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.

- Intention:
- To provide open source software for the temporal and spatio-temporal visualization, modelling and monitoring of epidemic phenomena. This includes count, binary and categorical data time series as well as continuous-time processes having discrete or continuous spatial resolution.
- Potential users:
- Biostatisticians, epidemiologists and others working in, e.g., applied infectious disease epidemiology. However, applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences.
- Main applications:
- Prospective detection of aberrations in routinely collected public health data seen as univariate and multivariate time series of counts.
- General regression modelling of spatio-temporal epidemic phenomena (retrospectively).
- License:
- This program is free software, and you are welcome to redistribute it
under the terms of
the GNU General Public
License, version 2.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY.

`surveillance`provides the`S4`class data structure`"sts"`and a framework for methodological developments of change-point algorithms for time series of counts.- Prospective outbreak detection procedures for count data time series:
`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).

This approach has been extended to cover time varying proportions in a binomial setting.`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)

- Online change-point detection in categorical time series:
`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)

- Prospective space-time cluster detection:
`stcd`

- (experimental) Point process based approach by Assuncao & Correa (2009)

- For evaluation purposes, the package contains example
datasets drawn
from the SurvStat@RKI database maintained by the RKI, Germany. More
comprehensive comparisons using simulation studies are possible by
methods for simulating point source outbreak data using a hidden Markov
model. To compare the algorithms, benchmark numbers like sensitivity,
specificity and detection delay can be computed for entire sets of
surveillance time series. Furthermore, a Markov Chain approximation for
computing the run-length distribution of the proposed likelihood ratio
CUSUMs is available as function
`LRCUSUM.runlength.`

- Multivariate time series models for count data:
`algo.hhh`

- Held et al. (2005) and Paul et al. (2008)`hhh4`

- Paul and Held (2011)`algo.twins`

- Held et al. (2006)

- Continuous-time point process modelling:
`twinSIR`

- continuous-time/discrete-space modelling as described in Höhle (2009). The`"epidata"`

class provides the appropriate data structure for such data.`twinstim`

- continuous-time/continuous-space modelling as described in Meyer et al. (2012). The`"epidataCS"`

class describes the data, which mainly consist of the observed events and exogenous covariates on a space-time lattice.

- See Meyer and Held (2014) for a joint description of the
`hhh4()`

(count data) and`twinstim()`

(individual-level data) modelling frameworks -- with a view to account for power-law decay of spatial interaction.

- Backprojection methods
`backprojNP`

- Non-parametric back-projection method of Becker et al. (1991) used in, e.g., Werber et al. (2013).

Thesurveillancepackage 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 inRvia

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

Currently, R-Forge does not offer binaries for MacOS X, but installation might succeed with the additional argumenttype="source"in the above call.

- See the NEWS of the latest version released on CRAN or the NEWS file of the current development version.
- 2013/04/23 Talk at the Stockholm R useR group (StockholmR) on Making R packages (and) Shiny.

- 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:

`algo.farrington`

+`algo.glrnb`

+`nowcast`

`backprojNP`

`twinSIR`

- Michael Höhle, Department of Mathematics, Stockholm University, Sweden (Project Admin)
- Sebastian Meyer, Institute of Social and Preventive Medicine, University of Zurich, Switzerland
- Michaela Paul (previously at the 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: 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)

- Held, L., Höhle, M. and Hofmann, M. (
**2005**) *A statistical framework for the analysis of multivariate infectious disease surveillance counts*- Statistical Modelling, Vol. 5(3), pp. 187-199
- Held, L., Hofmann, M., Höhle, M. and Schmid, V. (
**2006**) *A two-component model for counts of infectious diseases*- Biostatistics, Vol. 7(3), pp. 422-437
- Höhle, M. (
**2010**) *Online Change-Point Detection in Categorical Time Series*- In: Statistical Modelling and Regression Structures (Kneib, T. & Tutz, G.,
*eds.*) - Physica-Verlag HD, pp. 377-397
- Höhle, M. (
**2009**) *Additive-multiplicative regression models for spatio-temporal epidemics*- Biometrical Journal, Vol. 51(6), pp. 961-978
- Höhle, M. and Paul, M. (
**2008**) *Count data regression charts for the monitoring of surveillance time series*- Computational Statistics and Data Analysis, Vol. 52(9), pp. 4357-4368
- Höhle, M. (
**2007**) `surveillance`: An R package for the monitoring of infectious diseases- Computational Statistics, Vol. 22(4), pp. 571-582
- Manitz, J. and Höhle, M. (
**2013**) *Bayesian outbreak detection algorithm for monitoring reported cases of campylobacteriosis in Germany*- Biometrical Journal, Vol. 55(4), pp. 509-526
- Meyer, S., Elias, J. and Höhle, M. (
**2012**) *A space-time conditional intensity model for invasive meningococcal disease occurrence*- Biometrics, Vol. 68(2), pp. 607-616
- Meyer, S. and Held, L. (
**2014**) *Power-law models for infectious disease spread*- Revised for the Annals of Applied Statistics, available as arXiv:1308.5115
- Paul, M. and Held, L. (
**2011**) *Predictive assessment of a non-linear random effects model for multivariate time series of infectious disease counts*- Statistics in Medicine, Vol. 30(10), pp. 1118-1136
- Paul, M., Held, L. and Toschke, A. M. (
**2008**) *Multivariate modelling of infectious disease surveillance data*- Statistics in Medicine, Vol. 27(29), pp. 6250-6267
- Werber, D., King, L.A., Müller, L., Follin, P., Buchholz, U., Bernard, H., Rosner, B.M., Ethelberg, S., de Valk, H., Höhle, M. (
**2013**) *Associations of Age and Sex on Clinical Outcome and Incubation Period of Shiga toxin-producing Escherichia coli O104:H4 Infections, 2011*- American Journal of Epidemiology, Vol. 178(6):984-992