Selected article for: "benchmark approach and NobBS approach"
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_14
    Snippet: Nowcast point estimates tracked the ILI time series well for both approaches, though point estimates had greater error by all measures for the NobBS approach (NobBS rRMSE = 0.074 vs. benchmark rRMSE = 0.062; Table 1 ). However, the NobBS approach produced considerably wider prediction intervals (Figs. 1C, 2C) resulting in both higher log scores (NobBS average score = 0.218 vs. benchmark average score = 0.017) and 100% coverage by the 95% predicti.....
    Document: Nowcast point estimates tracked the ILI time series well for both approaches, though point estimates had greater error by all measures for the NobBS approach (NobBS rRMSE = 0.074 vs. benchmark rRMSE = 0.062; Table 1 ). However, the NobBS approach produced considerably wider prediction intervals (Figs. 1C, 2C) resulting in both higher log scores (NobBS average score = 0.218 vs. benchmark average score = 0.017) and 100% coverage by the 95% prediction intervals compared to 0% coverage for the benchmark (Table 1) .

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