Selected article for: "benchmark model 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_13
    Snippet: The benchmark approach made predictions in only 55% of weeks in the dengue time series (Table 1 ). In this subset of weeks, the NobBS approach achieved relatively smooth and accurate tracking of the dengue time series (rRMSE = 0.464, average score = 0.274) despite low proportions of cases reported on the week of onset ( Fig. 1A -B). The 95% PI coverage was 0.85, indicating that the 95% PI included the true number of cases for 85% of the nowcasts......
    Document: The benchmark approach made predictions in only 55% of weeks in the dengue time series (Table 1 ). In this subset of weeks, the NobBS approach achieved relatively smooth and accurate tracking of the dengue time series (rRMSE = 0.464, average score = 0.274) despite low proportions of cases reported on the week of onset ( Fig. 1A -B). The 95% PI coverage was 0.85, indicating that the 95% PI included the true number of cases for 85% of the nowcasts. In comparison, the benchmark approach produced substantially less accurate point estimates and slightly broader uncertainty intervals (rRMSE = 1.24, average score = 0.161, 95% PI coverage = 0.90) with greater fluctuation in nowcasts from week-to-week ( Fig. 1C-D) . Because many weeks in the dengue data were low incidence, assigning a prediction of 0 to the benchmark approach's missing nowcasts improved its rRMSE to 1.14 in the full time series compared to 1.24 for the subset over which nowcasts were generated from the model, though NobBS still surpassed the benchmark model's accuracy on this and all other metrics (Table 1) .

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