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Multivariate syndromic surveillance for cattle diseases: Epidemic simulation and algorithm performance evaluation.

Céline Faverjon1, Luís Pedro Carmo1, John Berezowski1

  • 1Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Liebefeld, Switzerland.

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Summary

Multivariate Syndromic Surveillance (SyS) effectively detects livestock diseases using control charts. Directional multivariate control charts like MEWMA and MCUSUM show promise for early epidemic detection with high specificity.

Keywords:
Directional multivariate control chartsEpidemic simulationMCUSUMMEWMASyndromic surveillanceTime series

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Area of Science:

  • Veterinary epidemiology
  • Public health surveillance
  • Statistical process control

Background:

  • Multivariate Syndromic Surveillance (SyS) systems integrate diverse data for early infectious disease epidemic detection.
  • Operational multivariate SyS in veterinary medicine are limited, partly due to challenges in performance assessment with scarce field data.

Purpose of the Study:

  • To demonstrate a practical multivariate event detection method using directionally sensitive multivariate control charts for livestock disease SyS.
  • To evaluate the performance of Multivariate Exponentially Weighted Moving Average (MEWMA) and Multivariate Cumulative Sum (MCUSUM) algorithms using simulated epidemics.

Main Methods:

  • Developed a standardized method for simulating multivariate epidemics of Bovine Virus Diarrhea (BVD), Infectious Bovine Rhinotracheitis (IBR), Bluetongue virus (BTV), and Schmallenberg virus (SV).
  • Applied MEWMA and MCUSUM algorithms to 12 syndrome time series from Swiss national databases.
  • Evaluated detection timeliness and specificity (95%) of the algorithms.

Main Results:

  • Both MEWMA and MCUSUM detected simulated epidemics approximately 4.5 months after onset with 95% specificity.
  • MEWMA consistently detected epidemics earlier than MCUSUM.
  • Epidemics of IBR and SV were detected earlier than BVD and BTV.

Conclusions:

  • Directional multivariate control charts are effective for combining time series data for early detection of subtle changes in livestock populations.
  • The simulation approach is adaptable for assessing SyS systems where real epidemic data are unavailable.
  • These methods can support the implementation and assessment of multivariate SyS in animal health surveillance.