Utility of algorithms for the analysis of integrated Salmonella surveillance data

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.