Supplementary material from \"A retrospective assessment of COVID-19 model performance in the USA\"

Kyle J. Colonna, Gabriela F. Nane, Ernani F. Choma, Roger M. Cooke & John S. Evans
Coronavirus disease 2019 (COVID-19) forecasts from over 100 models are readily available. However, little published information exists regarding the performance of their uncertainty estimates (i.e. probabilistic performance). To evaluate their probabilistic performance, we employ the classical model (CM), an established method typically used to validate expert opinion. In this analysis, we assess both the predictive and probabilistic performance of COVID-19 forecasting models during 2021. We also compare the performance of aggregated forecasts (i.e. ensembles) based...
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