Selected article for: "benchmark approach and brown line"
Author: Sarah F. McGough; Michael A. Johansson; Marc Lipsitch; Nicolas A. Menzies
Title: Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking
  • Document date: 2019_6_7
    • ID: 6kq0ptlg_10
    Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. . https://doi.org/10.1101/663823 doi: bioRxiv preprint accuracy, as measured by the log score and the prediction error, are compared to (C) nowcasts by the benchmark approach with (D) corresponding log scores and prediction errors. For nowcasting, the number of newly-reported cases each week (blue line) are the only data available in real-time for that week.....
    Document: The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. . https://doi.org/10.1101/663823 doi: bioRxiv preprint accuracy, as measured by the log score and the prediction error, are compared to (C) nowcasts by the benchmark approach with (D) corresponding log scores and prediction errors. For nowcasting, the number of newly-reported cases each week (blue line) are the only data available in real-time for that week, and help inform the estimate of the total number of cases that will be eventually reported (red line), shown with 95% prediction intervals (pink bands). For the benchmark approach, the 95% prediction intervals are very narrow and are thus difficult to see. The true number of cases eventually reported (black line) is known only in hindsight and is the nowcast target. Historical information on reporting is available within a 27-week moving window (grey shade) and used to make nowcasts. The log score (brown line) and the difference between the true and mean estimated number of cases (grey line) are shown as a function of time.

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