预测何时有用?评估流行病预测的新方法。

Maximilian Marshall, Felix Parker, Lauren M Gardner
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引用次数: 0

摘要

背景:COVID-19 不会是二十一世纪最后一次大流行。为了更好地应对下一次大流行,我们必须诚实地评估应对 COVID 的各种措施的效用。在本文中,我们特别关注流行病学预测。描述大流行病历史上的预测效果具有挑战性,尤其是考虑到其巨大的空间、时间和环境变异性。有鉴于此,我们引入了加权上下文区间得分(WCIS),这是一种用于回顾性区间预测评估的新方法:方法:WCIS 的核心原则是将上下文效用直接纳入评估。这就需要根据预测的使用情况,通过定义效用阈值参数,对预测效果进行具体描述。作为现有加权区间分数(WIS)的扩展,这一想法被推广到流行病学建模首选的概率区间预测中:我们将 WCIS 应用于两种预测情况:单个州的设施级住院人数和全美州级住院人数。我们发现,对 WCIS 进行适当参数化后,它既能反映有用预测的相对质量,也能反映有用预测的总体频率。由于 WCIS 使用上下文归一化来表示预测的效用,因此它很容易在高度多变的大流行情景中进行比较,同时还能直观地代表单个预测的现场质量:WCIS 为概率预测提供了一种实用的基于效用的表征方法。这种方法的明确目的是让那些可能不具备预测专业知识、但却是流行病应对工作重要合作伙伴的从业人员和决策者能够使用预测结果并对其进行深入分析。我们注意到,WCIS 专门用于回顾性预测评估,不应在竞争环境中用作最小化惩罚,因为它缺乏统计适当性。用于我们分析的代码和数据可在 https://github.com/maximilian-marshall/wcis 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
When are predictions useful? A new method for evaluating epidemic forecasts.

Background: COVID-19 will not be the last pandemic of the twenty-first century. To better prepare for the next one, it is essential that we make honest appraisals of the utility of different responses to COVID. In this paper, we focus specifically on epidemiologic forecasting. Characterizing forecast efficacy over the history of the pandemic is challenging, especially given its significant spatial, temporal, and contextual variability. In this light, we introduce the Weighted Contextual Interval Score (WCIS), a new method for retrospective interval forecast evaluation.

Methods: The central tenet of the WCIS is a direct incorporation of contextual utility into the evaluation. This necessitates a specific characterization of forecast efficacy depending on the use case for predictions, accomplished via defining a utility threshold parameter. This idea is generalized to probabilistic interval-form forecasts, which are the preferred prediction format for epidemiological modeling, as an extension of the existing Weighted Interval Score (WIS).

Results: We apply the WCIS to two forecasting scenarios: facility-level hospitalizations for a single state, and state-level hospitalizations for the whole of the United States. We observe that an appropriately parameterized application of the WCIS captures both the relative quality and the overall frequency of useful forecasts. Since the WCIS represents the utility of predictions using contextual normalization, it is easily comparable across highly variable pandemic scenarios while remaining intuitively representative of the in-situ quality of individual forecasts.

Conclusions: The WCIS provides a pragmatic utility-based characterization of probabilistic predictions. This method is expressly intended to enable practitioners and policymakers who may not have expertise in forecasting but are nevertheless essential partners in epidemic response to use and provide insightful analysis of predictions. We note that the WCIS is intended specifically for retrospective forecast evaluation and should not be used as a minimized penalty in a competitive context as it lacks statistical propriety. Code and data used for our analysis are available at https://github.com/maximilian-marshall/wcis .

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