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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
Abstract
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the
United States have served as a visible and important communication channel between
the scientific modeling community and both the general public and decision-makers.
Forecasting models provide specific, quantitative, and evaluable predictions that
inform short-term decisions such as healthcare staffing needs, school closures, and
allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub
(https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of
millions of specific predictions from more than 90 different academic, industry, and
independent research groups. A multimodel ensemble forecast that combined predictions
from dozens of groups every week provided the most consistently accurate probabilistic
forecasts of incident deaths due to COVID-19 at the state and national level from
April 2020 through October 2021. The performance of 27 individual models that submitted
complete forecasts of COVID-19 deaths consistently throughout this year showed high
variability in forecast skill across time, geospatial units, and forecast horizons.
Two-thirds of the models evaluated showed better accuracy than a naïve baseline model.
Forecast accuracy degraded as models made predictions further into the future, with
probabilistic error at a 20-wk horizon three to five times larger than when predicting
at a 1-wk horizon. This project underscores the role that collaboration and active
coordination between governmental public-health agencies, academic modeling teams,
and industry partners can play in developing modern modeling capabilities to support
local, state, and federal response to outbreaks.
Type
Journal articlePermalink
https://hdl.handle.net/10161/26494Published Version (Please cite this version)
10.1073/pnas.2113561119Publication Info
Cramer, Estee Y; Ray, Evan L; Lopez, Velma K; Bracher, Johannes; Brennen, Andrea;
Castro Rivadeneira, Alvaro J; ... Reich, Nicholas G (2022). Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality
in the United States. Proceedings of the National Academy of Sciences of the United States of America, 119(15). pp. e2113561119. 10.1073/pnas.2113561119. Retrieved from https://hdl.handle.net/10161/26494.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Pan Xu
Assistant Professor of Biostatistics & Bioinformatics
My research is centered around Machine Learning, with broad interests in the areas
of Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, High
Dimensional Statistics, and their applications to real-world problems including Bioinformatics
and Healthcare. My research goal is to develop computationally- and data-efficient
machine learning algorithms with both strong empirical performance and theoretical
guarantees.

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