| The course runs from 7 - 10 September 2026. |
The course will take place in London, UK.
A short course taught by members of the Centre for the Mathematical Modelling of Infectious Diseases.
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 make it possible 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 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 apply these methods in their own research.
The two short courses Modern Techniques in Infectious Disease Modelling and Model Fitting and Inference for Infectious Disease Dynamics are offered back-to-back across two consecutive weeks. While either course can be taken on its own, students who take both courses will benefit from a comprehensive introduction to modern methods in infectious disease transmission modelling.
Teaching methods and course materials
The course consists of computer practical sessions interspersed with introductory lectures. The practical sessions will use Julia (with the Turing.jl probabilistic programming framework), a programming language designed for scientific computing. No prior Julia experience is required, but some pre-reading of Julia basics and concepts is encouraged for those who are able. The course materials include guidance for those coming from R throughout. Participants should bring their own laptop if at all possible.
The course lasts three and a half days:
- Day 1: we will introduce the conceptual underpinning of inference using dynamic models and apply this to model fitting using deterministic models.
- Day 2: we will cover MCMC diagnostics, model checking and validation, and introduce stochastic models.
- Day 3: we will implement particle-based methods for matching stochastic models to data.
- Day 4 (half day): we will cover observation models and Approximate Bayesian Computation.
Additional materials on variational inference and universal differential equations are available for self-study.
Who is the course for?
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 modern 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 or Modern Techniques in Infectious Disease Modelling.
Some experience with a programming language (R, Python, MATLAB, or similar) is expected. No prior knowledge of Julia is required. The course materials provide all the guidance needed to get started.
Testimonial
“The Model Fitting and Inference for Infectious Disease Dynamics course was the perfect follow on from my basic prior understanding of modelling infectious disease dynamics. The course materials were clear and well-constructed, and the coordinators and session leads were helpful and clear. I would highly recommend this short course for anyone looking to get a hands on understanding of modelling infectious disease dynamics." - Em Prestige, 2023
Course fees for 2026 entry
- £1,260 full fee
Applying for this course
Applications for 2026 are now open and can be made via our online application form.
The application deadline will be 23:59 (UK time) on Friday 7th August 2026. We strongly advise that you apply early as courses may close earlier than the stated deadline if they become full.
Please read LSHTM's Admissions policies prior to submitting your application.
Important information
Please note:
- Students will be required to bring their own laptops.
- 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.
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.
- 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.