Biology Reference
In-Depth Information
12.1 Introduction
The objective of biosurveillance in this chapter is the detection of emerg-
ing incidence clusters in time of a health-related event. Reviews on temporal
surveillance can be found, for example, in Sonesson and Bock (2003), Bravata
et al. (2004), Buckeridge et al. (2005), and Tennant et al. (2007). In recent years,
a pleasant development has been a synthesis of surveillance methods with
methods from statistical process control (see, for example, Woodall (2006) for
a survey).
One important aspect to ensure a transfer of methodological developments
into practice is the availability of appropriate software implementations
and their documentation. With the present chapter, we want to introduce
one such open-source software implementation into a public health con-
text: the R package
surveillance
. In order to demonstrate functionality,
we use Danish mortality data from the ongoing European monitoring of
excess mortality for public health action (EuroMOMO) project (Anonymous,
2009).
The R system is a free software environment for statistical computing and
graphics distributed under a GNU-style copyleft license and running under
Unix, Windows, and Mac (R Development Core Team, 2009). Several docu-
ments and topics provide an introduction, such as Dalgaard (2008), Venables
et al. (2009), and Muenchen (2009). The add-on package
surveillance
of fers
functionality for the visualization, monitoring, and simulation of count data
time series in R for public health surveillance and biosurveillance. It pro-
vides an implementation of different aberration detection algorithms for epi-
demiologists and an infrastructure for developers of new algorithms. The
package is freely available under the GNU GPL license and obtainable from
the Comprehensive R Archive Network (CRAN). To install the package from
CRAN, the following call in R has to be performed once:
R> install.packages ("surveillance")
After installation, the package is loaded using:
R> library ("surveillance")
The focus in the present chapter is on using the aberration detection algo-
rithms in the package for univariate count data time series, but the package
also contains example outbreak data from the German SurvStat@RKI data-
base (Robert Koch Institute, 2009) functionality for the simulation of outbreak
data, and the comparison of algorithms. Höhle (2007) provides basic infor-
mation about the package; further information can be found at the package
homepage located at
http://surveillance.r-forge.r-project.org/.
The present text introduces a number of new developments in the package,
