surveillance (version 1.4-1)

surveillance-package: Outbreak detection algorithms for surveillance data

Description

A package implementing statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data. Focus is on the modelling and change-point detection in count data time series or point-processes originating from public health surveillance of infectious diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences.

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latin1

Details

ll{ Package: surveillance Type: Package Version: 1.1-0 Date: 2009-10-14 License: GPL version 2 (http://www.gnu.org/licenses/gpl.html) }

surveillance is an Rpackage implementing statistical methods for the retrospective modeling and prospective change-point detection in time series of counts, proportions and categorical data. The main application is in the detection of aberrations in routine collected public health data seen as univariate and multivariate time series of counts or point-processes. However, applications could just as well originate from environmetrics, econometrics or social sciences. As many methods rely on statistical process control methodology, the package is thus also relevant to quality control and reliability engineering.

The fundamental data structure of the package is an S4 class sts wrapping observations, monitoring results and date handling for multivariate time series. Currently the package contains implementations typical outbreak detection procedures such as Stroup et al. (1989), Farrington et al., (1996), Rossi et al. (1999), Rogerson and Yamada (2001), a Bayesian approach (H�hle{Hoehle}, 2007), negative binomial CUSUM methods (H�hle{Hoehle} and Mazick, 2009), and a detector based on generalized likelihood ratios (H�hle{Hoehle} and Paul, 2008). However, also CUSUMs for the prospective change-point detection in binomial, beta-binomial and multinomial time series is covered based on generalized linear modelling. This includes e.g. paired binary CUSUM described by Steiner et al. (1999) or paired comparison Bradley-Terry modelling described in H�hle{Hoehle} (2010). The package contains several real-world datasets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion.

Furthermore, inference methods for the retrospective infectious disease model in Held et al. (2005), Paul et al. (2008) ("algo.hhh") and Paul and Held (2011) ("hhh4") handling multivariate time series of counts. Furthermore, the fully Bayesian approach for univariate time series of counts from Held et al. (2006) ("twins") is also implemented Self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in H�hle (2009) ("twinSIR") and Meyer et al (2012) ("twinstim").

Altogether, the package contains several real-world datasets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion.

References

See citation(package="surveillance").

Examples

#Code from an early survey article about the package: Hoehle (2007)
#available from http://surveillance.r-forge.r-project.org/
demo(cost)
#Code from a more recent book chapter about using the package for the
#monitoring of Danish mortality data (Hoehle, 2009).
demo(biosurvbook)