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. 2025 Jun;30(25):2400753.
doi: 10.2807/1560-7917.ES.2025.30.25.2400753.

Real-time monitoring of excess mortality under a new endemic regime

Sasikiran Kandula  1 , Birgitte F de Blasio  1   2 , Marissa LeBlanc  1   2
Affiliations

Real-time monitoring of excess mortality under a new endemic regime

Sasikiran Kandula et al. Euro Surveill. 2025 Jun.

Abstract

BACKGROUNDMonitoring of mortality to identify trends and detect deviations from normal levels is an essential part of routine surveillance. In many European countries, disruptions in mortality patterns from the COVID-19 pandemic have required revisions to expected mortality estimates (and models) in the current endemic phase of SARS-CoV-2.AIMTo identify essential characteristics for future mortality surveillance and describe two Bayesian methods that satisfy these criteria while being robust to past periods of high COVID-19 mortality. We demonstrate their application in 19 European countries and subnational estimates in the United States, and report measures of model calibration.METHODSWe used a generalised additive model (GAM) with smoothed spline terms for annual trend and within-year seasonality and a generalised linear model (GLM) with a Serfling component for within-year seasonality and breakpoints to detect trend changes in trend. Both approaches modelled change in population size and group-specific (age and sex) mortality patterns.RESULTSModels were well-calibrated and able to estimate national and group-specific mortality before and during the acute COVID-19 pandemic phase. The effect of inclusion of mortality from the acute pandemic period was primarily an increase in uncertainty in expected mortality over the projection period. The GAM approach had better calibration and less variability in bias among countries.CONCLUSIONModels that can adapt to mortality anomalies seen during the acute COVID-19 pandemic period without a need for adjustments to observational data, or tailoring of model specifications, are feasible. The proposed methods can complement operational national and inter-agency surveillance systems currently used in Europe.

Keywords: COVID-19; break points; endemicity; generalised additive models; mortality surveillance.

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Conflict of interest statement

Conflict of interest: None declared.

Figures

A trend plot of observed weekly mortality overlaid with model fit and its predictions for expected and excess mortality.
Figure 1
Estimates of expected and excess deaths from a generalised additive model fit with mortality observed through three different endpoints, Finland, week 1/2020–week 26/2024
A grid of 18 panels, each showing trends in observed weekly mortality in different age–sex group, overlaid with model fit and estimates for expected deaths.
Figure 2
Age- and sex-stratified estimates of expected and excess deaths from a generalised additive model fit with mortality observed through week 26/2023, Finland, week 1/2018–week 26/2024
A bar plot showing the median and 95% prediction intervals of expected and excess all-cause mortality between week 27/2023 and week 26/2024 in 19 European countries.
Figure 3
Annual estimates of expected and excess mortality per 100,000 population from generalised additive models trained on mortality observed through three different endpoints, 19 European countries
A heatmap showing the probability of a any excess deaths during a given week for each of 19 European countries.
Figure 4
Probability of excess mortality during one week, as estimated by the generalised additive model, using mortality observed through week 52/2019 and week 26/2023, 19 European countries
A multi-panel plot showing change in model calibration by projection horizon and by country.
Figure 5
Calibration measures for GAM/GLM models, summarised across 19 countries, years, age–sex groups and projection horizons, 2011–2019
A multi-panel plot comparing weekly z-scores from three models in 12 countries.
Figure 6
Comparison of z-scores estimated by EuroMOMO and GAM/GLM models, 12 European countries, week 1/2020–week 26/2024
See this image and copyright information in PMC

References

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