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)
farringtonFlexible- Improved Farrington algorithm of Noufaily et al. (2012)
rki- The system previously used at the Robert Koch Institute, Germany
bayes- A Bayesian predictive posterior approach, see Höhle (2007)
boda- Bayesian outbreak detection algorithm based on a Generalized Additive Model fitted with INLA, see Manitz and Höhle (2013)
hmm- A predictive 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)
stcd- (experimental) Point process based approach by Assuncao & Correa (2009)
surveillance. The paper describes three regression-oriented model frameworks capturing endemic and epidemic dynamics, which are illustrated by applications to infectious disease surveillance data:
twinstimfor geo-referenced event data (i.e., a spatio-temporal point pattern)
twinSIRfor the SI[R][S] event history of a fixed population
hhh4for multivariate time series of counts
algo.hhh- Held et al. (2005) and Paul et al. (2008)
algo.twins- Held et al. (2006)
hhh4- Paul and Held (2011)
twinSIR- continuous-time/discrete-space modelling as described in Höhle (2009). The
"epidata"class provides the associated data structure.
twinstim- continuous-time/continuous-space modelling as described in Meyer et al. (2012). The
"epidataCS"class holds the data, which mainly consist of the observed events and exogenous covariates on a space-time grid.
hhh4()(count data) and
twinstim()(individual-level data) frameworks with a view to modelling power-law decay of spatial interaction.
backprojNP- Non-parametric back-projection method of Becker et al. (1991) used in, e.g., Werber et al. (2013).
nowcast- Nowcasting using frequentist approaches described in Lawless (1994) as well as more flexible hierarchical Bayes approaches developed in Höhle and an der Heiden (2014).
bodaDelay- Delay adjusted outbreak detection synthesizing the
bodaalgorithms into a context where the surveillance reports have delays before arriving. See Salmon et al. (2015) for details.
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 download as a source tarball and a Windows binary.
You can easily install the current snapshot in R via
Currently, R-Forge does not offer binaries for MacOS X, but installation might succeed with the additional argument type="source" in the above call.
- Two recent manuscripts provide an overview as well as step-by-step instructions on what you can do with the package: Salmon et al. (2014) covers prospective monitoring whereas Meyer et al. (2014) covers spatio-temporal modelling.
- 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 vignettes 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, Epidemiology, Biostatistics and Prevention Institute (EBPI), University of Zurich, Switzerland
- Michaela Paul (previously: 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: L. Held, T. Correa, M. Hofmann, J. Manitz, D. Sabanés Bové, D. Schumacher, S. Steiner, M. Virtanen
- German Science Foundation (DFG, 2003-2006)
- Munich Center of Health Sciences (MC-Health, 2007-2010)
- Swiss National Science Foundation (SNSF, 2007-2010, projects #116776 and #124429)
- Swiss National Science Foundation (SNSF, 2012-2015, project #137919)
- Robert Koch Institute, Berlin, Germany (2012-2015, Ph.D. project 'Modern surveillance algorithms for public health monitoring')