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





Modeling Infectious Diseases (4559)
Coordinating lecturer:Prof. dr. Niel HENS 
Co-lecturer:Prof. dr. Ziv SHKEDY 
Member of the teaching team:Prof. dr. Andrea TORNERI 
 Prof. dr. Christel FAES 
 dr. Lander WILLEM 
 dr. Pieter LIBIN 
 Prof. dr. Steven ABRAMS 

Credits: 6,0
Study load hours: 162
Period: semester 1 (6sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes
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
    Linear Models (3560) 5.0 stptn  
 


Prerequisites

The student should be familiar with statistical inference, statistical models, survival analysis.

The student should be familiar with programming in R.


Content

Introduction & the basic SIR model (Kermack and McKendrick, 1927), Stochastic models & multiple subpopulations, Metapopulation models, Individual-based models, The analysis of serological data, The analysis of outbreak data, The analysis of surveillance data, Within-host models, Case studies


Compulsory course material
 

The R software will be used in this course.

 

Recommended reading
  Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective,Niel Hens; Ziv Shkedy; Marc Aerts; Christel Faes; Pierre Van Damme; Philippe Beutels,Springer New York,9781461440710,Available as e-book: https://link.springer.com/book/10.1007%2F978-1-4614-4072-7

Infe ctious Diseases of Humans: Dynamics and Control,Roy M. Anderson; Robert M. May,Oxford University Press,9780198540403

Handbook of Infectious Disease Data Analysis,Leonhard Held, Niel Hens, Philip D O'Neill, Jacco Wallinga,1st edition,CRC Press,9781032087351

An Introduction to Infectious Disease Modelling,Emilia Vynnycky, Richard White,Oxford University Press, USA,9780198565765
 

Recommended course material
 


R and Python will be used in this course.


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Project ✔  


Evaluation

Semester 1 (6,00sp)

Evaluation method
Written evaluation during teaching period50 %
Homework✔
Take-home assignment✔
Written exam25 %
Open-book ✔
Oral exam25 %
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


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

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of statistical and data science.

     
  •  DC 

    • The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of statistical and data science.

     
  •  DC 

    • The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of application.

     
  •  DC 
    		• The student is able to extract new knowledge and insights from datasets in the application domain.
  •  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 oral communicator in their own field.

  •  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 N
2nd year Master Quantitative Epidemiology - icp N
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.