COVID-19 related deaths underestimate the pandemic burden on mortality because they suffer from completeness and accuracy issues. Excess mortality is a popular alternative, as it compares observed with expected deaths based on the assumption that the pandemic did not occur. Expected deaths had the pandemic not occurred depend on population trends, temperature, and spatio-temporal patterns. In addition to this, high geographical resolution is required to examine within country trends and the effectiveness of the different public health policies. In this tutorial, we propose a framework using R to estimate and visualise excess mortality at high geographical resolution. We show a case study estimating excess deaths during 2020 in Italy. The proposed framework is fast to implement and allows combining different models and presenting the results in any age, sex, spatial and temporal aggregation desired. This makes it particularly powerful and appealing for online monitoring of the pandemic burden and timely policy making.

翻译：新冠肺炎相关死亡人数因存在完整性和准确性问题，低估了疫情对死亡率的影响。超额死亡率作为一种常用替代指标，通过将观察到的死亡人数与基于疫情未发生假设的预期死亡人数进行比较来衡量。在无疫情情境下的预期死亡人数取决于人口变化趋势、温度及时空模式。此外，为分析国家内部趋势及不同公共卫生政策的效果，需要较高的地理分辨率。在本教程中，我们提出一个基于R语言的框架，用于高地理分辨率下的超额死亡率估计与可视化。我们以意大利2020年期间的超额死亡估计为例进行案例研究。该框架实现快速，支持整合不同模型，并能按任意年龄、性别、空间和时间聚合维度呈现结果。这一特性使其特别适用于疫情负担的在线监测和及时的政策制定。