Kuang et al. BMC Public Health 2012, 12:418 http://www.biomedcentral.com/1471-2458/12/418 diseases; the detection performance was poor in those diseases which had more case reports and fewer out- breaks [4]. These issues prompted us that epidemic fea- tures of infectious disease may affect outbreak detection performance. Several studies have described the determinants of outbreak detection performance, including: system fac- tors (representativeness, outbreak detection algorithms and algorithm-specifics), outbreak characteristics (out- break size, shape of the outbreak signal and time of the outbreak) [5-8]. Understanding the differences these determinants make in detection performance can help public health practitioners improve the automated sur- veillance system, thus raising detection capabilities. Re- cently, extensive researches have explored novel algorithms to improve the performance of outbreak de- tection [9-13], but evidence on how epidemic features impact on detection performance is still limited. To address this limitation, our study aimed to explore the influence of epidemic features (incubation period, baseline counts and outbreak magnitude) on algorithms’ detection performance. Findings of this study may help public health surveillance practitioners understand the detection performance of algorithms under these epi- demic features and improve the implementation of auto- mated surveillance. Methods Figure 1 presents the flow of data processing in this study. First, we selected eight notifiable infectious dis- eases in CIDARS for studying. Second, we sampled ten counties for each infectious disease. Third, we exacted the surveillance data sequences in 2005–2009 from CIDARS. Fourth, we marked the public health emergen- cies and injected simulated outbreaks in data sequences. Page 2 of 9 Fifth, we ran the three outbreak detection algorithms. Sixth, we computed the sensitivity and timeliness of the three outbreak detection algorithms. Finally, statistical inference and multiple linear regression were used to compare the performance and examine the relationship of sensitivity and timeliness with epidemic features. Data sources The infectious disease data were extracted from CIDARS. CIDARS was developed basing on the existing data from National Disease Surveillance Reporting and Management System on 28 diseases that are outbreak- prone and require prompt action. The 28 diseases were classified into two types according to severity, incidence rate and importance [3]. Type 1 diseases includes nine infectious diseases characterized with higher severity but lower incidence and are analysed using fixed-threshold detection method. For type 2 diseases (19 more common infectious diseases) , we selected eight diseases (dysen- tery, scarlet fever, mumps, measles, malaria, typhoid, en- cephalitis B and hepatitis A) which represented three routes of transmission(respiratory, oral-fecal and vector- borne). Five provinces were sampled for eight diseases (dysentery in Hunan, scarlet fever, measles in Xinjiang, mumps in Chongqing, malaria, typhoid, encephalitis B in Yunnan, hepatitis A in Guizhou), where the respective disease had high incidence and became important local public health problems. Then we randomly sampled 10 counties from the selected provinces for each disease, and obtained their actual daily number of reported cases in 2005–2009. Data from 2005 to 2007 were used as baseline, while data from 2008 to 2009 were used to evaluate the algorithms. Strategy of inserting simulated outbreaks was used to evaluate the detection performance. To prevent public Figure 1 The flowchart of data processing.