Mathematical Modelling in Infectious Disease Epidemiology

Course 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.
Education

MSc Programme in Statistics

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.

2-3 hours of lectures for 7 weeks
2-3 hours of exercises for 7 weeks

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

SAND, STAT A, Statistiske Metoder

Oral

Feedback by active participation in exercises and lectures

ECTS
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 assessment

The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 21
  • Preparation
  • 131
  • Exercises
  • 21
  • Exam Preparation
  • 21
  • Exam
  • 12
  • English
  • 206

Kursusinformation

Language
English
Course number
NMAK24012U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 4
Schedulegroup
A
Capacity
No limitation.
Unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Studyboard
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinators
  • Carsten Wiuf   (4-7a6c7869437064776b316e7831676e)
  • Jacob Liam Curran-Sebastian   (12-6d64667265316678757564714376787167316e7831676e)
Saved on the 14-02-2024

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