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Related Experiment Video

Updated: Jun 12, 2025

Catheterization of Intestinal Loops in Ruminants
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Rewiring cattle movements to limit infection spread.

Thibaut Morel-Journel1, Pauline Ezanno2, Elisabeta Vergu3

  • 1Oniris, INRAE, BIOEPAR, 44300, Nantes, France. thibaut.morel-journel@univ-paris13.fr.

Veterinary Research
|September 19, 2024
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Summary

This study introduces an algorithm to reduce cattle disease spread by rerouting animal movements from high- to low-prevalence herds. The method effectively contains infections, especially with recent outbreaks and frequent prevalence monitoring.

Keywords:
Control strategydata-basedepidemiologynetworkstochastic model

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Area of Science:

  • Veterinary Epidemiology
  • Network Analysis
  • Disease Control

Background:

  • Cattle tracing databases track livestock demographics and disease spread via trade.
  • Commercial animal movements form networks that can transmit infections.
  • Altering these networks can mitigate disease transmission risks.

Purpose of the Study:

  • To develop and evaluate an algorithm for reducing disease spread in cattle by modifying trade networks.
  • To assess the algorithm's effectiveness in epidemic and endemic disease scenarios.

Main Methods:

  • Developed a novel algorithm to rewire cattle movement networks, redirecting trade from high- to low-prevalence herds.
  • Utilized a computational model based on the French cattle movement tracing database (BDNI).
  • Simulated disease spread for recent (epidemic) and historical (endemic) outbreaks.

Main Results:

  • Rewiring movements successfully contained infections to fewer herds.
  • Effectiveness was higher for recent outbreaks and with frequent disease prevalence estimations.
  • Allowing all high-to-low prevalence movements reduced containment in epidemic scenarios.
  • Frequent, fine-grained prevalence assessments improved control in endemic settings.

Conclusions:

  • The proposed algorithm offers a targeted strategy for improving disease control in cattle populations.
  • Network modifications require monitoring for unintended consequences on other diseases.
  • The generalizable algorithm can be applied to diverse networks of disease-spreading movements.