By the end of the programme students will:
(i) have attained a fundamental understanding of the substantial body of applied modelling, statistics and recent developments in the field of Predictive Modelling and Quantitative Risk Assessment of foods,
(ii) have exercised personal responsibility and autonomous initiative in designing and solving complex microbiological problems that are solved in a rigorous and professional approach,
(iii) have engaged in critical dialogue and learned to criticise the broader implication of Applied Modelling approaches in Food safety through interactive teaching,
(iv) have exploited available software packages and quantitative approaches for enriching current studies in the field.

Indicative Module Content:

1: Introduction to BSEN40470 and Recap of BSEN30060 Quantitative Risk Assessment**
- Overview of BSEN40470 and its objectives.
- Recap of key concepts and methodologies covered in BSEN30060 Quantitative Risk Assessment.
2: Dose-response on Microbes**
- Understanding dose-response relationships for microbial contaminants.
- Case studies and examples illustrating microbial dose-response models.
3: Dose-response on Chemicals: Part A**
- Exploring dose-response relationships for chemical contaminants.
- Overview of traditional dose-response assessment methods.
4: Dose-response on Chemicals: Part B (Meta-analysis)**
- Introduction to meta-analysis techniques in dose-response assessment.
- Practical exercises and applications of meta-analysis in chemical risk assessment.
5: Exposure Assessment**
- Principles and methodologies of exposure assessment.
- Sampling strategies and data collection techniques for assessing exposure to contaminants.
6: Risk Characterization**
- Techniques for characterizing and communicating risks to stakeholders.
- Case studies demonstrating risk characterization in various environmental and occupational settings.
7: Bayesian Analysis (Theory + 2 Exercises)**
- Introduction to Bayesian analysis and its applications in risk assessment.
- Theoretical foundations and practical exercises to understand Bayesian inference.
8: Data Collection for Exposure Assessment and Regression Modelling**
- Practical session on data collection methodologies for exposure assessment.
- Introduction to regression modeling techniques for analyzing exposure data.
9: Case Study
- Application of concepts learned throughout the module to a real-world case study.
- Group discussion and analysis of the case study, focusing on risk assessment and Bayesian analy

Not applicable to this module.

• Group/class feedback, post-assessment

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