surveillance-package {surveillance} | R Documentation |
A package implementing statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data. Focus is on outbreak detection in count data time series originating from public health surveillance of infectious diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences.
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 R package 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, but 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 (Höhle, 2007),
negative binomial CUSUM methods (Höhle and Mazick, 2009), and a
detector based on generalized likelihood ratios (Höhle
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 Höhle
(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) and Paul et al. (2008) handling multivariate time series of counts. Finally, the fully Bayesian approach for univariate time series of counts from Held et al. (2006) is also implemented.
Author: M. Höhle with contributions from T. Correa, M. Hofmann, C. Lang, M. Paul, A. Riebler, S. Steiner and V. Wimmer
Maintainer: Michael Höhle <hoehle@stat.uni-muenchen.de>
surveillance: An R package for the surveillance of infectious diseases (2007), M. Höhle, Computational Statistics, 22(4), pp. 571—582.
#Code from an early survey article about the package: Hoehle (2007) #available from http://surveillance.r-forge.r-project.org/ ## Not run: demo(cost) #Code from a more recent book chapter about using the package for the #monitoring of Danish mortality data (Hoehle, 2009). ## Not run: demo(biosurvbook)