For practical reasons, many forecasts of case, hospitalization and death counts in the context of the current COVID-19 pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Forecast evaluation metrics like the logarithmic score, which has been applied in several infectious disease forecasting challenges, are then not available as they require full predictive distributions. This article provides an overview of how established methods for the evaluation of quantile and interval forecasts can be applied to epidemic forecasts in this format. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. It can be interpreted as a generalization of the absolute error to probabilistic forecasts and allows for a decomposition into a measure of sharpness and penalties for over- and underprediction.

翻译：出于实际原因,对当前COVID-19大流行情况下的病例、住院和死亡数的许多预测是以各级中央预测间隔的形式发布的,在COVID-19预报枢纽(https://covid19forescasthub.org/)中收集的预测也是如此。预测对数等评价指标,在几项传染病预测挑战中应用过,然后无法提供,因为它们需要全面的预测分布。本篇文章概述了如何将评估定量和间隔预测的既定方法应用于这一格式的流行病预测。具体地说,我们讨论加权间隔分的计算和解释,这是与连续的概率分数相近的适当分数,可以解释为对概率预测绝对错误的概括,可以分解为过度和下限的精确度和处罚措施。