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Table 1 Performance metrics for the accuracy and prediction quality of the outbreak detection algorithms and the decision fusion methods

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

  Sensitivity per outbreak Sensitivity per day Specificity PPV NPV AUC
Mean STD Mean STD Mean STD Mean STD Mean STD Mean STD
CUSUM 0.83 0.28 0.45 0.29 0.87 0.17 0.49 0.35 0.94 0.03 0.73 0.14
C1 0.72 0.34 0.10 0.07 0.99 0.00 0.38 0.21 0.92 0.01 0.53 0.02
C2 0.74 0.33 0.16 0.11 0.99 0.00 0.45 0.23 0.92 0.01 0.57 0.04
C3 0.82 0.25 0.25 0.14 0.96 0.00 0.36 0.16 0.93 0.01 0.62 0.07
Farrington 0.86 0.20 0.20 0.11 0.97 0.02 0.51 0.33 0.92 0.01 0.66 0.10
EWMA 0.89 0.20 0.29 0.17 0.95 0.02 0.37 0.20 0.93 0.02 0.64 0.09
Majority voting 0.82 0.26 0.24 0.17 0.99 0.01 0.61 0.32 0.93 0.02 0.60 0.09
Weighted majority voting 0.78 0.31 0.23 0.17 0.99 0.01 0.66 0.33 0.93 0.02 0.61 0.08
Logistic regression 0.65 0.44 0.27 0.25 1.00 0.00 0.90 0.06 0.93 0.02 0.70 0.12
CARTa 0.65 0.44 0.26 0.24 1.00 0.00 0.91 0.07 0.93 0.02 0.69 0.12
Bayesian Networks 0.66 0.43 0.26 0.24 1.00 0.00 0.90 0.09 0.93 0.02 0.70 0.12
  1. aCART Classification and Regression Trees, PPV Positive Predictive Values, NPV Negative Predictive Values, AUC Area Under the ROC (Receiver Operating Characteristic) Curve, STD Standard Deviation