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  • Abbott, Sam  (2)
  • Biggerstaff, Matthew  (2)
  • 1
    Online-Ressource
    Online-Ressource
    The United States COVID-19 Forecast Hub dataset
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Data Vol. 9, No. 1 ( 2022-08-01)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2022-08-01)
    Kurzfassung: Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.
    Materialart: Online-Ressource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/s41597-022-01517-w
    Sprache: Englisch
    Verlag: Springer Science and Business Media LLC
    Publikationsdatum: 2022
    ZDB Id: 2775191-0
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
    Online-Ressource
  • 2
    Online-Ressource
    Online-Ressource
    Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
    Proceedings of the National Academy of Sciences ; 2022
    In:  Proceedings of the National Academy of Sciences Vol. 119, No. 15 ( 2022-04-12)
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 119, No. 15 ( 2022-04-12)
    Kurzfassung: 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.
    Materialart: Online-Ressource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.2113561119
    RVK:
    TA 1000
    RVK:
    WA 15000
    Sprache: Englisch
    Verlag: Proceedings of the National Academy of Sciences
    Publikationsdatum: 2022
    ZDB Id: 209104-5
    ZDB Id: 1461794-8
    SSG: 11
    SSG: 12
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
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