Epidemiology

The epidemiological toolbox and your research

This is a course designed to help you ask the right questions. Sometimes these questions may be directed to an epidemiologist, sometimes to a data analyst, and sometimes to yourself. This is not a course which will transform you into an epidemiologist or a data analyst - your expertise is a crucial part which makes epidemiology tick.

Good research stems from knowing that you will have to make bad choices. But knowing which ones are least bad, and which ones get you closer to your goal, is key in moving your research field forward. You should appreciate the complexities, understand what can be done to alleviate difficulties, and always report your findings in light of your decisions.

Finally, while it is human to dichotomization into "right" and "wrong", "significant" and "non-significant", "causal" and "associational", remember that such distinctions are only useful in abstract terms outside of a real-world context. When we begin studying our surroundings, and put our findings into context, dichotomization often fails to generalise.


Feedback and changes from 2025 course:

  • 9 students completed the 2025 course
  • 6 completed the course evaluation giving the course an average score of 4.8 (of 5).
  • Small changes in instructions have been done over the years but no major changes.

Syllabus and Schedule (8FO0113)

Syllabus (SE) - Syllabus (EN)

Learning outcomes (from the Syllabus)

  1. Account for relevant methods that can be used to measure health in a population.
  2. Account for the identification of factors that influence health status on a population basis.
  3. Plan and critically appraise the conduct of epidemiological studies.
  4. Judge how epidemiological methods may be used under different circumstances.
Day 1 (Monday 13 April)
Go through material: Introduction, Prevalence, Incidence and Association (Learning outcomes: 1)
Meet up on Zoom (11:15 to 12:00 - Zoom) (Mandatory meet-and-greet)
Work on the task Descriptive epidemiology (Learning outcomes: 1, 3 and 4)
Day 2 (Tuesday 14 April)
Work on the task Descriptive epidemiology (Learning outcomes: 1, 3 and 4)
Send task to course leader no later than 12:15
Present task Descriptive epidemiology (13:15 to 17:00 - Rönnen) (Learning outcomes: 1, 3 and 4)
Day 3 (Wednesday 15 April)
Go through material: Prediction (Learning outcomes: 1)
Meet up on Zoom (11:15 to 12:00 - Zoom) (Bring your own questions)
Work on the task Predictive epidemiology (Learning outcomes: 1, 3 and 4)
Day 4 (Thursday 16 April)
Work on the task Predictive epidemiology (Learning outcomes: 1, 3 and 4)
Send task to course leader no later than 12:15
Present task Predictive epidemiology (13:15 to 17:00 - KarlJohan) (Learning outcomes: 1, 3 and 4)
Day 5 (Monday 20 April)
Prepare task Causal inference before (and after) lecture (No hand-in)
Lecture Causality (09:15 to 12:00 - Rönnen) (Learning outcomes: 2 and 4)
Day 6 (Tuesday 21 April)
Go through material: Study design, RCT and Observational (Learning outcomes: 2 and 4)
Day 7 (Wednesday 22 April)
Go through material: Study design, RCT and Observational (Learning outcomes: 2 and 4)
Meet up on Zoom (11:15 to 12:00 - Zoom) (Bring your own questions)
Day 8 (Thursday 23 April)
Worked example and extra materials (09:15 to 12:00 - Björken) (Learning outcomes: 1, 2, 3 and 4)
24 April to 29 April
Work on task Study design (Learning outcomes: 1, 2, 3 and 4)
Send task to course leader no later than 29 April (midnight)
Day 9 (Monday 4 May or Tuesday 5 May)
Present task Study design (09:15 to 15:00 - Rönnen) (Learning outcomes: 1, 2, 3 and 4)