When pandemics like COVID-19 spread around the world, the rapidly evolving situation compels officials and executives to take prompt decisions and adapt policies depending on the current state of the disease. In this context, it is crucial for policymakers to have always a firm grasp on what is the current state of the pandemic, and to envision how the number of infections and possible deaths is going to evolve over the next weeks. However, as in many other situations involving compulsory registration of sensitive data from multiple collectors, cases might be reported with errors, often with delays deferring an up-to-date view of the state of things. Errors in collecting new cases affect the overall mortality, resulting in excess deaths reported by official statistics only months later. In this paper, we provide tools for evaluating the quality of pandemic mortality data. We accomplish this through a Bayesian approach accounting for the excess mortality pandemics might bring with respect to the normal level of mortality in the population.

翻译：当诸如COVID-19的大流行病在全球蔓延时，快速演变的局势迫使官员和高管们根据疾病当前状态迅速做出决策并调整政策。在此背景下，政策制定者始终准确把握大流行病的当前状态，并预判未来数周感染人数及可能死亡人数的变化趋势，显得至关重要。然而，与许多涉及由多个数据采集者强制登记敏感数据的其他情况类似，病例报告可能存在错误，且常因延迟而无法反映事态的最新情况。新病例收集中出现的错误会影响总体死亡率，导致官方统计数据数月后才能报告超额死亡。本文提供了评估大流行病死亡率数据质量的工具。我们通过一种贝叶斯方法实现了这一目标，该方法考虑了大流行病可能给人口正常死亡率带来的超额死亡。