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

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Höhle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. 'hhh4' estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. 'twinSIR' models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Höhle (2009) <doi:10.1002/bimj.200900050>. 'twinstim' estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2015) <>.

Version: 1.12.1
Depends: R (≥ 3.2.0), methods, grDevices, graphics, stats, utils, sp (≥ 1.0-15), xtable (≥ 1.7-0), polyCub (≥ 0.4-3)
Imports: Rcpp (≥ 0.11.0), MASS, Matrix, nlme, 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 (≥ 0.11.0), coda, splancs, gamlss, INLA (≥ 0.0-1458166556), runjags, ggplot2, MGLM, knitr
Published: 2016-05-18
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: algo.glrnb: Count data regression charts using the generalized likelihood ratio statistic
hhh4: An endemic-epidemic modelling framework for infectious disease counts
Monitoring count time series in R: Aberration detection in public health surveillance
Getting started with outbreak detection
hhh4 (spatio-temporal): Endemic-epidemic modeling of areal count time series
twinSIR: Individual-level epidemic modeling for a fixed population with known distances
twinstim: An endemic-epidemic modeling framework for spatio-temporal point patterns
Package source: surveillance_1.12.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: surveillance_1.12.1.tgz, r-oldrel: surveillance_1.12.1.tgz
Old sources: surveillance archive

Reverse dependencies:

Reverse suggests: tscount


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