Estimating Covid-19 excess mortality during and after the pandemic

A Bayesian model, with an application to New Zealand

Dados Bibliográficos

ID24147237
AutoresJohn Bryant (0000-0002-4743-1276), Kim Dunstan, Pubudu Senanayake, Lucianne Varn, Junni Zhang
Ano2026
Volume55
Páginas491-532
Data de publicação2026-09-18
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoDemographic Research (JOURNAL)
Identificadores do periódicoISSN: 1435-9871 • E-ISSN: 2363-7064
EditoraMax Planck Institute for Demographic Research (PUBLISHER • DE)
DOI10.4054/demres.2026.55.17
OpenAlexW7213530381
IdiomaEN
Referências citadas36

BACKGROUND COVID-19 excess deaths are a standard measure of the effects of COVID on mortality.They are defined as the difference between actual death counts and the counts that would have been expected in the absence of the pandemic.One way to identify the full effect of the COVID pandemic on mortality is to calculate excess deaths over the entire course of the pandemic and subsequent return to normality.However, existing methods for deriving expected death counts are not reliable for periods longer than one to two years. OBJECTIVEWe develop a new model for expected deaths that can be used over periods longer than one to two years.We apply the model to estimating monthly excess deaths in New Zealand from February 2020 to December 2025. METHODSBuilding on models originally developed for long-term mortality forecasting, we construct a Bayesian hierarchical model for forecasting expected monthly death counts, disaggregated by age and sex.The model is designed to capture long-term and short-term shifts in underlying trends and age-sex-specific mortality rates.This flexibility allows us to fit the model to 22 years of historical data.The model also accommodates sparse, confidentialized data.

Bayes estimator · Bayesian inference · Bayesian probability · Estimation · Excess mortality · Population · COVID-19 epidemiological studies · Insurance, Mortality, Demography, Risk Management · Statistical Methods and Bayesian Inference