Course dates: 4 - 7 July 2023
The course will take place in London, UK.
A short course taught by members of the .
There is a growing demand for mathematical modellers in public health to explain observed disease trends and predict the outcome of interventions, often by synthesising information from different data sources. At the same time, increasing computational power and methodological advances are providing exciting opportunities to fit ever more complex mechanistic models to data. In light of the speed of methodological advances and the broad nature of the field, the task of choosing from the available methods and packages, as well as putting them into practice, can be daunting.
With this short course we aim to bridge the gap between state-of-the-art statistical inference methods and training in infectious disease modelling. We will introduce key terminology from frequentist and Bayesian approaches as well as methods ranging from Markov-Chain Monte Carlo (MCMC) to particle filters and Approximate Bayesian Computation (ABC).
Our aim is to equip students and researchers using infectious disease models with the theoretical background needed to better understand the literature, as well as with practical knowledge of the tools available to put these methods into practice.
Teaching methods and course materials
The course consists of computer practical sessions interspersed with introductory lectures. The practical sessions will use R, which is freely available at . Participants are encouraged to bring their own laptop, but where this is not possible a laptop will be made available.
The course lasts four days. On the first day we will introduce the conceptual underpinning of inference using dynamic models and apply this to model fitting using deterministic models in R. On the second day, we will extend the concepts and methods discussed on day one to stochastic models and implement methods for matching these to data in R. On the third day, we will discuss the landscape of available packages (not limited to R) for using the methods introduced on day one and two and provide an overview of further methods available as well as a summary and outlook.
Target audience
This course is aimed at students and researchers who are working with dynamic models of infectious disease (i.e., broadly based on the SIR model) and would like to learn about state-of-the art methods of matching these to data. The course is limited to 24 participants.
Prerequisites
In order to benefit from the course, basic knowledge of infectious disease modelling is required. Participants are expected to have a good understanding of key concepts outlined in Introduction to Infectious Disease Modelling and Its Applications. Moreover, some experience in using R is essential. Those who have never used R but have experience with other mathematical packages (e.g., Matlab, Mathematica) and/or programming languages should also be able to benefit from the course, but would improve their experience by familiarising themselves with the basics of R syntax ahead of the course.
Applying for this course
Applications are now closed. You can register your interest and we will let you know when applications reopen.
Please read LSHTM's Admissions policies prior to submitting your application.
Visas
The student is responsible for obtaining any visa or other permissions to attend the course, and is encouraged to start the application process as early as possible as obtaining a visa for the UK can sometimes take a long time. The Short Courses team can provide supporting documentation if requested.
Accommodation
A list of hotels located in the vicinity of LSHTM, along with further resources for short term accommodation, can be found on our accommodation pages.
Important information
Please note:
- Students will be required to bring their own laptops. The Stata package will be available for the duration of the course.
- If you have been offered a place on the course you will not be able to register without bringing a formal ID (Passport) and without having obtained the correct visa if required.
- It is essential that you read the current visa requirements for short course students.
- LSHTM may cancel courses two weeks before the first day of the course if numbers prove insufficient. In those circumstances, course fees will be refunded.
- LSHTM cannot accept responsibility for accommodation, travel and other losses incurred as a result of the course being cancelled.