Infectious Disease Epidemiology
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
MSc in Public Health Science - elective course
MSc in Health Informatics - elective course
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
Lectures with demonstration and co-programming together. With some group tasks
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'.
The course is open for MSc students with pre-approval in Health Informatics, Global Health, Human Biology and Health Science.
Oral feedback will be given during the course. After ended exam, students can get feedback on the result by contacting the course responsible.
Open for credit transfer students. Apply here:
- ECTS
- 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
- Re-exam
Criteria for exam assessment
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
- Category
- Hours
- Class Instruction
- 40
- Preparation
- 235
- Exam
- 10
- English
- 285
Kursusinformation
- Language
- English
- Course number
- SFOK09103U
- ECTS
- 10 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 semester
- Placement
- Autumn
- Schedulegroup
-
See the Schedule in syllabus.
- Capacity
- 35 students
- Studyboard
- Study Board for Public Health Science, Global Health and Health Informatics
Contracting department
- Department of Public Health
Contracting faculty
- Faculty of Health and Medical Sciences
Course Coordinator
- Samir Bhatt (11-7664706c7531656b6477774376787167316e7831676e)
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