Comparison of statistical methods for the early detection of disease outbreaks in small population settings.

ABSTRACT:

Objectives

This study examines the performance of 6 aberration detection algorithms for the early detection of disease outbreaks in small population settings using syndrome-based early warning surveillance data collected by the Pacific Syndromic Surveillance System (PSSS). Although previous studies have proposed statistical methods for detecting aberrations in larger datasets, there is limited knowledge about how these perform in the presence of small numbers of background cases.

Methods

To address this gap a simulation model was developed to test and compare the performance of the 6 algorithms in detecting outbreaks of different magnitudes, durations, and case distributions.

Results

The study found that while the Early Aberration Reporting System-C1 algorithm develop

SUBMITTER: Craig AT 

PROVIDER: S-EPMC10482728 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature