Epidemic features affecting the performance of outbreak detection algorithms.

ABSTRACT:

Background

Outbreak 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.

Methods

Exponentially 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 Infect

SUBMITTER: Kuang J 

PROVIDER: S-EPMC3489582 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature