A Bayesian generalized additive model approach for forecasting mortality improvement with external information
提出贝叶斯广义加性模型，将专家意见等外部信息融入死亡率改善率的预测，用COVID-19数据验证了模型在不同场景下的应用。
Abstract Mortality modelling is facing new challenges as historical mortality experiences are insufficient to foresee unprecedented changes, such as the impact of the COVID-19 pandemic. Expert opinion has become one important source of information that provides additional insights into the pandemic’s possible future courses. In this paper, we develop a Bayesian generalized additive model where external information can be seamlessly integrated into the projection of future mortality improvement (MI) rates. A collection of spline functions over the age and period dimensions is utilized to construct a smooth transition of MI trends from recent changes to long-term rates. Our modelling approach is able to incorporate different types of external information and elicit expert opinions in a coherent probabilistic manner. Lastly, we use three case studies with COVID-19 mortality data to illustrate the applications of the proposed model in different modelling scenarios.