Skip to main content

Utility of algorithms for the analysis of integrated Salmonella surveillance data

  • L. VRBOVA (a1), D. M. PATRICK (a1) (a2), C. STEPHEN (a3) (a4), C. ROBERTSON (a5), M. KOEHOORN (a1), E. J. PARMLEY (a6), N. I. DE WITH (a7) and E. GALANIS (a1) (a2)...

The objective of this study was to assess the use of statistical algorithms in identifying significant clusters of Salmonella spp. across different sectors of the food chain within an integrated surveillance programme. Three years of weekly Salmonella serotype data from farm animals, meat, and humans were used to create baseline models (first two years) and identify weeks with counts higher than expected using surveillance algorithms in the third (test) year. During the test year, an expert working group identified events of interest reviewing descriptive analyses of same data. The algorithms did not identify Salmonella events presenting as gradual increases or seasonal patterns as identified by the working group. However, the algorithms did identify clusters for further investigation, suggesting they could be a valuable complementary tool within an integrated surveillance system.

Corresponding author
*Author for correspondence: Dr L. Vrbova, School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada. (Email:
Hide All
1. Thomas, MK, et al. Estimates of the burden of foodborne illness in Canada for 30 specified pathogens and unspecified agents, circa 2006. Foodborne Pathogens and Disease 2013; 10: 639648.
2. Heymann, D (ed.). Control of Communicable Diseases Manual, 19th edn. Washington, DC: American Public Health Association, 2008.
3. Public Health Agency of Canada. National Enteric Surveillance Program (NESP) ( Accessed 19 November 2014.
4. Centers for Disease Control and Prevention. National Salmonella Surveillance ( Accessed 19 November 2014.
5. European Centre for Disease Prevention and Control. Food- and Waterborne Diseases and Zoonoses Programme ( Accessed 19 November 2014.
6. World Health Organization. Global Foodborne Infections Network (GFN) ( Accessed 19 November 2014.
7. Public Health Agency of Canada. FoodNet Canada (formerly known as C-EnterNet) ( Accessed 19 November 2014.
8. Public Health Agency of Canada. Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) ( Accessed 19 November 2014.
9. DANMAP. DANMAP: the Danish programme for surveillance of antimicrobial consumption and resistance in bacteria from animals, food and humans ( Accessed 19 November 2014.
10. Galanis, E, et al. Integrated surveillance of salmonella along the food chain using existing data and resources in British Columbia, Canada. Food Research International 2012; 45.
11. Sonesson, C, Bock, D. A review and discussion of prospective statistical surveillance in public health. Journal of the Royal Statistical Society Series A (Statistics in Society) 2003; 166: 521.
12. Hohle, M, Paul, M, Held, L. Statistical approaches to the monitoring and surveillance of infectious diseases for veterinary public health. Preventive Veterinary Medicine 2009; 19: 210.
13. Danan, C, et al. Automated early warning system for the surveillance of salmonella isolated in the agro-food chain in France. Epidemiology and Infection 2011; 139: 736741.
14. Kosmider, R, et al. A statistical system for detecting Salmonella outbreaks in British livestock. Epidemiology and Infection 2006; 134: 952960.
15. Humphrey, T, Threlfall, E, Cruikshank, J. Salmonellosis. In: Palmer, S. R., Soulsby, Lord, Simpson, D. I. H., eds. Zoonoses: Biology, Clinical Practice, and Public Health Control. New York, NY: Oxford University Press, 1998, pp. 191206.
16. Public Health Agency of Canada. Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) Annual Report 2008 ( Accessed 20 January 2015.
17. Farrington, CP, et al. A statistical algorithm for the early detection of outbreaks of infectious disease. Journal of the Royal Statistical Society Series A (Statistics in Society) 1996; 159: 547563.
18. Kosmider, RD, et al. Detecting new and emerging diseases on livestock farms using an early detection system. Epidemiology and Infection 2011; 139: 14761485.
19. Hohle, M. Inside R: Surveillance ( Accessed 7 October 2015.
20. WU Wien Institute for Statistics and Mathematics. The R Project for Statistical Computing (www.r-project-org). Accessed 9 October 2012.
21. Robertson, C, et al. Review of methods for space-time disease surveillance. Spatial and Spatio-temporal Epidemiology 2010; 1: 105116.
22. Guerin, MT, Martin, SW, Darlington, GA. Temporal clusters of salmonella serovars in humans in Alberta, 1990–2001. Canadian Journal of Public Health 2005; 96: 390395.
23. Guerin, MT, et al. A temporal study of salmonella serovars in animals in Alberta between 1990 and 2001. Canadian Journal of Veterinary Research 2005; 69: 8899.
24. Allard, R. Use of time-series analysis in infectious disease surveillance. Bulletin of the World Health Organization 1998; 76: 327333.
25. Heyndrickx, M, et al. Routes for Salmonella contamination of poultry meat: epidemiological study from hatchery to slaughterhouse. Epidemiology and Infection 2002; 129: 253265.
26. Currie, A, et al. Frozen chicken nuggets and strips and eggs are leading risk factors for Salmonella Heidelberg infections in Canada. Epidemiology and Infection 2005; 133: 809816.
27. Scotch, M, Odofin, L, Rabinowitz, P. Linkages between animal and human health sentinel data. BMC Veterinary Research 2009; 5: 15.
28. Bruun, T, et al. An outbreak of Salmonella Typhimurium infections in Denmark, Norway, and Sweden, 2008. Eurosurveillance 2009; 14: 16.
29. Pires, SM. Assessing the applicability of currently available methods for attributing foodborne disease to sources, including food and food commodities. Foodborne Pathogens & Disease 2013; 10: 206213.
30. Harker, KS, et al. National outbreaks of Salmonella infection in the UK, 2000–2011. Epidemiology and Infection. Published online: 31 May 2013. doi: 10.1017/S0950268813001210.
31. Sarwari, AR, et al. Serotype distribution of Salmonella isolates from food animals after slaughter differs from that of isolates found in humans. Journal of Infectious Diseases 2001; 183: 12951299.
32. Mather, AE, et al. An ecological approach to assessing the epidemiology of antimicrobial resistance in animal and human populations. Proceedings of the Royal Society of London, Series B: Biological Sciences 2012; 279: 16301639.
33. Fournier, PE, Drancourt, M, Raoult, D. Bacterial genome sequencing and its use in infectious diseases. Lancet Infectious Diseases 2007; 7: 711723.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Epidemiology & Infection
  • ISSN: 0950-2688
  • EISSN: 1469-4409
  • URL: /core/journals/epidemiology-and-infection
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Altmetric attention score

Full text views

Total number of HTML views: 4
Total number of PDF views: 37 *
Loading metrics...

Abstract views

Total abstract views: 300 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 21st March 2018. This data will be updated every 24 hours.