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Table 2 Performance of different detection algorithms with sensitivity of 100% to detect influenza (UrgIndex-hospitalisation and ICD10-consultations), excluding data from 2009-S19 to 2010-S18

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

Time series Detection algorithms Outbreaks periods Non-epidemic periods
   Mean timeliness (days) Number of days with false alarms N = 793 Specificity (%)
UrgIndex - hospitalisations C2, k = 0.08, 1d 3.7 149 81.2
C2, k = 0.08, 3d 8.7 75 90.5
C2, k = 0.1, 1d 3.7 120 84.9
C2, k = 0.1, 3d 14.3 56 92.9
C3, k = 0.08, 1d −10.7 560 29.4
C3, k = 0.08, 3d −8.7 497 37.3
C3, k = 0.08, 5d −6.7 446 43.8
C3, k = 0.1, 1d −8.3 511 35.6
C3, k = 0.1, 3d −6.3 440 44.5
C3, k = 0.1, 5d −0.3 384 51.6
C3, k = 0.5, 1d 4.0 139 82.5
C3, k = 0.5, 3d 6.0 78 90.2
C3, k = 1, 1d 4.0 41 94.8
ICD10 – consultations C1, k = 0.07, 1d 1.0 37 95.3
C1, k = 0.07, 3d 5.0 23 97.1
C1, k = 0.07, 5d 12.3 15 98.1
C1, k = 0.1, 1d 1.0 36 95.5
C1, k = 0.1, 3d 5.0 22 97.2
C1, k = 0.1, 5d 12.3 14 98.2
C2, k = 0.07, 1d −1.7 34 95.7
C2, k = 0.07, 3d 24.7 26 96.7
C2, k = 0.07, 5d 26.7 18 97.7
C2, k = 0.1, 1d 6.7 30 96.2
C2, k = 0.1, 3d 25.0 22 97.2
C2, k = 0.1, 5d 27.0 14 98.2
C3, k = 0.07, 1d −8.0 48 93.9
C3, k = 0.07, 3d 3.0 40 95.0
C3, k = 0.07, 5d 5.0 35 95.6
C3, k = 0.1, 1d −8.0 46 94.2
C3, k = 0.1, 3d 3.0 36 95.5
C3, k = 0.1, 5d 5.0 31 96.1
  C3, k = 0.5, 1d 8.3 26 96.7
  1. C1, C2, and C3 refer to the three different moving average calculations of CUSUM statistics (C1-mild, C2-medium, C3-ultra).
  2. k is the detectable difference to the mean used to the calculation of CUSUM statistics.
  3. Negative mean timeliness: first day signal before the outbreaks beginning, on average.
  4. Positive mean timeliness: first day signal after the outbreaks beginning, on average.