Disease surveillance using a hidden Markov model.

ABSTRACT:

Background

Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.

Methods

A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms an

SUBMITTER: Watkins RE 

PROVIDER: S-EPMC2735038 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature