Simulation-model evaluation of bovine tuberculosis-eradication strategies in Argentine dairy herds

Andres M. Perez, Michael P. Ward, Viviana Ritacco

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

We used stochastic modification of the Reed-Frost model to assess the impact of 14 different eradication strategies on bovine tuberculosis, under three scenarios of disease introduction, in Argentine dairy herds. All strategies investigated were based on a test-and-cull approach using either the caudal-fold test (CFT), the single cervical test (SCT), the gamma-interferon (γ-IFN) test or a combination of these tests. The maximum, minimum and most-likely sensitivity and specificity were investigated in three scenarios simulating different epidemiological conditions. Simulation results were highly variable; therefore, it is difficult to predict the effect of disease-control strategies within individual herds. On average, the use of the SCT was less efficient in eradicating tuberculosis from the simulated herd than the CFT. Eradication would be achieved most efficiently by strategies in which the CFT was used assuming maximum possible sensitivity and specificity (difficult to achieve in the field) and/or the γ-IFN test - which has both economical and logistical limitations to its widespread application in Argentina. When disease-control was simulated in situations in which herd tuberculosis prevalence is ≥22%, all strategies we simulated were less efficient than herd depopulation. Considering that Argentine dairy producers are not compensated financially for cattle culled because of tuberculosis, eradication strategies currently used in the Argentine national tuberculosis eradication might not succeed.

Original languageEnglish (US)
Pages (from-to)351-360
Number of pages10
JournalPreventive Veterinary Medicine
Volume54
Issue number4
DOIs
StatePublished - Aug 30 2002
Externally publishedYes

Keywords

  • Argentina
  • Bovine tuberculosis
  • Dairy herds
  • Epidemiology
  • Eradication
  • Simulation modeling

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