surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Implementation of statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g., discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models, or continuous-space point process data such as the occurrence of infectious diseases. Main focus is on outbreak detection in count data time series originating from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences. Currently, the package contains implementations of many typical outbreak detection procedures such as Farrington et al (1996), Noufaily et al (2012) or the negative binomial LR-CUSUM method described in Höhle and Paul (2008). A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Furthermore, inference methods for the retrospective infectious disease models in Held et al (2005), Held et al (2006), Paul et al (2008), Paul and Held (2011), Held and Paul (2012), and Meyer and Held (2014) are provided. Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in Höhle (2009) ('twinSIR', discrete space) and Meyer et al (2012) ('twinstim', continuous space). The package contains several real-world data sets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion. Note: Using the 'boda' algorithm requires the 'INLA' package, which should be installed automatically through the specified Additional_repositories, if uninstalled dependencies are also requested. As this might not work under OS X it might be necessary to manually install the 'INLA' package as specified at <>.

Version: 1.10-0
Depends: R (≥ 3.0.2), methods, grDevices, graphics, stats, utils, sp (≥ 1.0-15), xtable, polyCub (≥ 0.4-3)
Imports: Rcpp (≥ 0.11.0), MASS, Matrix, spatstat (≥ 1.36-0)
LinkingTo: Rcpp
Suggests: parallel, grid, xts, gridExtra, lattice, colorspace, scales, animation, msm, spc, quadprog, memoise, polyclip, rgeos, gpclib, maptools, intervals, spdep, numDeriv, maxLik, gsl, testthat, coda, splancs, gamlss, INLA, runjags
Published: 2015-11-06
Author: Michael Höhle [aut, ths], Sebastian Meyer [aut, cre], Michaela Paul [aut], Leonhard Held [ctb, ths], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Sabanés Bové [ctb], Maëlle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R)
Maintainer: Sebastian Meyer <Sebastian.Meyer at>
License: GPL-2
NeedsCompilation: yes
Citation: surveillance citation info
Materials: NEWS
In views: Environmetrics, SpatioTemporal, TimeSeries
CRAN checks: surveillance results


Reference manual: surveillance.pdf
Vignettes: Additional documentation of the function algo.glrnb
Additional documentation of the function hhh4
Getting started with the package
Package source: surveillance_1.10-0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: surveillance_1.9-1.tgz, r-oldrel: surveillance_1.8-3.tgz
OS X Mavericks binaries: r-release: surveillance_1.10-0.tgz
Old sources: surveillance archive