Using decision fusion methods to improve outbreak detection in disease surveillance

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

	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
CART^a	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

^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

ISSN: 1472-6947