De elektronische studiegids voor het academiejaar 2026 - 2027 is onder voorbehoud.





Microbial Risk Assessment (1743)
Coordinating lecturer:Prof. dr. Christel FAES 
Co-lecturer:Prof. dr. Marc AERTS 

Credits: 3,0
Study load hours: 81
Period: semester 1 (3sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes
Sequentiality
No sequentiality


Prerequisites

The student should be familiar with statistical inference and statistical models.

The student should be familiar with programming in R.


Content

Introduction to the different steps (and their integration) of microbial risk assessment.

Statistical, mathematical and simulation methodology for:

  • models for quantitative risk assessment
  • models forexposure assessment (concentration distribution, limit of detection, distribution of number of organisms)
  • models for the quantification of consumption data and dealing with correlated inputs
  • mechanistic and empiricaldose-response models, infection versus illness, model averaging.


Compulsory course material
 

Lecture notes will be made available via blackboard. 

The R software will be used in this course. 
 

Recommended reading
  Quantitative Microbial Risk Assessment,Charles N. Haas; Joan B. Rose; Charles P. Gerba,Wiley,9780471183976,Nu reeds beschikbaar als e-book @UHasselt: http://bib-proxy.uhasselt.be/login?url=https://ebookcentral.proquest.com /lib/ubhasselt/detail.action?docID=1706900


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Project ✔  


Evaluation

Semester 1 (3,00sp)

Evaluation method
Written evaluation during teaching period50 %
Transfer of partial marks within the academic yearYes, no resit exam
Paper✔
Oral exam50 %
Open questions✔
Use of study material during evaluation✔
Explanation (English)Slides, course notes

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
Explanation (English)Score for project is carried over to the retake exam.


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

     
  •  DC 

    • ... correctly using state-of-the-art analysis methodology.

     
  •  DC 

    • ... correctly using state-of-the-art software.

  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective writer in their own field.

     
  •  DC 

    • The student is an effective writer, both within their own field as well as across disciplines.

  •  EC 

    • The student is capable of acquiring new knowledge.

 
Included in these programmesTolerance3
2nd year Master Bioinformatics Y
2nd year Master Biostatistics Y
2nd year Master Data Science Y
2nd year Master Quantitative Epidemiology Y
2nd year Master Quantitative Epidemiology - icp Y
Exchange Programme Statistics Y



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.