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Mathematical Modelling in Epidemiology
Provider: Faculty of Science

Activity no.: 5567-21-07-31 
Enrollment deadline: 26/04/2021
PlaceDepartment of Mathematical Sciences
Universitetsparken 5, 2100 København Ø
Date and time26.04.2021, at: 08:00 - 27.06.2021, at: 16:00
Regular seats50
ECTS credits7.50
Contact personNina Weisse    E-mail address: weisse@math.ku.dk
Enrolment Handling/Course OrganiserRobyn Margaret Stuart    E-mail address: robyn@math.ku.dk
Written languageEnglish
Teaching languageEnglish
Semester/BlockBlock 4
Scheme groupA (Tues 8-12 + Thurs 8-17)
Exam formWritten examination, 4 hours under invigilation
Exam formWritten examination
Exam detailsSupervised written exam of 4hrs, including the use of computers for modelling All aids allowed
Criteria for exam assessmentComplete at least one of two within-course assignments.
Course workload
Course workload categoryHours
Lectures35.00
Preparation107.00
Practical exercises35.00
Project work25.00
Exam4.00
Sum206.00
Content
Epidemiology may be thought of as the study of patterns of health and disease in populations. Mathematical modelling is one of the cornerstones of modern epidemiology, and in this course we will cover various analytic techniques used to interrogate and understand epidemics. Particular topics may include: deterministic and stochastic modelling; simulation and analysis; transmission dynamics; agent-based models; compartmental models; network models; vector-based models.

Learning outcome
KNOWLEDGE
Knowledge of modelling, simulation and analysis techniques used in epidemiology

SKILLS
Ability to create appropriate models to analyse epidemic behaviour
Ability to undertake critical analysis and validation of models
Ability to interpret the output of models
Ability to undertake self-directed research for mathematical modelling approaches

COMPETENCES
Create, parameterise, fit, evaluate, and interpret the results of models for understanding population disease dynamics

Literature
See Absalon for a list of course literature

Teaching and learning methods
Lectures and exercise classes

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