Work

Dependence modelling across major causes of death via time-varying copula state space models

Year: 2025

Type: article

Abstract: We propose a time-varying copula state space approach, which quantifies and visualizes the joint dynamics across major causes of death, utilizing data both before and during the COVID-19 pandemic. Our research investigates how the COVID-19 pandemic has affected mortality experience of five major causes, and more importantly how COVID-19 has changed the dependence structure across these causes. This enables us to gain more insights into the potential impact of COVID-19 on future life expectancy, and conduct scenario-based projections. Based on US weekly mortality data from January 2015 to November 2022, we find that COVID-19 has elevated mortality levels for the majority of causes and altered the dependence structure across these causes, particularly for Alzheimer’s and respiratory diseases. In our scenario-based analysis, we observe a noticeably wider prediction interval for total deaths when the number of COVID-19 deaths is assumed to be high, confirming the significant impact of the pandemic on population mortality. This finding could help explain the extreme mortality levels experienced during the pandemic.

Source: Journal of the Royal Statistical Society Series C (Applied Statistics)

DOI: 10.1093/jrsssc/qlaf049

Authors: Ariane Hanebeck, Han Li, Claudia Czado

Institutions: Technical University of Munich, The University of Melbourne

Language: English

Topic: Insurance, Mortality, Demography, Risk Management

Subfield: Demography

Field: Social Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,456