Evaluating epidemic forecasts in an interval format.
Evaluating epidemic forecasts in an interval format.
复制标题
以区间格式评估流行病预测。
DOI：
10.1371/journal.pcbi.1008618
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Reich NG
中科院分区：
文献类型：
--
作者：
Bracher J;Ray EL;Gneiting T;Reich NG
出于实际原因，在当前的冠状病毒病2019(新冠肺炎)大流行背景下，许多对病例、住院和死亡人数的预测是以各级中央预测间隔的形式发布的。新冠肺炎预测中心(https://covid19forecasthub.org/).)收集的预测也是如此预测评估指标，如对数分数，已被应用于几个传染病预测挑战，因此不可用，因为它们需要完整的预测分布。本文概述了评估分位数和区间预报的既定方法如何适用于这种格式的流行病预报。具体地说，我们讨论了加权区间得分的计算和解释，它是一种逼近连续排序概率得分的适当得分。它可以被解释为将绝对误差推广到概率预测，并允许分解为预测过高和预测不足的敏锐度和惩罚措施。在新冠肺炎大流行期间，基于模型的病例、住院和死亡人数概率预测有助于提高对情况的认识并指导公共卫生干预。新冠肺炎预测中心(https://covid19forecasthub.org/))从众多国家和国际组织收集此类预测。对预报进行系统和统计上合理的评估是修改和改进模型并将不同预报组合成集合预报的重要前提。我们直观地介绍了适用于Forecast Hub中使用的基于区间/分位数的格式的评分方法，并将它们与其他常用的绩效衡量标准进行了比较。
For practical reasons, many forecasts of case, hospitalization, and death counts in the context of the current Coronavirus Disease 2019 (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. During the COVID-19 pandemic, model-based probabilistic forecasts of case, hospitalization, and death numbers can help to improve situational awareness and guide public health interventions. The COVID-19 Forecast Hub (https://covid19forecasthub.org/) collects such forecasts from numerous national and international groups. Systematic and statistically sound evaluation of forecasts is an important prerequisite to revise and improve models and to combine different forecasts into ensemble predictions. We provide an intuitive introduction to scoring methods, which are suitable for the interval/quantile-based format used in the Forecast Hub, and compare them to other commonly used performance measures.
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