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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Journal article   Open access   Peer reviewed

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Estee Y Cramer, Evan L Ray, Velma K Lopez, Johannes Bracher, Andrea Brennen, Alvaro J Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H House, Yuxin Huang, …
Proceedings of the National Academy of Sciences - PNAS, Vol.119(15), pp.e2113561119-e2113561119
04/12/2022
DOI: 10.1073/pnas.2113561119
PMCID: PMC9169655
PMID: 35394862
url
https://doi.org/10.1073/pnas.2113561119View
Published (Version of record) Open Access

Abstract

SignificanceThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.
Probability COVID-19 - mortality Data Accuracy Forecasting Humans Pandemics Public Health - trends United States - epidemiology

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