The study tested ensemble models for estimating expected mortality, the baseline against which excess deaths are measured. It used weekly death counts for the United States from the National Vital Statistics System, 2008 to 2024. The authors compared 76 candidate models, with different long-term and seasonal trend specifications, against ensemble models. Ensembles performed more consistently and robustly, with better accuracy and interval coverage than individual models. The abstract does not report the size of these gains in accuracy or coverage.
Why it is interesting: Excess mortality estimates after COVID-19 have varied with the model and analytic choices used. Pooling many trend specifications reduces how far the expected baseline depends on any one of those choices.
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