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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