Browsing by Author "Niemi, Jarad"

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    The United States COVID-19 Forecast Hub dataset
    (Springer, 2022-08-01) Cramer, Estee Y.; Huang, Yuxin; Wang, Yijin; Ray, Evan L.; Cornell, Matthew; Bracher, Johannes; Brennen, Andrea; Rivadeneira, Alvaro J. Castro; Gerding, Aaron; House, Katie; Jayawardena, Dasuni; Kanji, Abdul Hannan; Khandelwal, Ayush; Le, Khoa; Mody, Vidhi; Mody, Vrushti; Niemi, Jarad; Stark, Ariane; Shah, Apurv; Wattanchit, Nutcha; Zorn, Martha W.; Reich, Nicholas G.; US COVID-19 Forecast Hub Consortium; Computer Science, Luddy School of Informatics, Computing, and Engineering
    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.