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 Nov 27;19(11):e1011653. doi: 10.1371/journal.pcbi.1011653. eCollection 2023 Nov.
The effective reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates.
Rt 在 COVID-19 大流行期间的科学、政治和公众讨论中占据核心地位，许多关于该数量的实时估计值经常被公布。这些估计值之间的分歧可能很大，可能导致决策者和公众的困惑。在这项工作中，我们比较了 2020 年和 2021 年德国 COVID-19 的全国性有效繁殖数的不同估计值。我们考虑了来自同一方法但在不同时间点发布的估计值之间的一致性（方法内一致性），以及跨八种不同方法的回溯一致性（方法间一致性）。对于前者，一些方法的估计值随时间非常稳定，几乎无需修订，而其他方法则显示出相当大的波动。为了评估方法间的一致性，我们使用各种统计方法再现了不同小组生成的估计值，标准化了分析选择，以评估它们如何导致观察到的不一致。这些分析选择包括数据源、数据预处理、假设的生成时间分布、统计调整参数以及各种延迟分布。我们发现，在实践中，这些 Rt 估计中的辅助选择至少与统计方法的选择一样强烈地影响结果。因此，它们应该与估计值一起透明地传达。
