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Foot and mouth disease model verification and 'relative validation' through a formal model comparison
R L Sanson1, N Harvey, M G Garner
1AsureQuality Limited, P.O. Box 585, Palmerston North 4440, New Zealand.
Summary
Three foot and mouth disease models were validated using Irish epidemic scenarios. Early ring vaccination proved most effective in reducing infected premises across all models, boosting confidence in these decision-support tools.
Area of Science:
- Veterinary Epidemiology
- Disease Modeling
- Infectious Disease Control
Background:
- Foot and mouth disease (FMD) poses a significant threat to livestock globally.
- Accurate disease spread models are crucial for effective FMD control strategies.
- Validation of existing models enhances their reliability as decision-support tools.
Purpose of the Study:
- To validate three distinct FMD models: AusSpread, InterSpread Plus, and the North American Animal Disease Spread Model.
- To assess the predictive performance of these models under various epidemic scenarios.
- To build end-user confidence in the models' utility for disease management.
Main Methods:
- Collaborative validation involving researchers from Australia, New Zealand, Canada, and the United States.
- Simulation of FMD outbreak scenarios in the Republic of Ireland, including uncontrolled epidemics and those managed by stamping out and vaccination.
- Comparison of model predictions for infected premises, epidemic duration, and outbreak area size.
Main Results:
- While absolute outcomes varied between models, within-model comparisons of control strategies were consistent.
- Early implementation of ring vaccination demonstrated the most significant reduction in infected premises compared to stamping out.
- Quantification of relative changes in outcomes based on different control strategies and resource constraints.
Conclusions:
- The consistency of within-model results across different scenarios suggests appropriate model assumptions.
- Validation using real-world data (Republic of Ireland) increases confidence in the models' predictive capabilities.
- The findings support the use of these validated models as reliable decision-support tools for FMD management.
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