• 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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