Statistical Consulting Unit StaBLab, LMU Munich, Germany.
Department of Genetic Epidemiology, University of Regensburg, Germany.
Epidemiol Infect. 2021 Mar 11;149:e68. doi: 10.1017/S0950268821000558.
We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.
我们分析了 2020 年 3 月至 4 月底德国的 2019 年冠状病毒病疫情曲线。我们使用统计模型来估计给定日期发病的病例数,并使用回溯技术来获得每日新感染的数量。分别通过带有转折点的趋势回归模型分析相应的时间序列。转折点是直接从数据中估计出来的。我们对德国和巴伐利亚联邦州进行了分析,因为那里有更详细的数据。这两项分析都表明,3 月 9 日至 13 日之间,感染时间序列发生了重大变化:从大幅增加转为减少。另一个变化发生在 3 月 25 日至 29 日之间,下降趋势加剧。此外,我们还按年龄进行了分层分析。一个主要结果是,80 岁以上人群的大流行进程出现延迟,导致 3 月底出现转折点。我们的结果与其他作者的结果不同,因为我们考虑到了报告延迟,这与新报告病例的曲线相比,报告延迟是时间相关的,因此改变了疫情曲线的结构。