Kuang et al. BMC Public Health 2012, 12:418 http://www.biomedcentral.com/1471-2458/12/418 Table 4 Variable coding in multiple linear regression Variable Dependent Variable Sensitivity or Timeliness Independent Variable Algorithm Incubation Baseline counts Outbreak magnitude Variable coding Actual value 1 CUSUM 2 MPM 3 EWMA 1 Short 2 Medium 3 Long Actual value 0.5 1.0 2.0 3.0 Variable Algorithms CUSUM MPM EWMA(reference ) Incubation Short Median Long(reference ) Baseline counts Outbreak magnitude R2 = 0.28 F =73.29 β* standard regression coefficient. β 0.10 −0.76 −2.80 −2.42 0.61 −0.46 Table 6 Summary of Multiple Regression Analysis for Variables Predicting Timeliness SE 0.13 0.13 0.18 0.16 0.11 0.05 β* 0.02 −0.17 −0.57 −0.56 0.14 −0.23 Page 7 of 9 P-value 0.420 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 performance with disease incubation, baseline counts and outbreak magnitude. In algorithms’ detection performance, we found MPM had better performance than the EWMA, CUSUM methods. In theory, MPM method is simple, with fewer parameters and without the limit of the overall distribu- tion of monitoring data. The results showed that the performance of MPM was stable under different test conditions, which indicated that the MPM method has a broad scope of application. These advantages prompted that this method should be first considered when design- ing an automatic disease surveillance system. Consistent with previous evaluations of outbreak de- tection algorithms [5,34-36], the multiple linear regres- sion results found that the ability to detect outbreaks Table 5 Summary of Multiple Regression Analysis for Variables Predicting Sensitivity Variable Algorithms CUSUM MPM Incubation Short Medium EWMA(reference ) Long(reference ) Baseline counts Outbreak magnitude R2 = 0.39 F =125.85 β* standard regression coefficient. β 2.70 13.69 −9.68 −6.97 −12.01 16.11 SE 1.74 1.74 2.47 2.21 1.38 0.66 β* 0.04 0.21 −0.13 −0.11 −0.20 0.55 P-value 0.121 <0.001 <0.001 0.002 <0.001 <0.001 <0.001 was better with lower baseline counts and larger magni- tude. While previous studies inserted the actual counts with fixed outbreak case numbers, our study inserted the actual counts with outbreak case numbers based on the proportion of case distribution of simulated outbreaks. Our study examined how different incubation periods affect the detection performance. There are three indica- tors(minimum, maximum and average of incubation period) to describe incubation, and these three indica- tors are closely related. To date there is still no definitely way of classifying the incubation period with these three indicators. So we tried the K-means clustering method to classify eight types of disease into three categories. The regression results showed that diseases of long incu- bation period had a higher sensitivity, but needed more time to detect than those of short and medium incuba- tion period. Generally, the outbreaks of short incubation diseases occur ferociously and transiently, which are more easily to be missed by algorithms. The outbreaks of long incubation diseases, however, occur with longer duration, and can be detected by algorithms more accur- ately. As the early detection of outbreaks is important, additional work is still needed for timely detection long incubation diseases. The biggest challenge for the evaluation of outbreak detection algorithms is to obtain a sufficiently large number of outbreak data with which to measure sensi- tivity and timeliness [33]. Injecting geometric shaped spikes into real surveillance data is a feasible approach [5,36-39]. In this study, in addition to inserting litera- tures-based simulated outbreak, we also used the public health emergency data. This method provided a solution on the issues where the simulated data may completely detach from real outbreaks and outbreaks in the real