Chair of Statistics and Econometrics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
Computational Statistics Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.
Nat Commun. 2021 Aug 27;12(1):5173. doi: 10.1038/s41467-021-25207-0.
Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October-19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.
疾病建模在当前的 COVID-19 大流行期间产生了相当大的政策影响,人们越来越认识到,结合使用多种模型可以提高输出的可靠性。在这里,我们报告了在德国和波兰进行的为期十周的 COVID-19 短期联合预测(2020 年 10 月 12 日至 12 月 19 日)的见解。研究期间涵盖了两国第二波疫情的开始,随着非药物干预措施(NPIs)的收紧,随后报告的病例数量减少(波兰)或趋于平稳并再次增加(德国)。十三个独立团队提供了 COVID-19 病例和死亡的概率实时预测。这些预测的提前期为一到四周,评估重点集中在一到两周的时间范围内,因为这些时间范围受不断变化的 NPIs 的影响较小。预测之间的异质性在单点预测和预测范围方面都很大。集合预测表现出良好的相对性能,特别是在覆盖范围方面,但并没有明显优于单模型预测。该研究已预先注册,并将在大流行的未来阶段进行跟进。