Using decision fusion methods to improve outbreak detection in disease surveillance

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

	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
CART^a	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

^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

ISSN: 1472-6947