Abstract
BackgroundOutbreak detection algorithms play an important role in effective automated surveillance. Although many algorithms have been designed to improve the performance of outbreak detection, few published studies have examined how epidemic features of infectious disease impact on the detection performance of algorithms. This study compared the performance of three outbreak detection algorithms stratified by epidemic features of infectious disease and examined the relationship between epidemic features and performance of outbreak detection algorithms.MethodsExponentially weighted moving average (EWMA), cumulative sum (CUSUM) and moving percentile method (MPM) algorithms were applied. We inserted simulated outbreaks into notifiable infectious disease data in China Infectious Disease Automated-alert and Response System (CIDARS), and compared the performance of the three algorithms with optimized parameters at a fixed false alarm rate of 5% classified by epidemic features of infectious disease. Multiple linear regression was adopted to analyse the relationship of the algorithms’ sensitivity and timeliness with the epidemic features of infectious diseases.ResultsThe MPM had better detection performance than EWMA and CUSUM through all simulated outbreaks, with or without stratification by epidemic features (incubation period, baseline counts and outbreak magnitude). The epidemic features were associated with both sensitivity and timeliness. Compared with long incubation, short incubation had lower probability (β* = −0.13, P < 0.001) but needed shorter time to detect outbreaks (β* = −0.57, P < 0.001). Lower baseline counts were associated with higher probability (β* = −0.20, P < 0.001) and longer time (β* = 0.14, P < 0.001). The larger outbreak magnitude was correlated with higher probability (β* = 0.55, P < 0.001) and shorter time (β* = −0.23, P < 0.001).ConclusionsThe results of this study suggest that the MPM is a prior algorithm for outbreak detection and differences of epidemic features in detection performance should be considered in automatic surveillance practice.
Highlights
Outbreak detection algorithms play an important role in effective automated surveillance
At the end of 2010, analysis results of the operation of China Infectious Disease Automated-alert and Response System (CIDARS) in nationwide showed that a large number of outbreaks of infectious diseases could be timely detected, but it was found that there were many of false-positive signals; large differences existed between outbreak signal counts and final identified outbreaks in different diseases; the detection performance was poor in those diseases which had more case reports and fewer outbreaks [4]
Our study aimed to explore the influence of epidemic features on algorithms’ detection performance
Summary
Outbreak detection algorithms play an important role in effective automated surveillance. At the end of 2010, analysis results of the operation of CIDARS in nationwide showed that a large number of outbreaks of infectious diseases could be timely detected, but it was found that there were many of false-positive signals; large differences existed between outbreak signal counts and final identified outbreaks in different diseases; the detection performance was poor in those diseases which had more case reports and fewer outbreaks [4] These issues prompted us that epidemic features of infectious disease may affect outbreak detection performance. Several studies have described the determinants of outbreak detection performance, including: system factors (representativeness, outbreak detection algorithms and algorithm-specifics), outbreak characteristics (outbreak size, shape of the outbreak signal and time of the outbreak) [5,6,7,8] Understanding the differences these determinants make in detection performance can help public health practitioners improve the automated surveillance system, raising detection capabilities. Extensive researches have explored novel algorithms to improve the performance of outbreak detection [9,10,11,12,13], but evidence on how epidemic features impact on detection performance is still limited
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