NMAK24012U Mathematical Modelling in Infectious Disease Epidemiology
Volume 2024/2025
Education
MSc Programme in Statistics
Content
- Introduction to epidemic modelling, compartmental models.
- Models of endemic diseases.
- Models with host heterogeneity.
- Stochastic models of epidemics
- Fitting models to data and estimation of epidemiological parameters
- Modelling interventions and vaccination.
Learning Outcome
Knowledge:
- Compartmental, ODE models of disease dynamics, understanding of the asymptotic behaviour of different models.
- Derivation of basic properties of models.
- Stochastic models, including Reed-Frost models, Branching Processes and the stochastic SIR model.
- Maximum likelihood and Bayesian approaches to inferring epidemiological parameters.
Skills:
- Explain insights obtained from different infectious disease models
- Solve systems of ODEs and simulate stochastic models of epidemics in R
- Perform model fitting for completed and ongoing epidemics.
Competences:
- Design compartmental models to model different epidemic scenarios and interventions and be able to study the properties of these models.
- Fit different models to real data from infectious disease outbreaks and evaluate the outputs.
- Simulate disease outbreaks from stochastic models using different simulation methods.
Literature
Suggested Reading:
Extracts from some of these books will likely be used
- Held, Leonhard, et al., eds.Handbook of infectious disease data analysis. CRC Press, 2019.
- M.J. Keeling and P. Rohani,Modelling Infectious Diseases in Humans and Animals, Princeton University Press, 2007 (ISBN 0691116172)
- R.M. Anderson and R.M. May,Infectious Diseases of Humans, Oxford University Press, 1992. (ISBN 019854040X)
- Andersson, Hakan, and Tom Britton.Stochastic epidemic models and their statistical analysis. Vol. 151. Springer Science & Business Media, 2012.
See Absalon for a list of course literature
Recommended Academic Qualifications
SAND, STAT A, Statistiske
Metoder
Teaching and learning methods
2-3 hours of lectures for 7
weeks
2-3 hours of exercises for 7 weeks
2-3 hours of exercises for 7 weeks
Workload
- Category
- Hours
- Lectures
- 21
- Preparation
- 131
- Exercises
- 21
- Exam Preparation
- 21
- Exam
- 12
- Total
- 206
Feedback form
Oral
Feedback by active participation in exercises and lectures
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Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Written assignment, 27 hours
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner
- Re-exam
Oral exam, 30 minutes without preparation time. No aids allowed
Criteria for exam assesment
The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.
Course information
- Language
- English
- Course code
- NMAK24012U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 4
- Schedule
- A
- Course capacity
- No limitation.
Unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinators
- Carsten Wiuf (4-7b6d796a447165786c326f7932686f)
- Jacob Liam Curran-Sebastian (12-766d6f7b6e3a6f817e7e6d7a4c7f817a703a77813a7077)
Saved on the
14-02-2024