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The Mathematics and Statistics of Infectious Disease Outbreaks

The course gives an introduction to the mathematical and statistical modelling of infectious diseases.

Information for admitted students Spring 2022

Congratulations! You have been admitted at Stockholm University and we hope that you will enjoy your studies with us.

In order to ensure that your studies begin as smoothly as possible we have compiled a short checklist for the beginning of the semester.

Follow the instructions on wether you have to reply to your offer or not.


Checklist for admitted students

  1. Activate your university account

    The first step in being able to register and gain access to all the university's IT services.

  2. Register at your department

    Registration can be done in different ways. Read the instructions from your department below.

  3. Read all the information on this page

    Here you will find what you need to know before your course or programme starts.


Your seat may be withdrawn if you do not register according to the instructions provided by your department.

Information from the department - courses

Here you can find information about online registration times and our learning platform. If you are conditionally admitted or placed on a reserve list, you can read more about that here.

First: Reply to your offer!

If you are offered a place or reserve place in the first notification of selection results, you must reply to it via by 17 December to keep your place!

Info at about replying to your offer

If you forget to do this, you must apply for the course again (assuming it opens for late application) if you want to take it.

Registration in Ladok

Once you are admitted to a course you must register online via to keep your place. Online registration opens 3 January, and closes at different times for different courses.

For the following courses the last day for online registration is extra early:

  • Mathematics I (only given in Swedish): last day for registration is 10 January.
  • Mathematics for the Natural Sciences II (only given in Swedish): last day for registration is 16 January.
  • Courses in computer science or scientific computing given at KTH, so courses with course codes BE7008, BE7012, DA3019, DA7054-DA7064: last day for registration is 16 January.

For other courses, the last day for online registration is 6 February if the course begins in the first half of the semester and 12 April if it begins in the second half.

Note that you cannot register online for degree project courses in mathematics or computer science, we will register you for these when your project plan has been approved.

Learning platform

Our course pages can be found at

On most of the course pages you can "self enrol", but doing this does not mean that you are registered for the course! You always have to register in Ladok separately, see above about registration.

Conditionally admitted

It is quite common that a student at the time of application has not finished all courses that are given as special eligibility requirements for the course or programme the student has applied for. This will result in the admission status "Conditionally admitted".

The administration of conditions will be carried out just before course start. As long as the condition remains you cannot complete online registration to the courses you will take. If the condition is still in place by course start, and you haven't received any notification about whether or not you'll be allowed to take the course or programme you have applied for, contact our student advisors. If the course is one where online registration closes early, contact us before it closes.

You can also be conditionally admitted because you need to pay a tuition fee. You can find more information on tuition fees here:

Payment and repayment of tuition fee

Placed on reserve list

If you have a reserve place for a course, we may be able to offer you a spot in which case we will contact you around the start of the semester. If you have a conditional reserve place and are offered a place on the course, you also need to fulfil the condition(s) before you can take your place on the course.

More information

New student: information about admission, registration, course literature and course web

During your studies: information about exams, our code of honour, student representation and IT resources

Contact for questions about studies

Welcome activities

Stockholm University organises a series of welcome activities that stretch over a few weeks at the beginning of each semester. The programme is voluntary (attendance is optional) and includes Arrival Service at the airport and an Orientation Day, see more details about these events below.
Your department may also organise activities for welcoming international students. More information will be provided by your specific department. 

Find your way on campus

Stockholm University's main campus is in the Frescati area, north of the city centre. While most of our departments and offices are located here, there are also campus areas in other parts of the city.

Find your way on campus

Read more

New student

During your studies

Student unions

For new international students

Pre-departure information

New in Sweden

The aim of the course is to gain understanding of models for the spread of infectious diseases and to draw conclusions from observations of an ongoing outbreak. The contents of the course includes basic models for the spread of infectious diseases and their basic properties, simulation of more complicated epidemic models and finally statistical methods for estimating parameters and predicting the outcome of an epidemic.

The study goals include:

  • Epidemic Models: The SIR model
  • Data situation in outbreaks
  • Estimation of epidemic growth rate and the effective reproduction number
  • Handling latencies and delays
  • Vaccination
  • Extending the SIR model: Networks, Age-Groups and other heterogeneities
  • Outbreak detection
  • COVID-19
  • Course structure

    The course consists of two elements; theory and project.

    Teaching format

    This is an online course. Instruction is given in the form of web-based lectures, and supervision of individual project work.

    Videos of the pre-recorded lectures will be made available through the moodle platform. In order to reach a larger audience, the videos are also available from the SU video sharing platform and YouTube.

    In the lectures we will use the statistical programming environment R to illustrate models and their inference through programming code. You are free in your choice of programming environment to use for your project work, though we strongly recommend using R. For tips on resources to learn programming with R, see "More information" below.


    The course is assessed in the following manner:

    • Written home exam for the theory part.
    • Hand-in assignments for the project work.


    Examiners on this course during the spring 2022 are Tom Britton and Michael Höhle.

    A list of all examiners can be found on

    Exam information

  • Schedule

    The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.

    Since this is a virtual course there is no direct schedule. The live sessions will take place twice a week, with recordings as well as live sessions. Attendance in live sessions is not mandatory.

  • Course literature

    Note that the course literature can be changed up to two months before the start of the course.

    Throughout the course we will use individual resources, because there is no good book covering both the mathematical modelling and the statistical inference part on a basic level. Good sources to get an overall overview of mathematical modelling in infectious diseases, but not course literature per-se (i.e. it's voluntary to read). We shall also link to particular papers for individual lectures.

    Becker, N, Modeling to Inform Infectious Disease Control (2015)

    Diekmann O, Heesterbeek H, Britton T, Mathematical Tools for Understanding Infectious Disease Dynamics (2013)

    Keeling MJ, Rohani P, Modeling Infectious Diseases (2008)

    See also "More information" below for tips on resources to learn programming with R.

  • Course reports

  • More information

    New student
    During your studies

    Course web

    You can find our course webpages on


    In the course we will illustrate models and their inference through programming code. In the lectures will use the statistical programming environment R for this. R is best used together with an IDE such as, e.g., RStudio. However, even though we strongly recommend to use R, you are free in your choice of programming environment to use for your project work. A good resource to learn programming with R is The Art of R Programming book by Matloff (relevant for our course).

    An excellent resource for a data based view on analysis is R for Data Science book by Wickham and Grolemund (used in the Statistical Data Processing Class, but less relevant for our course) or some of the RStudio Tutorials on R programming.

  • Contact