We aim to assess the impact of a pandemic data point on the calibration of a stochastic multi-population mortality projection model and its resulting projections for future mortality rates. Throughout the paper we put focus on the Li & Lee mortality model, which has become a standard for projecting mortality in Belgium and the Netherlands. We calibrate this mortality model on annual deaths and exposures at the level of individual ages. This type of mortality data is typically collected, produced and reported with a significant delay of -- for some countries -- several years on a platform such as the Human Mortality Database. To enable a timely evaluation of the impact of a pandemic data point we have to rely on other data sources (e.g. the Short-Term Mortality Fluctuations Data series) that swiftly publish weekly mortality data collected in age buckets. To be compliant with the design and calibration strategy of the Li & Lee model, we have to transform the weekly mortality data collected in age buckets to yearly, age-specific observations. Therefore, our paper constructs a protocol to ungroup the deaths and exposures registered in age buckets to individual ages. To evaluate the impact of a pandemic shock, like COVID-19 in the year 2020, we weigh this data point in either the calibration or projection step. Obviously, the more weight we place on this data point, the more impact we observe on future estimated mortality rates and life expectancies. Our paper allows to quantify this impact and provides actuaries and actuarial associations with a framework to generate scenarios of future mortality under various assessments of the pandemic data point.

翻译：我们的目标是评估大流行病数据点对随机多人口死亡率预测模型及其对未来死亡率的预测的校准的影响。我们在整个论文中将重点放在作为比利时和荷兰死亡率预测标准的Li & Lee死亡率模型上,该模型已成为比利时和荷兰死亡率预测标准。我们根据个体年龄水平的年死亡率和接触量校准这一死亡率模型。这种类型的死亡率数据通常在收集、制作和报告方面大大推迟了一些国家在诸如人类死亡数据库等平台上收集、制作和报告数年的数据点。为了能够及时评估流行病数据点的影响,我们必须依靠其他数据来源(例如短期死亡率结构数据系列)迅速公布在年龄桶中收集的每周死亡率数据。为了符合Li & Lee模型的设计和校准战略,我们必须将年龄桶中收集的每周死亡率数据转换为每年的、具体年龄的观察。因此,我们的文件构建了一项协议,将死亡率和在年龄桶中登记的死亡率和暴露到个体年龄的情景分类。为了评估对大流行病未来影响的影响进行评估,例如短期死亡率结构数据,我们在2020年的精算评估中将这一指标值纳入我们这一指数的预测。