Decision Support for Mitigation of Livestock Disease: Rinderpest as a Case Study

Area of Science:

• Epidemiology
• Veterinary Medicine
• Computational Biology

Background:

• Rinderpest, a devastating cattle disease, is eradicated in the wild but poses a threat due to stored virus samples.
• Predictive modeling is crucial for understanding and mitigating potential re-emergent infectious diseases.

Purpose of the Study:

• To present a versatile, interactive model for predicting geographically resolved epidemic progression.
• To assess the impact of interventions like vaccination and transmission reduction on disease spread.
• To evaluate the potential consequences of a rinderpest virus release.

Main Methods:

• Utilized deterministic simulations with a compartmental disease model.
• Developed an interactive platform for rapid analysis of epidemic scenarios.
• Validated the model through comparison with historical rinderpest outbreaks.

Main Results:

• The simulation model runs rapidly, enabling efficient analysis of disease mitigation strategies.
• Simulations demonstrated the potential social and economic consequences of a rinderpest outbreak.
• Model validation against historical data provides confidence in its predictive capabilities.

Conclusions:

• The developed model is a valuable tool for predicting epidemic progression and evaluating control measures.
• A rinderpest outbreak could have severe socio-economic repercussions.
• The model can inform preparedness and response strategies for livestock diseases.