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





Modeling Infectious Diseases DL (4578)
  
Coordinating lecturer :Prof. dr. Niel HENS 
  
Co-lecturer :Prof. dr. Ziv SHKEDY 
  
Member of the teaching team :Prof. dr. Christel FAES 
 dr. Lander WILLEM 
 dr. Pieter LIBIN 
 Prof. dr. Steven ABRAMS 

Language of instruction : English
Credits: 6,0
  
Period: semester 1 (6sp)
  
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
    Project: Multivariate and Hierarchical Data DL (3582) 8.0 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 epidemic data, The analysis of surveillance data, Within-host models, Case studies I & II


Organisational and teaching methods
Organisational methods  
Distance learning ✔  


Evaluation

Period 1    Credits 6,00

Evaluation method
Written evaluaton during teaching periode50 %
Homework✔
Take-home assignment✔
Oral exam50 %

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory course material
 

The R software will be used in this course.  
 

Recommended reading
  Infectious Diseases of Humans: Dynamics and Control,Roy M. Anderson; Robert M. May,Oxford University Press,9780198540403

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

Hand book of Infectious Disease Data Analysis,Leonhard Held, Niel Hens, Philip D O'Neill, Jacco Wallinga,1st edition,CRC Press,1032087358

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. 


Learning outcomes
Master of Statistics and Data Science
  •  EC 

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

  •  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 extract new knowledge and insights from datasets in the application domain.
     
  •  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 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 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.

  •  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 oral communicator in their own field.

     
  •  DC 

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

  •  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 N



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.