Outbreak detection algorithms for seasonal disease data: a case study using Ross River virus disease.

ABSTRACT:

Background

Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens.

Methods

We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-ad

SUBMITTER: Pelecanos AM 

PROVIDER: S-EPMC3004813 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature