Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

Fernanda C. Dórea, Beverly J. McEwen, W. Bruce McNab, Crawford W. Revie, Javier Sanchez

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
8 Downloads (Pure)


Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt-Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel.

Original languageEnglish
Article number20130114
Number of pages11
JournalJournal of the Royal Society Interface
Issue number83
Publication statusPublished - 6 Jun 2013


  • Control charts
  • Laboratory
  • Outbreak detection
  • Syndromic surveillance
  • Temporal aberration detection


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