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Welcome to surveillance project!

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.


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:
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.


Prospective Outbreak Detection

Spatio-Temporal Regression Frameworks for the Retrospective Modelling of Epidemic Phenomena

Modelling Structural Delays and Reporting Delays in Surveillance Data


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.



Financial Support


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
Held, L., and Paul, M. (2012)
Modeling seasonality in space-time infectious disease surveillance data
Biometrical Journal, Vol. 54(6), pp. 824-843
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 An der Heiden, M. (2014)
Bayesian Nowcasting during the STEC O104:H4 Outbreak in Germany, 2011
Biometrics, 70(4):993-1002.
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
(The Accepted Author Manuscript is available as arXiv:1508.05740)
Meyer, S. and Held, L. (2014)
Power-law models for infectious disease spread
Annals of Applied Statistics, Vol. 8(3), pp. 1612-1639
(The paper is also available from ZORA or as arXiv:1308.5115, and has supplementary animations)
Meyer, S. and Held, L. (2017)
Incorporating social contact data in spatio-temporal models for infectious disease spread
Biostatistics, Vol. 18(2), pp. 338-351
Meyer, S., Held, L. and Höhle, M. (2017)
Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance
Journal of Statistical Software, Vol. 77(11), pp. 1-55
Meyer, S., Warnke, I., Rössler, U. and Held, L. (2016)
Model-based testing for space-time interaction using point processes: An application to psychiatric hospital admissions in an urban area
Spatial and Spatio-temporal Epidemiology, Vol. 17, pp. 15-25
(The Accepted Author Manuscript is available as arXiv:1512.09052)
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
Salmon, M., Schumacher, D. and Höhle, M. (2016)
Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance
Journal of Statistical Software, 70(10), 1-35
Salmon, M., Schumacher, D., Stark, K. and Höhle, M. (2015)
Bayesian outbreak detection in the presence of reporting delays
Biometrical Journal, Vol. 57(6), pp. 1051--1067
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