surveillance

1.26.0

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

4packages depend

23.4Kdownloads / year

35.8%test coverage

9/13checks pass

Overview

About

Maintained by Sebastian MeyerFirst published 2005-11-1890 releasesCRAN page ↗

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 Hoehle 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 Hoehle (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. (2017) doi:10.18637/jss.v077.i11.

Install

R (CRAN)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("surveillance", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-surveillance

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-surveillance

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-surveillance

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

4NOTE9OK

Failing flavors

NOTE r-devel-linux-x86_64-debian-clang
NOTE r-devel-linux-x86_64-debian-gcc
NOTE r-devel-linux-x86_64-fedora-gcc
NOTE r-devel-windows-x86_64

Slowest check: 14.5 min · r-devel-linux-x86_64-fedora-clang

Code health

Yes

Tests · ratio 0.07

35.8%

Coverage · measured lines

52%

Documentation · exports

Dependencies · direct

Check history

NOTE2026-07-16
11 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
OK2026-07-04
13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
ERROR2026-06-19
10 OK · 0 NOTE · 1 WARNING · 2 ERROR · 0 FAILURE
WARNING2026-06-14
12 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
OK2026-06-13
13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Show 5 earlier snapshots

ERROR2026-06-04
11 OK · 0 NOTE · 0 WARNING · 2 ERROR · 0 FAILURE
OK2026-04-29
13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
WARNING2026-04-28
12 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
OK2026-04-25
12 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
NOTE2026-03-10
11 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMEYes · 662 wordsVignettesYes · dynamicpkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo

Examples that run: 100%
Documented parameters: 86%
Return-value docs: 84%
References docs: 10%

Downloads

23.4K

CRAN downloads in the past year

Rank #3,339 · ~64/day · ~2K/mo

Releases R versions

Daily download trend is not available in this view yet.

1.4K30 days

3.6K90 days

23.4K1 year

Compare downloads with other packages →

Also on642 r2u39 autocran7.7K conda_forge104 c2d4u

Dependencies

Declared dependencies

30 external dependencies (excludes base and recommended)

Depends (7)

R >= 3.6.0methodsgrDevicesgraphicsstatsutilssp

Imports (5)

polyCubMASSMatrixnlmespatstat.geom

LinkingTo (1)

polyCub

Suggests (29)

parallelgridxtable gridExtralatticecolorspace scales animation msm spc coda runjags INLA spdep numDeriv maxLik gsl fanplot hhh4contacts quadprog memoise polyclip intervals splancs gamlss MGLM sf tinytest knitr

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

4direct

0indirect

hhh4contacts EpiSignalDetection csalert tscount

Code & Tests

Code Composition

R 31,766 (65%)Rd 7,978 (16%)Vignettes 5,988 (12%)C 1,869 (4%)Other 904 (2%)C++ 353 (1%)

Code characteristics

Object systems

S3151S4 class3S4 generic30S4 method28

Cyclomatic complexity: 3.0 median / 238 max
Test cases: 138 / 0.07 per code line

Test coverage

Line coverage

36%

Expression

33.2%

Tests / Examples

35.8% / 69% ex

Functions

613 188 exported

Complexity

6.7 avg / 238 max

Call network

613 nodes / 600 edges

Call graph

Open call graph →

Loading call graph…

Lowest coverage

613 functions

Function	Cyclo	Coverage
LRCUSUM.runlength exp	11	0%
R0 exp	1	0%
addFormattedXAxis exp	8	0%
algo.bayes exp	6	0%
algo.bayes1 exp	1	0%
algo.bayes2 exp	1	0%

Datasets

Bundled datasets · 3

Name	Class	Rows × Cols	Also ships in
MMRcoverageDE	data.frame	19 × 5	–
campyDE	data.frame	522 × 12	–
husO104Hosp	data.frame	630 × 2	–

3 internal objects are bundled for the package's own use and not listed here.

People & History

People (19)

Maintainer (1)

Sebastian Meyer

Author, Maintainer

Authors (3)

Sebastian Meyer

Author, Maintainer

Michael Hoehle

Author, Thesis advisor

Michaela Paul

Author

Contributors (16)

R Core Team

Contributor · added in 1.25.0

Howard Burkom

Contributor

Thais Correa

Contributor

Leonhard Held

Contributor, Thesis advisor

Contributor

Contributor

Contributor

Contributor · added in 1.23.1

Show 8 more contributors

Contributor

Contributor

Contributor

Contributor

Contributor

Contributor

Contributor

Contributor

Listed in earlier versions (1)

R Core Team A few code segments are modified

no longer listed · 1.20.3 to 1.24.1

Package Timeline

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Package metadata

First published: 2005-11-18
Total releases: 90 / 21 yrs
License: GPL-2 OSI
Additional repositories: inla.r-inla-download.org
Minimum R: ≥ 3.6.0
Bundled data: 812 KB / 41 files
Download size: 3.7 MB
Installed size: not tracked yet
With dependencies: not tracked yet

Appears in task views

Anomaly Detection Analysis of Ecological and Environmental Data Epidemiology Handling and Analyzing Spatio-Temporal Data Time Series Analysis

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