Predicting the Landscape Epidemiology of Foot-and-Mouth Disease in Endemic Regions: An Interpretable Machine Learning Approach

Keyphrases

• Foot-and-mouth Disease 100%
• Machine Learning Approach 100%
• Endemic Region 100%
• Landscape Epidemiology 100%
• Interpretable Machine Learning 100%
• Middle East and North Africa 40%
• Serotype 1 40%
• Serotype O 40%
• Cropland 20%
• Animal Pathogens 20%
• Surveillance Program 20%
• Sheep 20%
• Risk-based Surveillance 20%
• Food Security 20%
• Environmental Factors 20%
• Ecological Niche 20%
• Complex Interactions 20%
• Disease Outbreaks 20%
• Host Factors 20%
• Health Security 20%
• Performance Accuracy 20%
• Spatial Risk 20%
• Public Health Impact 20%
• Machine Learning Algorithms 20%
• Performance Prediction 20%
• High-risk Areas 20%
• Nonlinear Relationship 20%
• Statistical Framework 20%
• Arabian Peninsula 20%
• Virus Circulation 20%
• Anthropogenic Factors 20%
• Threshold Effect 20%
• Africa Region 20%
• Livestock Health 20%
• Shapley Value 20%
• Human Population Density 20%
• Analytical pipeline 20%
• Interaction Value 20%
• Economic Health 20%
• Proximity to Roads 20%
• Livestock nutrition 20%
• Winding Temperature 20%

Immunology and Microbiology

• Foot 100%
• Mouth 100%
• Cohort Effect 100%
• Epidemiology 100%
• Serotype 60%
• Virus 20%
• Infectious Agent 20%
• Sheep 20%
• Population Density 20%
• Buffalo 20%