Improving Forecast Calibration

Background: Predictive models often exhibit undercoverage, meaning the observations fall outside the calculated prediction intervals more often than the nominal coverage level suggests, a common issue in COVID-19 forecasting.

Question / Future Work: The observed undercoverage across all models and horizons suggests that future work should focus on calibration methods or adjusting model uncertainty estimation to ensure empirical coverage aligns better with nominal coverage levels.