Fig. 1: Cumulative mortality forecasts and prediction errors by model—example for the United States.
From: Predictive performance of international COVID-19 mortality forecasting models
The most recent version of each model is shown on the top left, as well as 95% prediction intervals when available. The middle row shows all iterations of each model as separate lines. The vertical dashed lines indicate the first and last model release date for each model. The bottom row shows all errors calculated at weekly intervals (circles). The top right panel summarises all observed errors, using median error (top) and median absolute error (bottom), by weeks of forecasting and month of model estimation. Errors incorporate an intercept shift to account for differences in each model’s input data. This figure represents an example for the United States of country-specific plots made for all locations examined in this study. Graphs for all geographies can be found in the Supplementary Information. Note that while certain models use different input data source than the other modelling groups causing apparently discordant past trends in the top-left panel. We plot raw estimates on the top-left panel; however, we implement an intercept shift to account for this issue in the calculation of errors. Delphi DELPHI-MIT (red), Los Alamos Nat Lab Los Alamos National Laboratory (blue), Youyang Gu (orange), Imperial Imperial College London (peach), SIKjalpha USC SIKJ-alpha (pink), IHME Institute for Health Metrics and Evaluation (green), UCLA-ML UCLA Statistical Machine Learning Lab (purple).
