BIKE

BIKE performs Bayesian hierarchical dietary exposure assessments for microbiological and chemical hazards using occurrence and consumption data.

Key Features:

• Bayesian hierarchical modeling: Implements Bayesian hierarchical models to estimate exposure levels from occurrence and consumption data.
• OpenBUGS and R integration: Integrates OpenBUGS and R for model specification and statistical computation.
• Automatic BUGS code generation: Automatically generates specific BUGS code based on provided occurrence and consumption datasets.
• Chronic exposure support: Supports chronic exposure assessments for chemical contaminants.
• Acute exposure support: Supports acute exposure assessments for microbiological hazards.
• Uncertainty and variability estimation: Estimates uncertainty and variability in exposure levels within the Bayesian framework.
• Optional model structures: Provides several optional model structures to accommodate diverse assessment scenarios.
• Simulated synthetic data: Includes simulated occurrence and consumption datasets that mimic real-world patterns for example analyses.

Scientific Applications:

• Chemical hazard exposure assessment: Quantifying chronic dietary exposure to chemical contaminants for risk assessment.
• Microbiological hazard exposure assessment: Quantifying acute dietary exposure to microbiological hazards for risk assessment.
• Uncertainty characterization: Characterizing uncertainty and variability in exposure estimates to inform decision making.
• Model development and testing: Developing and testing exposure models using simulated occurrence and consumption datasets.

Methodology:

Implements Bayesian hierarchical models using OpenBUGS and R, automatically generates BUGS code from occurrence and consumption data, supports multiple optional model structures, and estimates uncertainty and variability in exposure levels.