论文标题
以间隔格式评估流行病预测
Evaluating epidemic forecasts in an interval format
论文作者
论文摘要
出于实际原因，在当前Covid-19的案例中，许多案件，住院和死亡人数的预测都以各个级别的中央预测间隔的形式发布。在COVID-19预测中心（https://covid19forecasthub.org/）中收集的预测也是如此。预测评估指标（例如对数得分）已在几种传染病预测挑战中应用，因此由于需要完全的预测分布而无法获得。本文概述了如何将分位数和间隔预测的评估方法应用于这种格式的流行预测。具体而言，我们讨论了加权间隔得分的计算和解释，这是一个近似连续排名概率分数的适当分数。它可以解释为对概率预测的绝对误差的概括，并允许将过度预测和低估的锋利性和惩罚性分解为衡量标准。
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.
