Kuang et al. BMC Public Health 2012, 12:418 http://www.biomedcentral.com/1471-2458/12/418 health emergency confounding the evaluation, we got the records of public health emergency in the corre- sponding counties from Emergency Public Reporting System [14]. Outbreak detection algorithms To date many outbreak detection algorithms can be used for temporal data [15-19]. Considering that we collected five years data, using the same periods’ historical data as baseline is appropriate, to some extent, can reduce the seasonal and day-of-week variation in the baseline. How- ever, for using this, the regression and ARIMA models may subject to certain restrictions, as the steps in their processing require recent continuous time interval to calculate expected statistic. So we chose two most com- monly used statistical process control algorithms (EWMA, CUSUM) and a non-parameter algorithm (MPM) which enable the application of the same peri- ods’ historical data in theory. For EWMA, the current time-series value is replaced by a weighted average of the recent values. If the observed values were assumed for Poisson distribution, The smoothed daily count was calculated as and the upper control limit(UCL) was calculated as Zt ¼ λXt þ ð1 λÞ Zt 1 UCL ¼μþ kσ rﬃﬃﬃﬃﬃλﬃﬃﬃﬃﬃﬃ 2 λ In the algorithm, the λ (0 < λ < 1) was the weighting factor, k was the control limit coefficient [20]. Cumulative Sum (CUSUM) has been used to detect outbreaks of infectious diseases in recent decades [21]. Two parameters k and h are involved. k refers to the pre-specified reference value and h the decision bound- ary. Here, μt is the sample mean and σt the standard de- viation of the reference values. When we denote xt the count per day, then the CUSUM statistic is defined as St ¼ maxð0; St 1 þ ðxt ðμt þ kσtÞ=σtÞÞ If the CUSUM statistic is larger than h, then the current day is considered as a possible outbreak. The MPM uses previous several years (such as 3– 5 years) over the same period as baseline data, setting a percentile of baseline data as a detection parameter c. If the current day counts xt is greater than the detection parameter's corresponding percentile (Pc), outbreak sig- nal is produced [22]. Algorithm parameters To obtain the optimized parameter values of the three algorithms, we used R software [23] generating two-year Poisson distribution sequences with five daily average Page 3 of 9 Baseline Data Current Day Figure 2 Example of baseline data for EWMA, CUSUM, and MPM. counts levels (0.1, 0.5, 1.0, 2.0, 5.0). We set a fixed false alarm rate of 0.05 (an average of one false alarm every twenty days) by applying each algorithm to these five sequences without any added outbreak signals, and de- termining the parameters that would yield an average of one false alert every twenty days (see Additional file 1: Table S1). Baseline data We used baseline data from 2005 to 2007, in which we use the corresponding day, the seven previous days and the seven later days. These summed up to a total of 45 days (Figure 2). The EWMA algorithm was associated with the order day of baseline data (Figure 3). Outbreak simulation and insertion We generated outbreak signals by simulation. We selected real outbreak events reported in the literatures for each disease [24-31], and calculated the proportion of daily case distribution (Figure 4). Next, we simulated four outbreak magnitudes (0.5, 1.0, 2.0, 3.0), which meant that the increase cases of outbreak signals are the corresponding multiple of the baseline number of cases. As the baselines were required to use the same period of history (seven days before and after the current day), to increase the stability of the calculation, we made 10th-day the insertion date of outbreak signals every month. If the intervals had public health emergency, then skipped the insertion. As the outbreak duration of Baseline Data Current Day Figure 3 Example of the order number of baseline data for EWMA.