Can epidemic detection systems at the hospital level complement regional surveillance networks: Case study with the influenza epidemic?

Table 1 Performance of different detection algorithms with sensitivity of 100% to detect influenza (UrgIndex-hospitalisation and ICD10-consultations)

Time series	Detection algorithms	Outbreaks periods	Non-epidemic periods
		Mean timeliness (days)	Number of days with false alarms N = 1094	Specificity (%)
UrgIndex - hospitalisations	C2, k = 0.08, 1d	−7.5	327	70.1
	C2, k = 0.08, 3d	−3.3	226	79.3
	C2, k = 0.1, 1d	−15.3	247	77.4
	C2, k = 0.1, 3d	5.0	155	85.8
	C3, k = 0.08, 1d	−58.3	817	25.3
	C3, k = 0.08, 3d	20.3	739	32.4
	C3, k = 0.08, 5d	−13.3	589	46.2
	C3, k = 0.1, 1d	18.3	745	31.9
	C3, k = 0.1, 3d	−18.3	658	39.9
	C3, k = 0.1, 5d	−13.3	589	46.2
	C3, k = 0.5, 1d	0.5	230	79.0
	C3, k = 1, 1d	5.5	78	92.9
ICD10 – consultations	C1, k = 0.07, 1d	−18.3	151	86.2
	C1, k = 0.07, 3d	−11.5	127	88.6
	C1, k = 0.07, 5d	−7.8	115	89.5
	C1, k = 0.1, 1d	−18.3	148	86.5
	C1, k = 0.1, 3d	−7.8	122	88.4
	C2, k = 0.07, 1d	−19.8	147	86.6
	C2, k = 0.07, 3d	0.5	135	87.7
	C2, k = 0.07, 5d	2.5	125	88.6
	C2, k = 0.1, 1d	−13.5	143	86.9
	C2, k = 0.1, 3d	0.8	131	88.0
	C2, k = 0.1, 5d	2.8	121	88.9
	C3, k = 0.07, 1d	−32.3	172	84.3
	C3, k = 0.1, 1d	−32.3	168	84.6
	C3, k = 0.1, 3d	−15.8	151	86.2
	C3, k = 0.1, 5d	−11.3	154	87.0
	C3, k = 0.5, 1d	9.8	74	93.2

C₁, C₂, and C₃ refer to the three different moving average calculations of CUSUM statistics (C₁-mild, C₂-medium, C₃-ultra).
k is the detectable difference to the mean used to the calculation of CUSUM statistics.
Negative mean timeliness: first day signal before the outbreaks beginning, on average.
Positive mean timeliness: first day signal after the outbreaks beginning, on average.

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