Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
Computational Statistics Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.
PLoS Comput Biol. 2023 Aug 11;19(8):e1011394. doi: 10.1371/journal.pcbi.1011394. eCollection 2023 Aug.
Real-time surveillance is a crucial element in the response to infectious disease outbreaks. However, the interpretation of incidence data is often hampered by delays occurring at various stages of data gathering and reporting. As a result, recent values are biased downward, which obscures current trends. Statistical nowcasting techniques can be employed to correct these biases, allowing for accurate characterization of recent developments and thus enhancing situational awareness. In this paper, we present a preregistered real-time assessment of eight nowcasting approaches, applied by independent research teams to German 7-day hospitalization incidences during the COVID-19 pandemic. This indicator played an important role in the management of the outbreak in Germany and was linked to levels of non-pharmaceutical interventions via certain thresholds. Due to its definition, in which hospitalization counts are aggregated by the date of case report rather than admission, German hospitalization incidences are particularly affected by delays and can take several weeks or months to fully stabilize. For this study, all methods were applied from 22 November 2021 to 29 April 2022, with probabilistic nowcasts produced each day for the current and 28 preceding days. Nowcasts at the national, state, and age-group levels were collected in the form of quantiles in a public repository and displayed in a dashboard. Moreover, a mean and a median ensemble nowcast were generated. We find that overall, the compared methods were able to remove a large part of the biases introduced by delays. Most participating teams underestimated the importance of very long delays, though, resulting in nowcasts with a slight downward bias. The accompanying prediction intervals were also too narrow for almost all methods. Averaged over all nowcast horizons, the best performance was achieved by a model using case incidences as a covariate and taking into account longer delays than the other approaches. For the most recent days, which are often considered the most relevant in practice, a mean ensemble of the submitted nowcasts performed best. We conclude by providing some lessons learned on the definition of nowcasting targets and practical challenges.
实时监测是应对传染病爆发的关键要素。然而，发病率数据的解释常常受到数据收集和报告各个阶段延迟的影响。因此，最近的数据值存在向下偏差，掩盖了当前的趋势。可以采用统计即时预测技术来纠正这些偏差，准确描述最近的发展情况，从而增强态势感知。在本文中，我们对 8 种即时预测方法进行了预先注册的实时评估，这些方法由独立的研究团队应用于德国 COVID-19 大流行期间的 7 天住院发病率。该指标在德国疫情管理中发挥了重要作用，通过某些阈值与非药物干预水平相关联。由于其定义，即住院人数是按病例报告日期而不是入院日期进行汇总的，因此德国的住院发病率特别容易受到延迟的影响，可能需要数周或数月才能完全稳定。在这项研究中，所有方法均从 2021 年 11 月 22 日至 2022 年 4 月 29 日应用，每天为当前和 28 天前的日期生成概率性即时预测。国家、州和年龄组层面的即时预测结果以公共存储库中的分位数形式收集，并在仪表板中显示。此外，还生成了均值和中位数集成即时预测。我们发现，总体而言，相比方法能够消除延迟引入的大部分偏差。然而，大多数参与团队低估了非常长延迟的重要性，导致即时预测存在轻微的向下偏差。几乎所有方法的预测区间也都太窄。在所有即时预测范围内，表现最好的是一种使用病例发病率作为协变量并考虑比其他方法更长延迟的模型。对于最近的几天，这在实践中通常被认为是最相关的，提交的即时预测的均值集成表现最好。最后，我们提供了一些关于即时预测目标定义和实际挑战的经验教训。
