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Abstract

We describe the design and implementation of a novel automated outbreak detection system in Germany that monitors the routinely collected surveillance data for communicable diseases. Detecting unusually high case counts as early as possible is crucial as an accumulation may indicate an ongoing outbreak. The detection in our system is based on state-of-the-art statistical procedures conducting the necessary data mining task. In addition, we have developed effective methods to improve the presentation of the results of such algorithms to epidemiologists and other system users. The objective was to effectively integrate automatic outbreak detection into the epidemiological workflow of a public health institution. Since 2013, the system has been in routine use at the German Robert Koch Institute.

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/content/10.2807/1560-7917.ES.2016.21.13.30180
2016-03-31
2017-10-20
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2016.21.13.30180
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References

  1. Faensen D, Claus H, Benzler J, Ammon A, Pfoch T, Breuer T, et al. [email protected] multistate electronic reporting system for communicable diseases. Euro Surveill. 2006;11(4):100-3. PMID: 16645245 
  2. Unkel S, Farrington C, Garthwaite PH, Robertson C, Andrews N. Statistical methods for the prospective detection of infectious disease outbreaks: a review. J R Stat Soc [Ser A]. 2012;175(1):49-82.  https://doi.org/10.1111/j.1467-985X.2011.00714.x 
  3. Koch J, Schrauder A, Alpers K, Werber D, Frank C, Prager R, et al. Salmonella agona outbreak from contaminated aniseed, Germany. Emerg Infect Dis. 2005;11(7):1124-7.  https://doi.org/10.3201/eid1107.041022  PMID: 16022796 
  4. Cakici B, Hebing K, Grünewald M, Saretok P, Hulth A. CASE: a framework for computer supported outbreak detection. BMC Med Inform Decis Mak. 2010;10(1):14.  https://doi.org/10.1186/1472-6947-10-14  PMID: 20226035 
  5. Kling AM, Hebing K, Grünewald M, Hulth A. Two years of computer supported outbreak detection in Sweden: the user’s perspective. J Health Med Informat. 2012;3(1):108.
  6. Hulth A, Andrews N, Ethelberg S, Dreesman J, Faensen D, van Pelt W, et al. Practical usage of computer-supported outbreak detection in five European countries. Euro Surveill. 2010;15(36):8-13. PMID: 20843470 
  7. Noufaily A, Enki DG, Farrington P, Garthwaite P, Andrews N, Charlett A. An improved algorithm for outbreak detection in multiple surveillance systems. Stat Med. 2013;32(7):1206-22.  https://doi.org/10.1002/sim.5595  PMID: 22941770 
  8. Salmon M, Schumacher D, Höhle M. Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance. J Stat Softw. Forthcoming.
  9. Stöcker P, Rosner B, Werber D, Kirchner M, Reinecke A, Wichmann-Schauer H, et al. Outbreak of Salmonella Montevideo associated with a dietary food supplement flagged in the Rapid Alert System for Food and Feed (RASFF) in Germany, 2010. Euro Surveill. 2011;16(50):20040. PMID: 22221497 
  10. R Core Team. R: A Language and Environment for Statistical Computing. 2013: Vienna, Austria.
  11. Höhle M. surveillance: An R package for the monitoring of infectious diseases. Comput Stat. 2007;22(4):571-82.  https://doi.org/10.1007/s00180-007-0074-8 
  12. Ripley B, Lapsley M. RODBC: ODBC Database Access. 2012. R package version 1.3-10
  13. Reis BY, Kirby C, Hadden LE, Olson K, McMurry AJ, Daniel JB, et al. AEGIS: a robust and scalable real-time public health surveillance system. J Am Med Inform Assoc. 2007;14(5):581-8.  https://doi.org/10.1197/jamia.M2342  PMID: 17600100 
  14. Microsoft. Microsoft SQL Server Analysis Services. 2012.
  15. Stroup DF, Williamson GD, Herndon JL, Karon JM. Detection of aberrations in the occurrence of notifiable diseases surveillance data. Stat Med. 1989;8(3):323-9.  https://doi.org/10.1002/sim.4780080312  PMID: 2540519 
  16. Gertler M, Dürr M, Renner P, Poppert S, Askar M, Breidenbach J, et al. Outbreak of Cryptosporidium hominis following river flooding in the city of Halle (Saale), Germany, August 2013. BMC Infect Dis. 2015;15(1):88.  https://doi.org/10.1186/s12879-015-0807-1  PMID: 25879490 
  17. Salmon M, Schumacher D, Stark K, Höhle M. Bayesian outbreak detection in the presence of reporting delays. Biom J. 2015;57(6):1051-67.  https://doi.org/10.1002/bimj.201400159 
  18. Noufaily A, Farrington P, Garthwaite P, Enki DG, Andrews N, Charlett A. Detection of infectious disease outbreaks from laboratory data with reporting delays. J Am Stat Assoc. Published ahead of print.
  19. Höhle M, Paul M. Count data regression charts for the monitoring of surveillance time series. Comput Stat Data Anal. 2008;52(9):4357-68.  https://doi.org/10.1016/j.csda.2008.02.015 
  20. Kulldorff M. SaTScan - Software for the spatial, temporal, and space-time scan statistics. Boston: Information Management Services; 2014
  21. Tango T, Takahashi K, Kohriyama K. A space-time scan statistic for detecting emerging outbreaks. Biometrics. 2011;67(1):106-15.  https://doi.org/10.1111/j.1541-0420.2010.01412.x  PMID: 20374242 
  22. Neill DB. Fast subset scan for spatial pattern detection. J R Stat Soc Series B Stat Methodol. 2012;74(2):337-60.  https://doi.org/10.1111/j.1467-9868.2011.01014.x 
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