Abstract
Public health surveillance of emerging infectious diseases is an essential instrument in the attempt to control and prevent their spread. This paper presents the R package “surveillance”, which contains functionality to visualise routinely collected surveillance data and provides algorithms for the statistical detection of aberrations in such univariate or multivariate time series. For evaluation purposes, the package includes real-world example data and the possibility to generate surveillance data by simulation. To compare algorithms, benchmark numbers like sensitivity, specificity, and detection delay can be computed for a set of time series. Package motivation, use and potential are illustrated through a mixture of surveillance theory, case study and R code snippets.
Similar content being viewed by others
References
Altmann D (2003) The surveillance system of the Robert Koch Institute, Germany (Personal Communication)
Andersson H, Britton T (2000) Stochastic epidemic models and their statistical analysis, vol 151. Springer Lectures Notes in Statistics. Springer, Heidelberg
Ethelberg S, Mølbak K (2007) GastroEnteRitis Monitor, Statens Serum Institut, Denmark, http://germ.dk [Online; accessed 27-March-2007]
Farrington C, Andrews N (2003) Outbreak detection: application to infectious disease surveillance. In: Brookmeyer R, Stroup D (eds). Monitoring the health of populations, chapter 8. Oxford University Press, NY USA, pp 203–231
Farrington C, Andrews N, Beale A, Catchpole M (1996) A statistical algorithm for the early detection of outbreaks of infectious disease. J R Stat Soc Ser A 159:547–563
Held L, Hofmann M, Höhle M, Schmid V (2006) A two component model for counts of infectious diseases. Biostatistics 7:422–437
Held L, Höhle M, Hofmann M (2005) A statistical framework for the analysis of multivariate infectious disease surveillance data. Stat Modell 5:187–199
Höhle M (2006) Poisson regression charts for the monitoring of surveillance time series. Technical report, Department of Statistics, University of Munich. SFB Discussion Paper 500
Hutwagner L, Browne T, Seeman G, Fleischhauer A (2005) Comparing abberation detection methods with simulated data. Emerg Infect Dis 11:314–316
Kenett R, Pollak M (1983) On sequential detection of a shift in the probability of a rare event. J Am Stat Assoc 78(382):389–395
Knoth S (2004) spc: Statistical Process Control. R package version 0.2
Lawson A, Kleinman K (eds) (2005) Spatial and syndromic surveillance for public health. Wiley, London
Leisch F (2003) Sweave, Part I: Mixing R and LaTeX. R Newsletter 2(3):28–31
Lewin-Koh NJ, Bivand R, contributions by Edzer Pebesma J, Hausmann P, Rubio VG, Jagger T, Luque SP (2006) maptools: Tools for reading and handling spatial objects. R package version 0.6-5
Pebesma EJ, Bivand RS (2005) Classes and methods for spatial data in R. R News 5(2):9–13
Pierce D, Schafer D (1986) Residuals in generalized linear models. J Am Stat Assoc 81(396):977–986
R Development Core Team (2006) R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria, ISBN 3-900051-07-0
Riebler A (2004) Empirischer Vergleich von statistischen Methoden zur Ausbruchserkennung bei Surveillance Daten. Department of Statistics, University of Munich, Bachelor’s thesis
Robert Koch Institute (2004) SurvStat@RKI. http://www3.rki.de/SurvStat (Date of query: September 2004)
Robert Koch Institute (2006) Epidemiologisches Bulletin 33. Available from http://www.rki.de
Rogerson P, Yamada I (2004) Approaches to syndromic surveillance when data consist of small regional counts. Morb Mortal Wkly Rep 53:79–85
Rossi G, Lampugnani L, Marchi M (1999) An approximate CUSUM procedure for surveillance of health events. Stat Med 18:2111–2122
Sonesson C, Bock D (2003) A review and discussion of prospective statistical surveillance in public health. J R Stat Soc Ser A 166:5–12
Sonesson C, Frisén M (2005) Multivariate surveillance. In: Lawson A, Kleinman K (eds). Spatial and syndromic surveillance for public health, chapter 9. Wiley, London, pp 153–166
Stroup D, Williamson G, Herndon J, Karon J (1989) Detection of aberrations in the occurrence of notifiable diseases surveillance data. Stat Med 8:323–329
Widdowson M-A, Bosman A, van Straten E, Tinga M, Chaves S, van Eerden L, van Pelt W (2003) Automated, Laboratory-based system using the Internet for disease outbreak detection, the Netherlands. Emerg Infect Dis 9(9):1046–1052
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Höhle, M. \({\tt surveillance}\): An R package for the monitoring of infectious diseases. Computational Statistics 22, 571–582 (2007). https://doi.org/10.1007/s00180-007-0074-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00180-007-0074-8