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Cramer, Estee Y.
[VerfasserIn];
Ray, Evan L.
[VerfasserIn];
Lopez, Velma K.
[VerfasserIn];
Bracher, Johannes
[VerfasserIn];
Brennen, Andrea
[VerfasserIn];
Castro Rivadeneira, Alvaro J.
[VerfasserIn];
Gerding, Aaron
[VerfasserIn];
Gneiting, Tilmann
[VerfasserIn];
House, Katie H.
[VerfasserIn];
Huang, Yuxin
[VerfasserIn];
Jayawardena, Dasuni
[VerfasserIn];
Kanji, Abdul H.
[VerfasserIn];
Khandelwal, Ayush
[VerfasserIn];
Le, Khoa
[VerfasserIn];
Mühlemann, Anja
[VerfasserIn];
Niemi, Jarad
[VerfasserIn];
Shah, Apurv
[VerfasserIn];
Stark, Ariane
[VerfasserIn];
Wang, Yijin
[VerfasserIn];
Wattanachit, Nutcha
[VerfasserIn];
Zorn, Martha W.
[VerfasserIn];
Gu, Youyang
[VerfasserIn];
Jain, Sansiddh
[VerfasserIn];
Bannur, Nayana
[VerfasserIn];
[...]
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
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- Medientyp: E-Artikel; Sonstige Veröffentlichung
- Titel: Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
- Beteiligte: Cramer, Estee Y. [VerfasserIn]; Ray, Evan L. [VerfasserIn]; Lopez, Velma K. [VerfasserIn]; Bracher, Johannes [VerfasserIn]; Brennen, Andrea [VerfasserIn]; Castro Rivadeneira, Alvaro J. [VerfasserIn]; Gerding, Aaron [VerfasserIn]; Gneiting, Tilmann [VerfasserIn]; House, Katie H. [VerfasserIn]; Huang, Yuxin [VerfasserIn]; Jayawardena, Dasuni [VerfasserIn]; Kanji, Abdul H. [VerfasserIn]; Khandelwal, Ayush [VerfasserIn]; Le, Khoa [VerfasserIn]; Mühlemann, Anja [VerfasserIn]; Niemi, Jarad [VerfasserIn]; Shah, Apurv [VerfasserIn]; Stark, Ariane [VerfasserIn]; Wang, Yijin [VerfasserIn]; Wattanachit, Nutcha [VerfasserIn]; Zorn, Martha W. [VerfasserIn]; Gu, Youyang [VerfasserIn]; Jain, Sansiddh [VerfasserIn]; Bannur, Nayana [VerfasserIn]; [...]
- Erschienen: National Academy of Sciences, 2022-05-04
- Erschienen in: Proceedings of the National Academy of Sciences of the United States of America, 119 (15), e2113561119 ; ISSN: 0027-8424, 1091-6490
- Sprache: Englisch
- DOI: https://doi.org/10.5445/IR/1000145738; https://doi.org/10.1073/pnas.2113561119
- ISSN: 0027-8424; 1091-6490
- Schlagwörter: Mathematics
- Beschreibung: 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.
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