Mathematical models are increasingly used to understand the transmission of infectious diseases in populations and to evaluate the potential impact of control programmes in reducing morbidity and mortality. With this short course we aim to bridge the gap between theoretical training in infectious disease modelling, and the specialist technical skills needed for research in this area.
Participants will gain a working knowledge of using R to code dynamic transmission models. They will learn key principles for best practice in model coding including version control. They will learn how to code stochastic and deterministic epidemic models from scratch. They will also learn how to present model output by implementing sensitivity analysis and graphing data, and best practices for writing coherent code and using version control.
Introduction to R (optional morning session)
Using loops, functions, packages and sourcing in R
Best practices in coding
Discrete-time deterministic models
Ordinary differential equation models, including using deSolve for integration
Simulation, sensitivity and sampling parameter sets, including Latin Hypercube sampling
Processing outputs using ggplot2: making graphs and stratifying outputs
Network models: reading adjacency matrices and simulation of Reed-Frost models
Stochastic models in discrete time
Stochastic models in continuous time
Version control: a hands-on introduction to Git and Github
The course is taught as a series of hands-on computer practicals in R. A 2-hour Introductory session is available for those with no prior experience with R. We will provide some exercises before the course to help participants decide if they need to attend the introductory session.
Who is this course for?
This course is aimed at people who have had some exposure to the theory and use of infectious disease modelling and who would like to start coding their own models using R. Individuals who know some R but do not have experience using R to code infectious disease models will benefit. The course is ideal for those who will be conducting research using infectious disease models in R or who want a deeper understanding of techniques for implementing models.