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Creators/Authors contains: "Zou, Zihang"

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  1. ; ; (, European Conference on Computer Vision (ECCV))
    Accepted Manuscript Full Text Available
  2. ; ; ; ; (, Accident Analysis & Prevention)
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  3. ; ; ; (, Transportation Research Part C: Emerging Technologies)
    null (Ed.)
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  4. ; ; ; ; ; ; ; ; ; ; et al (, Scientific Data)
    Abstract 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. 
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