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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.


Description

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:
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.

Prospective Outbreak Detection


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


Modelling Structural Delays and Reporting Delays in Surveillance Data


Download

The surveillance package is available for download from CRAN.
Current package development, help-forum and bugtracking is hosted through R-Forge:

https://r-forge.r-project.org/projects/surveillance/

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

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 argument type="source" in the above call.

News


Documentation


Developers


Financial Support


References

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