Cargando…
Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States
The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be...
Autores principales: | Ray, Evan L., Brooks, Logan C., Bien, Jacob, Biggerstaff, Matthew, Bosse, Nikos I., Bracher, Johannes, Cramer, Estee Y., Funk, Sebastian, Gerding, Aaron, Johansson, Michael A., Rumack, Aaron, Wang, Yijin, Zorn, Martha, Tibshirani, Ryan J., Reich, Nicholas G. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier B.V. on behalf of International Institute of Forecasters.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247236/ https://www.ncbi.nlm.nih.gov/pubmed/35791416 http://dx.doi.org/10.1016/j.ijforecast.2022.06.005 |
Ejemplares similares
-
Recalibrating probabilistic forecasts of epidemics
por: Rumack, Aaron, et al.
Publicado: (2022) -
The United States COVID-19 Forecast Hub dataset
por: Cramer, Estee Y., et al.
Publicado: (2022) -
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
por: Cramer, Estee Y., et al.
Publicado: (2022) -
Reply to Bracher: Scoring probabilistic forecasts to maximize public health interpretability
por: Reich, Nicholas G., et al.
Publicado: (2019) -
Scoring epidemiological forecasts on transformed scales
por: Bosse, Nikos I., et al.
Publicado: (2023)