Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F22%3A00126280" target="_blank" >RIV/00216224:14310/22:00126280 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1073/pnas.2113561119" target="_blank" >http://dx.doi.org/10.1073/pnas.2113561119</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1073/pnas.2113561119" target="_blank" >10.1073/pnas.2113561119</a>
Alternative languages
Result language
angličtina
Original language name
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Original language description
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 naive 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Proceedings of the National Academy of Sciences of the United States of America
ISSN
0027-8424
e-ISSN
1091-6490
Volume of the periodical
119
Issue of the periodical within the volume
15
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
„e2113561119“
UT code for WoS article
000819659900005
EID of the result in the Scopus database
2-s2.0-85127843410