Summary: The COVID-19 crisis has highlighted the value of mathematical models in the fight against infectious diseases, with many governments regularly reporting on the predictions of their modelling teams. Mathematical epidemiology, the discipline codifying these approaches, has a long history. Daniel Bernoulli formulated a model for smallpox inoculation in 1760. However, the real emergence came with Ross, Kermack and McKendrick in the early twentieth century. Since then, mathematical epidemiology has blossomed into a very active field of research, involving not only theoretical work on models but also, as evidenced by COVID-19, in actual policy making.
In this course, we will take a brief tour of mathematical epidemiology. We will start with the basic theory, building on the so-called Kermarck-McKendrick model that provides the framework for most more advanced models. We will then consider two natural expansions of basic models: multigroup models, which consider the role of heterogeneity of individuals within a population, and metapopulation models, which consider the role of the spatial nature of population distribution. Then, we will investigate the (important) role of stochasticity in models through two different approaches, one amenable to mathematical analysis and the other focused on simulations. Topics will be motivated by real-world epidemic data and tutorials will cover model formulation, analysis and simulation.
Registration deadline:1 February 2022
Invited lecturer: Prof Julien Arino (University of Manitoba, Canada)
Contact: With any questions or comments, please contact one of the organizers