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Autor
Abbott, Sam 1 Abernethy, Neil F 1 Adee, Madeline 1 Adhikari, Bijaya 1 Arik, Sercan O 1 Asplund, John 1 Ayer, Turgay 1 Baccam, Prasith 1 Baek, Jackie 1 Baer, Thomas M 1 Ban, Xuegang 1 Bannur, Nayana 1 Barber, Ryan 1 Baxter, Arden 1 Ben-Nun, Michal 1 Bennouna, Mohammed Amine 1 Bertsimas, Dimitris 1 Bian, Jiang 1 Biegel, Hannah 1 Bien, Jacob 1
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Pracoviště
Advanced Data Analytics Metron Inc Re... 1 Atlanta GA 30333 1 COVID 19 Response Centers for Disease... 1 Cambridge MA 02139 1 Catalog Data Science Walmart Inc Sunn... 1 Centre for Mathematical Modelling of ... 1 Chair of Econometrics and Statistics ... 1 College of Computing Georgia Institut... 1 College of Information and Computer S... 1 Computational Statistics Group Heidel... 1 Construx Bellevue WA 98004 1 Core Consultant Group Oliver Wyman Ne... 1 Department of Biological Sciences Uni... 1 Department of Biomedical Data Science... 1 Department of Biostatistics Columbia ... 1 Department of Biostatistics Universit... 1 Department of Biostatistics Universit... 1 Department of Biostatistics and Epide... 1 Department of Civil and Environmental... 1 Department of Civil and Systems Engin... 1
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- Zeměpisné označení
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Autor
Abbott, Sam 1 Abernethy, Neil F 1 Adee, Madeline 1 Adhikari, Bijaya 1 Arik, Sercan O 1 Asplund, John 1 Ayer, Turgay 1 Baccam, Prasith 1 Baek, Jackie 1 Baer, Thomas M 1 Ban, Xuegang 1 Bannur, Nayana 1 Barber, Ryan 1 Baxter, Arden 1 Ben-Nun, Michal 1 Bennouna, Mohammed Amine 1 Bertsimas, Dimitris 1 Bian, Jiang 1 Biegel, Hannah 1 Bien, Jacob 1
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Pracoviště
Advanced Data Analytics Metron Inc Re... 1 Atlanta GA 30333 1 COVID 19 Response Centers for Disease... 1 Cambridge MA 02139 1 Catalog Data Science Walmart Inc Sunn... 1 Centre for Mathematical Modelling of ... 1 Chair of Econometrics and Statistics ... 1 College of Computing Georgia Institut... 1 College of Information and Computer S... 1 Computational Statistics Group Heidel... 1 Construx Bellevue WA 98004 1 Core Consultant Group Oliver Wyman Ne... 1 Department of Biological Sciences Uni... 1 Department of Biomedical Data Science... 1 Department of Biostatistics Columbia ... 1 Department of Biostatistics Universit... 1 Department of Biostatistics Universit... 1 Department of Biostatistics and Epide... 1 Department of Civil and Environmental... 1 Department of Civil and Systems Engin... 1
- Formát
- Publikační typ
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- Kategorie
- Zeměpisné označení
- Jazyk
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NLK
Free Medical Journals
od 1915 do Před 6 měsíci
Freely Accessible Science Journals
od 1915 do Před 6 měsíci
PubMed Central
od 1915 do Před 6 měsíci
Europe PubMed Central
od 1915 do Před 6 měsíci
Open Access Digital Library
od 1915-01-15
Open Access Digital Library
od 1915-01-01
PubMed
35394862
DOI
10.1073/pnas.2113561119
Knihovny.cz E-zdroje
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.
- MeSH
- COVID-19 * mortalita MeSH
- lidé MeSH
- pandemie MeSH
- pravděpodobnost MeSH
- předpověď MeSH
- správnost dat MeSH
- veřejné zdravotnictví trendy MeSH
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- lidé MeSH
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- časopisecké články MeSH
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- Spojené státy americké MeSH
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