The R-package 'surveillance' is a framework for the development of statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data as well as continuous-time epidemic like point process phenomena.
cdc- Stroup et al. (1989)
farrington- Farrington et al. (1996)
rki- The system used at the Robert Koch Institute, Germany
bayes- A Bayesian predictive posterior approach, see Höhle (2007)
hmm- An online version of the Hidden Markov Model approach by Le Strat and Carrat (1999)
rogerson- Surveillance for time varying Poisson means as documented in Rogerson and Yamada (2004).
cusum- An approximate CUSUM method for time varying Poisson means as documented in Rossi et al (1999)
glrnb- Likelihood and generalized likelihood ratio detectors for time varying Poisson and negative binomial distributed series documented in Höhle and Paul (2008).
outbreakP- Semiparametric surveillance of outbreaks by Frisén and Andersson (2009)
categoricalCUSUM- includes change-point detection based on regression models for binomial and beta-binomial distributed response. Furthermore, multi-categorical models includes the multinomial logistic model, proportional odds model and the Bradley-Terry models, see Höhle (2010).
pairedbinCUSUM- paired-binary approach taken in Steiner et al. (1999)
algo.hhh- Held et al. (2005) and Paul et al. (2008)
hhh4- Paul and Held (2011)
algo.twins- Held et al. (2006)
twinSIR- continuous-time and discrete-space modelling as described in Höhle (2009). The
epidataclass provides the appropriate data structure for such data.
twinstim- continuous-time and continuous-space modelling as desribed in Meyer et al. (2012). The
epidataCSdata provides a novel data class for point-referenced space-time data.
stcd- (experimental) Point process based approach by Assuncao & Correa (2009)
The surveillance package is available for download from CRAN.
Current package development, help-forum and bugtracking is hosted through R-Forge:
From this page snapshots of the current development version are available. For example -- if you are running a recent R version -- you can obtain a binary snapshot of the development version as
install.packages("surveillance",repos="http://r-forge.r-project.org").You can also manually download the binary snapshot or the source tarball.
- See NEWS file in the current distribution
- A good (but slightly outdated) introduction to the outbreak detection part of the package is provided in the paper surveillance: An R package for the surveillance of infectious diseases, Computational Statistics (2007), 22(4), pp. 571-582. [preprint]
- A more recent description can be found in the book chapter Aberration detection in R illustrated by Danish mortality monitoring (2010), M. Höhle and A. Mazick, To appear in T. Kass-Hout and X. Zhang (Eds.) Biosurveillance: A Health Protection Priority, CRC Press. [preprint]. Note: As ISO 8601 handling is not fully implemented in R on Windows the demo("biosurvbook") will only run with package version >= 1.2, where a workaround was implemented.
- An overview of statistical methods and implementational usage is given the course notes of several courses on the package, e.g. the course notes of the lecture Temporal and spatio-temporal modelling of infectious diseases at the Department of Statistics, University of Munich, Oct 10-13, 2011 or the shortcourse Statistical surveillance of infectious diseases held at the Department of Statistics, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil, Nov 27-28, 2008.
- Invited talk held at the 2008 ESCAIDE satellite workshop on
Computer supported outbreak detection and signal management(R-File, Data from SurvStat@RKI)
- Application of the package in veterinary public health surveillance is described in Statistical approaches to the surveillance of infectious diseases for veterinary public health [preprint].
- Read the package vignette or look here for further preprints.
- Sometimes pictures says more than 1000 words:
- Michael Höhle, Department of Mathematics, Stockholm University, Sweden (Project Admin)
- Sebastian Meyer, Institute of Social and Preventive Medicine, University of Zurich, Switzerland
- Michaela Paul, Institute of Social and Preventive Medicine, University of Zurich, Switzerland
- Maëlle Salmon, Department for Infectious Disease Epidemiology, Robert Koch Institute, Germany
- Former student programmers: C. Lang, A. Riebler, V. Wimmer
- Contributions by: T. Correa, L. Held, M. Hofmann, D. Sabanés Bové, S. Steiner, M. Virtanen
The development of models and algorithms implemented in surveillance was financially supported by :
- Munich Center of Health Sciences (MC-Health, 2007-2010)
- Swiss National Science Foundation (SNF, since 2007)
- German Science Foundation (DFG, 2003-2006)