Kuang et al. BMC Public Health 2012, 12:418 http://www.biomedcentral.com/1471-2458/12/418 Page 6 of 9 Figure 6 Sensitivity versus timeliness for three detection algorithms in three baseline counts levels. EWMA, exponentially weighted moving average; CUSUM, cumulative sum; MPM, moving percentile method. and EWMA within all days from the start of the out- break; the CUSUM method had a better sensitivity than EWMA in 2.0,3.0 magnitude (Figure 7). Multiple linear regression The variable coding in multiple linear regression (Table 4). The results of multiple linear regression showed that algorithms, incubation period, baseline counts and out- break magnitude were associated with both sensitivity (Table 5) and timeliness (Table 6). As it was illustrated in Table 5, the algorithms, incuba- tion period, baseline counts and outbreak magnitude had a statistically significant relationship with sensitivity. MPM had a higher probability of detecting outbreaks compared with EWMA (β* = 0.21, P < 0.001). Short (β* = −0.13, P < 0.001) and Medium(β* = −0.11, P = 0.002) incubation had a lower probability of detecting the out- breaks compared with long incubation. The lower the baseline counts, the higher probability (β* = −0.20, P < 0.001). The larger outbreak magnitude, the higher probability (β* = 0.55, P < 0.001). The algorithms, incubation period, baseline counts and outbreak magnitude had a statistically significant re- lationship with timeliness. MPM needed shorter time to detect the outbreaks compared with EWMA(β* = −0.17, P < 0.001). Short(β* = −0.57, P < 0.001) and Medium (β* = −0.56, P < 0.001) incubation needed shorter time to detect the outbreaks compared with long incubation. The higher baseline counts, the longer time to detect the outbreaks (β* = 0.14, P < 0.001). The larger outbreak magnitude, the shorter time (β* = −0.23, P < 0.001) (Table 6). Discussion Many determinants affect the performance of outbreak detection in automated surveillance, and knowing about how these factors influence the detection performance can help to improve automated surveillance system. In this study, we compared the performance of three out- break detection methods by adding simulated outbreaks to actual daily counts of notifiable infectious diseases in CIDARS and examined the relationship of the detection Figure 7 Sensitivity versus timeliness for three detection algorithms in four outbreak magnitudes. EWMA, exponentially weighted moving average; CUSUM, cumulative sum; MPM, moving percentile method.