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Table 2 Performance metrics for the timeliness of outbreak detection of the detection algorithms and decision fusion methods

From: Using decision fusion methods to improve outbreak detection in disease surveillance

  Cases required Proportion of delay Time to detection AMOC AUWROC
Mean STD Mean STD Mean STD Mean STD Mean STD
CUSUM 0.47 0.24 0.41 0.20 5.10 2.83 0.83 0.05 0.66 0.11
C1 0.54 0.27 0.49 0.23 6.50 3.44 0.87 0.03 0.50 0.03
C2 0.52 0.27 0.48 0.23 5.90 3.09 0.86 0.03 0.54 0.05
C3 0.56 0.18 0.46 0.16 6.30 2.64 0.82 0.04 0.56 0.07
Farrington 0.46 0.17 0.41 0.14 5.23 2.26 0.87 0.04 0.61 0.10
EWMA 0.41 0.19 0.38 0.14 5.28 2.45 0.87 0.03 0.59 0.08
Majority voting 0.49 0.22 0.44 0.18 5.30 2.56 0.75 0.11 0.57 0.07
Weighted majority voting 0.53 0.24 0.47 0.20 5.43 2.54 0.75 0.11 0.57 0.07
Logistic regression 0.59 0.31 0.56 0.30 7.15 3.82 0.82 0.07 0.63 0.10
CARTa 0.60 0.30 0.57 0.30 7.10 3.85 0.77 0.12 0.62 0.09
Bayesian networks 0.60 0.30 0.56 0.29 6.75 3.55 0.81 0.09 0.63 0.11
  1. aCART Classification and Regression Trees, Cases required proportion of cases needed for outbreak detection, Proportion of delay = 1 – timeliness score, that is: 1- (sum of time to detection) / outbreak duration, AMOC Activity Monitor Operating Characteristic, AUWROC Area Under Weighted ROC, STD Standard Deviation