Comparison of statistical algorithms for daily syndromic surveillance aberration detection.

ABSTRACT: MOTIVATION:Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the 'rising activity, multilevel mixed effects, indicator emphasis' (RAMMIE) method and the improved quasi-Poisson regression-based method known as 'Farrington Flexible' both currently used at Public Health England, and the 'Early Aberration Reporting System' (EARS) method used at the US Cen

SUBMITTER: Noufaily A 

PROVIDER: S-EPMC6736430 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature