Rewiring cattle movements to limit infection spread.

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.