De elektronische studiegids voor het academiejaar 2025 - 2026 is onder voorbehoud.





Microbial Risk Assessment DL (3394)
  
Coordinating lecturer :Prof. dr. Marc AERTS 
  
Co-lecturer :Prof. dr. Christel FAES 

Language of instruction : English
Credits: 3,0
  
Period: semester 1 (3sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   Mandatory sequentiality bound on the level of programme components
 
 
  Following programme components must have been included in your study programme in a previous education period
    Generalized Linear Models DL (5465) 6.0 stptn
 

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 diffferent steps (and their integration) of microbial risk assessment

Statistical, mathematical and simulation methodology for

  • models for quantitative risk assessment
  • models for exposure 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 empirical dose response models, infection versus illness, model averaging.


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Project ✔  


Evaluation

Period 1    Credits 3,00

Evaluation method
Written evaluaton during teaching periode50 %
Transfer of partial marks within the academic year✔
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 the project is carried over to the retake exam.
 

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


Learning outcomes
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.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
second year Data Science - distance learning J
second year Master Bioinformatics - distance learning J
second year Master Biostatistics - distance learning J
second year Quantitative Epidemiology - distance learning J



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.