We have all received a vaccine. Not by chance. Someone decided when, who, and why should get it. In this course, we will explore how statistics and epidemiology shape decisions about our health.

Together, we will look at mathematical modelling as a way of thinking about a world full of uncertainty. We will examine how models are created, what they can (and cannot) do, and how they are used in public health decision-making and risk communication. We will see why all models are inherently wrong, yet still extremely useful when it comes to public health decisions. You will learn how to read epidemiological graphs, understand vaccination and health policy decisions, and critically evaluate claims made by politicians and experts.

The course will cover the following topics:
Mathematical modelling of infectious diseases: What goes into a mathematical model, and what role do models play in society? How is it possible to describe the world using simple equations or a few lines of code?
Basic epidemiological concepts and metrics: Prevalence, incidence, risk, and herd immunity. Why do these concepts matter? Why do epidemic curves often have a similar shape?
Mosquito-borne infectious diseases: Why is malaria still a global problem? What is missing for its elimination?
Vaccination in theory and practice: How were measles eliminated in many countries? Is mandatory vaccination justified?
Genetic epidemiology: Is epidemiology only about infectious diseases? Can mathematical modelling be used to predict the risk of all diseases?
Simulation game: You will try crisis decision-making (e.g. during COVID-19) while taking on the roles of the WHO, politicians, mathematicians, and pharmaceutical companies.

We will work with simple mathematical ideas, but the focus of the course is on understanding concepts rather than calculations. The technical level will be adapted to the group, making the course accessible to everyone.