Table 1.
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.