180.603.11 Bayesian Decision Analysis and Mathematical Models in Occupational and Environmental Exposure Assessment
- Environmental Health and Engineering
- Summer Inst. term
- 2 credits
- Academic Year:
- 2018 - 2019
- East Baltimore
Some familiarity with the AIHA Strategy for Assessing and Managing Occupational Exposures. Some experience in exposure assessments and monitoring data interpretation. Some experience in mathematical exposure models.
Are you interested in improving the accuracy and efficiency of your exposure decision-making and improving your professional judgment in occupational and environmental exposure measurement data interpretation?
Provides tools for applying the Bayesian framework for decision analysis. Explores, through discussion and exercises, opportunities for its application in occupational and environmental hygiene data interpretation and exposure risk assessment. Emphasizes the use of a number of heuristics (rules of thumb) and mathematical exposure models to increase the accuracy and efficiency of exposure decision-making. Includes several exposure assessment exercises using videos of tasks and basic characterization of the environment.
- Learning Objectives:
- Describe a Bayesian framework for decision analysis and relate a Bayesian framework for decision analysis to industrial hygiene exposure assessment strategies
- Explain and use heuristics and mathematical exposure models to improve exposure decision accuracy and efficiency in various workplace scenarios
- Use simple software tools to perform a Bayesian decision analysis of industrial hygiene monitoring data and exposure models
- Identify determinants of exposure for inhalation exposures in actual workplace settings, be able to use these in mathematical models, and be able to express uncertainties in these in a distributional form
- Define similarly exposed groups (SEGs) using knowledge of the workplace, operations, workforce, materials, tasks, exposure controls
- Methods of Assessment:
25% participation, 25% in-class exercises, 50% homework assignments
- Enrollment Restriction:
Master's students, PhD students and working professionals
- Instructor Consent:
No consent required