We analyse the temporal and regional structure in mortality rates related to COVID-19 infections, making use of the openly available data on registered cases in Germany published by the Robert Koch Institute on a daily basis. Estimates for the number of present-day infections that will, at a later date, prove to be fatal are derived through a nowcasting model, which relates the day of death of each deceased patient to the corresponding day of registration of the infection. Our district-level modelling approach for fatal infections disentangles spatial variation into a global pattern for Germany, district-specific long-term effects and short-term dynamics, while also taking the age and gender structure of the regional population into account. This enables to highlight areas with unexpectedly high disease activity. The analysis of death counts contributes to a better understanding of the spread of the disease while being, to some extent, less dependent on testing strategy and capacity in comparison to infection counts. The proposed approach and the presented results thus provide reliable insight into the state and the dynamics of the pandemic during the early phases of the infection wave in spring 2020 in Germany, when little was known about the disease and limited data were available.

中文翻译：

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在德国区域层面临近预报致命的 COVID-19 感染

我们分析了与 COVID-19 感染相关的死亡率的时间和区域结构，利用罗伯特科赫研究所每天发布的德国登记病例的公开数据。将在以后证明是致命的当前感染数量的估计是通过临近预报模型得出的，该模型将每个已故患者的死亡日期与感染登记的相应日期联系起来。我们针对致命感染的地区级建模方法将空间变化分解为德国的全球模式、地区特定的长期影响和短期动态，同时还考虑了地区人口的年龄和性别结构。这能够突出具有意外高疾病活动的区域。与感染计数相比，对死亡计数的分析有助于更好地了解疾病的传播，同时在某种程度上较少依赖检测策略和能力。因此，所提出的方法和所呈现的结果提供了对 2020 年春季德国感染浪潮早期阶段的大流行状态和动态的可靠洞察，当时对该疾病知之甚少，可用数据有限。