BEST PRACTICES - SPACE-TIME DISEASE SURVEILLANCE TOOLS

R-DCluster

Features

Spatial scanning
Besag & Newell test
Geographical Analysis Machine

General spatial clustering methods
Bootstrapping for sampling distributions (four different models)

R based graphing and mapping
Stone's focused test

Comments

The DCluster Package offers spatial cluster detection methods within R. This means data can be easily imported and translated between different spatial R packages. The focus of the DCluster package is testing methods for spatial clustering. There are no methods for space-time analysis and the documentation within R is limited for those not accustomed to R or the methods themselves. Implementation in R also means that functions can be coupled with other statistical methods in other packages and easily automated.

Selected case studies

We were unable to find examples of the Surveillance package as part of a surveillance system or space-time analysis.

Surveillance Objective(s)

Spatial cluster detection, Data exploration

User Expertise

Users need to be familiar with spatial cluster detection tests and the use of R statistical programming software.

Key Considerations

Main R package for spatial cluster detection
No space-time analysis
Flexibility of other R functions and graphing capabilities

More Information

Website: http://cran.r-project.org/web/packages/DCluster/index.html

Key Resource: Gomez-Rubio, V., Ferrandiz-Ferragud, J. & Lopez-Quilez, A. 2005. Detecting clusters of disease with R. Journal of Geographical Systems 7, no. 2: 189-206.