Selected article for: "average score and delay distribution"
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_20
    Snippet: To test the robustness of the model, we simulated ILI data using the final counts from the true dataset, but imposing a time-varying delay distribution; specifically, with faster initial reporting during weeks of high incidence (described in Materials and Methods). Using these simulated data, we found that NobBS was relatively robust to changes in reporting delays (Fig. S4 , Table S2 ). In the context of stable reporting delays (original ILI dat.....
    Document: To test the robustness of the model, we simulated ILI data using the final counts from the true dataset, but imposing a time-varying delay distribution; specifically, with faster initial reporting during weeks of high incidence (described in Materials and Methods). Using these simulated data, we found that NobBS was relatively robust to changes in reporting delays (Fig. S4 , Table S2 ). In the context of stable reporting delays (original ILI data), NobBS performed comparably to the benchmark model (Fig. 2, Table 1 ). However, in the presence of simulated time-varying reporting delays, NobBS outperformed the benchmark in terms of confidence (NobBS average score = 0.06 vs. benchmark average score ≈ 0), point estimates (NobBS rRMSE = 0.302 vs. benchmark rRMSE = 0.621), and accuracy of the predicted change (Table S3) . Such variations are a reality in many epidemics (17) .

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