Granular mortality modeling with temperature and epidemic shocks: A three-state regime-switching approach
开发了一个三状态区制转换框架，将死亡率偏离季节性基线的部分归因于温度和流行病冲击，并用法国21个地区六年龄组的周数据校准，为保险公司和医疗机构提供风险管理和资源规划依据。
This paper develops a granular regime-switching framework to model mortality deviations from seasonal baseline trends driven by temperature and epidemic shocks. The framework features three states: (1) a baseline state that captures observed seasonal mortality patterns, (2) a summer shock state that captures heat waves and other high mortality events, and (3) a winter shock state that addresses mortality deviations caused by cold spells and strong outbreaks of respiratory diseases due to influenza and COVID-19. Transition probabilities between states are modeled using covariate-dependent multinomial logit functions. These functions incorporate, among others, lagged temperature and influenza incidence rates as predictors, allowing dynamic adjustments to evolving shocks. Calibrated on weekly mortality data across 21 French regions and six age groups, the regime-switching framework accounts for spatial and demographic heterogeneity. Under various projection scenarios for temperature and influenza, we quantify uncertainty in mortality forecasts through prediction intervals constructed using an extensive bootstrap approach. These projections can guide insurance companies, healthcare providers, and hospitals in managing risks and planning resources for potential future shocks.