|
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
|
- 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.
