This paper develops a granular regime-switching framework to model mortality deviations from seasonal baseline trends driven by temperature- and epidemiological-related shocks.

The model features three states:

(1) a baseline state that captures observed seasonal mortality patterns,

(2) an environmental shock state for heat waves, and

(3) a respiratory shock state that addresses mortality deviations caused by respiratory outbreaks due to influenza

     and COVID-19.

Transition probabilities between states are modelled 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.

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