@article{Bryant_55_17,
author = {Bryant, John and Zhang, Junni and Dunstan, Kim and Varn, Lucianne and Senanayake, Pubudu},
title={{Estimating COVID-19 excess mortality during and after the pandemic: A Bayesian model, with an application to New Zealand}},
journal = {Demographic Research},
volume = {55},
number = {17},
pages = {491--532},
doi = {10.4054/DemRes.2026.55.17},
year = {2026},
abstract = {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. 

Objective: We 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. 

Methods: Building 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. 

Conclusions: The model captures the key features of the data in the years leading up to the pandemic, lending credibility to the associated estimates of expected and excess deaths. Although the excess death estimates are subject to considerable uncertainty, they confirm that New Zealand experienced relatively few excess deaths during the pandemic, and suggest that by 2025 the country had returned to pre-pandemic mortality trends. 

Contribution: We build on existing mortality forecasting methods to develop a new Bayesian hierarchical model. The new model captures complex mortality dynamics, allowing it to forecast expected deaths, and estimate excess COVID deaths, over periods longer than one to two years. },
URL = {https://www.demographic-research.org/volumes/vol55/17/},
eprint = {https://www.demographic-research.org/volumes/vol55/17/55-17.pdf}
}