Recursive least squares background prediction of univariate syndromic surveillance data.

ABSTRACT:

Background

Surveillance of univariate syndromic data as a means of potential indicator of developing public health conditions has been used extensively. This paper aims to improve the performance of detecting outbreaks by using a background forecasting algorithm based on the adaptive recursive least squares method combined with a novel treatment of the Day of the Week effect.

Methods

Previous work by the first author has suggested that univariate recursive least squares analysis of syndromic data can be used to characterize the background upon which a prediction and detection component of a biosurvellance system may be built. An adaptive implementation is used to deal with data non-stationarity. In this paper we develop and implement the RLS method for background estimation

SUBMITTER: Najmi AH 

PROVIDER: S-EPMC2639573 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature