolute Error (AE). Values less than 1 indicate the model performs better than the model it is being compared with, and values greater than 1 that it performs worse. WIS values are only shown if all 23 quantiles are provided.

Performance over time

The plots below show the data and forecasts with 50% and 90% predictive intervals and, in the panel to the right of the data plots, forecast skill as measured by the Weighted Interval Score. Lower values indicate better predictive performance.

Forecast calibration

The plots below describe the calibration of the model, that is its ability to correctly quantify its uncertainty, across all predicted countries.

Overall coverage

Coverage is the proportion of observations that fall within a given prediction interval. Ideally, a forecast model would achieve 50% coverage of 0.50 (i.e., 50% of observations fall within the 50% prediction interval) and 95% coverage of 0.95 (i.e., 95% of observations fall within the 95% prediction interval), incidcated by the dashed horizontal lines below. Values of coverage greater than these nominal values indicate that the forecasts are underconfident, i.e. prediction intervals tend to be too wide, whereas values of coverage smaller than these nominal values indicate that the ensemble forecasts are overconfident, i.e. prediction intervals tend to be too narrow.

PIT histograms

The figures below are PIT histograms for the all past forecasts. These show the proportion of true values within each predictive quantile (width: 0.1). If the forecasts were perfectly calibrated, observations would fall evenly across these equally-spaced quantiles, i.e. the histograms would be flat.