SFOK09103U Infectious Disease Epidemiology

Volume 2025/2026
Education

MSc in Public Health Science - elective course

MSc in Health Informatics - elective course 

 

Content

The course will provide students with insight into infectious disease modelling, with a strong focus on on the mathematical and technical methods. In the course we will discuss:

  • Basic infectious disease epidemiology
  • Bascis of R programming
  • How to measure infectious disease
  • Likelihood and Bayesian inference
  • Succeptible-infected-recovered models
  • Renewal equations
  • Spatial statistics
  • Stochastic models 
  • Guest lecturers 

 

 

Learning Outcome

At the end of the course the student should:

Knowledge

  • Have basic knowledge about the maths of infectious disease models and how to implement them
  • Should a pandemic happen again today, the student can analyse the data

 

Skills

  • Learn about modelling infectious disease data
  • Understand the basics of inference and learning of models
  • Understand the data the maths behind the models

 

Competencies

  • Contribute to handling of real-life infectious disease outbreaks
  • Contribute to setting up infectious disease preparedness systems
Students must have passed Public Health Science BSc courses 'SFOB20004U Epidemiologi 1' and 'SFOB20008U Epidemiologi 2' as well as the course BSc course in 'SFOB20006U Statistik' or Health Informatics BSc courses ‘SITB23001U Epidemiology’ and ‘SITB18001U Sundhedsvidenskabelig statistik'.
Lectures with demonstration and co-programming together. With some group tasks
The course is open for MSc students with pre-approval in Health Informatics, Global Health, Human Biology and Health Science.
  • Category
  • Hours
  • Class Instruction
  • 40
  • Preparation
  • 235
  • Exam
  • 10
  • Total
  • 285
Continuous feedback during the course of the semester

Oral feedback will be given during the course. After ended exam, students can get feedback on the result by contacting the course responsible.

Credit
10 ECTS
Type of assessment
Written assignment, 24 hours
Type of assessment details
The written assignment will consist of a series of questions. Before the exam the course responsible will go over a large number of sample questions and provide a discussion around them.
The written assignment is to be made in groups of 2 to 4 students.
Aid
All aids allowed

It is the responsibility of the student to ensure the accuracy, integrity, and originality of the text, including ensuring that the text is not factually incorrect, plagiarized, or contains copyrighted material. AI/LLM’s may not be used as an actual author or a scientific source cf. Vancouver Guidelines.

Marking scale
7-point grading scale
Censorship form
External censorship
Exam period

Please see the exam schedule at KUnet

Re-exam

Please see the exam schedule at KUnet

Criteria for exam assesment

To achieve the grade 12 the student is expected to

Knowledge

  • Show basic knowledge about the maths of infectious disease models and how to implement them
  • Be able to analyse the data, should a pandemic happen again today

 

Skills

  • Be able to model infectious disease data
  • Explain the basics of inference and learning of models
  • Explain the data the maths behind the models

 

Competencies

  • Contribute to handling of real-life infectious disease outbreaks
  • Contribute to setting up infectious disease preparedness systems