Mathematical epidemiology: Stochastic models and their statistical calibration
Listed as Applied Mathematics Topic A in the Course Planner.
Go to this course in the University Course Planner.
Mathematical models are increasingly used to inform governmental policy-makers on issues that
threaten human health or which have an adverse impact on the economy. It is this real-world success
combined with the wide variety of interesting mathematical problems which arise that makes
mathematical epidemiology one of the most exciting topics in applied mathematics. During the
summer school, you will be introduced to mathematical epidemiology and some fundamental theory
required for studying and parametrising stochastic models of infection dynamics, which will provide an
ideal basis for addressing key research questions in this area; several such questions will be
introduced and explored in this course.
An introduction to mathematical epidemiology
Discrete-time and continuous-time discrete-state stochastic infection models
Numerical methods for studying stochastic infection models: EXPOKIT, transforms and their inversion
Methods for simulating stochastic infection models: classical (Gillespie) algorithm, more efficient exact
and approximate algorithms
Methods for parameterising stochastic infection models: frequentist approaches, Bayesian
approaches, approximate Bayesian computation
Optimal observation of stochastic infection models
This course is not recorded as prequisite for other courses.