论文标题
估计和可视化在19日大流行期间过剩死亡率的框架
A framework for estimating and visualising excess mortality during the COVID-19 pandemic
论文作者
论文摘要
Covid-19相关的死亡人数低估了死亡率的大流行负担，因为它们遭受了完整性和准确性问题。过量死亡率是一种流行的替代方案，因为它基于没有发生大流行的假设与预期的死亡进行了比较。预期死亡的发生未发生，这取决于人口趋势，温度和时空模式。除此之外，还需要高地理决议来检查国家趋势范围内的趋势和不同公共卫生政策的有效性。在本教程中，我们提出了一个使用R的框架来估计和可视化高地理分辨率的过剩死亡率。我们展示了一项案例研究，估计在意大利2020年期间死亡过多。所提出的框架可以迅速实施，并允许在任何年龄，性别，空间和时间聚集中结合不同模型并呈现结果。这使得它特别有力且吸引人在线监控大流行负担和及时的政策制定。
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.
