Mathematical epidemiology: Stochastic models and their statistical calibration
Listed as Applied Mathematics Topic A in the Course Planner.
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Description
Mathematical models are increasingly used to inform governmental policymakers on issues that
threaten human health or which have an adverse impact on the economy. It is this realworld 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.
Objective
Content
Topics:
An introduction to mathematical epidemiology
Discretetime and continuoustime discretestate 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
 
Year  Semester  Level  Units 

2013  1  4  3 
Graduate attributes
Linkage future
This course is not recorded as prequisite for other courses.
Recommended text
None.
