Review of methods for space-time disease surveillance

Abstract

A review of some methods for analysis of space-time disease surveillance data is presented. Increasingly, surveillance systems are capturing spatial and temporal data on disease and health outcomes in a variety of public health contexts. A vast and growing suite of methods exists for detection of outbreaks and trends in surveillance data and the selection of appropriate methods in a given surveillance context is not always clear. While most reviews of methods focus on algorithm performance, in practice, a variety of factors determine what methods are appropriate for surveillance. In this review, we focus on the role of contextual factors such as scale, scope, surveillance objective, disease characteristics, and technical issues in relation to commonly used approaches to surveillance. Methods are classified as testing-based or model-based approaches. Reviewing methods in the context of factors other than algorithm performance highlights important aspects of implementing and selecting appropriate disease surveillance methods.

Fig. 1

Methods for prospective surveillance. (A) Parallel surveillance where a test statistic is computed separated for each region under surveillance and each assessed individually. (B) Vector accumulation where test statistics in a parallel setting are combined to form one alarm statistic which is evaluated. (C) Scalar accumulation where on statistic is computed over all regions under surveillance and evaluated.